Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:( High Dimension). Showing records 1 – 30 of 97 total matches.

[1] [2] [3] [4]

Search Limiters

Last 2 Years | English Only

Degrees

Languages

Country

▼ Search Limiters


Texas A&M University

1. Gregory, Karl Bruce. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.

Degree: 2014, Texas A&M University

 This document contains three sections. The first two present new methods for two-sample testing where there are many variables of interest and the third presents… (more)

Subjects/Keywords: high dimension; bootstrap; two-sample

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gregory, K. B. (2014). Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153324

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gregory, Karl Bruce. “Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.” 2014. Thesis, Texas A&M University. Accessed October 21, 2019. http://hdl.handle.net/1969.1/153324.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gregory, Karl Bruce. “Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series.” 2014. Web. 21 Oct 2019.

Vancouver:

Gregory KB. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/1969.1/153324.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gregory KB. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153324

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Arizona

2. Zeng, Yue. Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data .

Degree: 2017, University of Arizona

 Variable screening techniques are fast and crude techniques to scan high-dimensional data and conduct dimension reduction before a refined variable selection method is applied. Its… (more)

Subjects/Keywords: Classification; High Dimension; Variable Screening

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zeng, Y. (2017). Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/624579

Chicago Manual of Style (16th Edition):

Zeng, Yue. “Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data .” 2017. Doctoral Dissertation, University of Arizona. Accessed October 21, 2019. http://hdl.handle.net/10150/624579.

MLA Handbook (7th Edition):

Zeng, Yue. “Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data .” 2017. Web. 21 Oct 2019.

Vancouver:

Zeng Y. Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data . [Internet] [Doctoral dissertation]. University of Arizona; 2017. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10150/624579.

Council of Science Editors:

Zeng Y. Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data . [Doctoral Dissertation]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/624579

3. Bonnet, Anna. Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications.

Degree: Docteur es, Mathématiques appliquées, 2016, Paris Saclay

Nous nous intéressons à desméthodes statistiques pour estimer l'héritabilitéd'un caractère biologique, qui correspond à lapart des variations de ce caractère qui peut êtreattribuée à des… (more)

Subjects/Keywords: Héritabilité; Modèles mixtes; Grande dimension; Sélection de variables; Heritability; Mixed models; High dimension; Variable selection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bonnet, A. (2016). Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLS498

Chicago Manual of Style (16th Edition):

Bonnet, Anna. “Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications.” 2016. Doctoral Dissertation, Paris Saclay. Accessed October 21, 2019. http://www.theses.fr/2016SACLS498.

MLA Handbook (7th Edition):

Bonnet, Anna. “Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications.” 2016. Web. 21 Oct 2019.

Vancouver:

Bonnet A. Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2016SACLS498.

Council of Science Editors:

Bonnet A. Heritability Estimation in High-dimensional Mixed Models : Theory and Applications. : Estimation de l'héritabilité dans les modèles mixtes en grande dimension : théorie et applications. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLS498


University of California – Berkeley

4. Xu, Ying. Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices.

Degree: Statistics, 2011, University of California – Berkeley

 In multivariate analysis, canonical correlation analysis is a method that enable us to gain insight into the relationships between the two sets of variables. Itdetermines… (more)

Subjects/Keywords: Statistics; canonical correlation analysis; high dimension; rank correlation; regularization; Renyi correlation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xu, Y. (2011). Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/7zr9p85r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xu, Ying. “Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices.” 2011. Thesis, University of California – Berkeley. Accessed October 21, 2019. http://www.escholarship.org/uc/item/7zr9p85r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xu, Ying. “Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices.” 2011. Web. 21 Oct 2019.

Vancouver:

Xu Y. Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices. [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2019 Oct 21]. Available from: http://www.escholarship.org/uc/item/7zr9p85r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu Y. Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices. [Thesis]. University of California – Berkeley; 2011. Available from: http://www.escholarship.org/uc/item/7zr9p85r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – Santa Cruz

5. Albrecht, Georg Hans. Interactive High Dimensional Data Analysis using the Three Experts.

Degree: Computer Science, 2015, University of California – Santa Cruz

 With the increasing availability of different kinds of data from various domains such as health care, finance, social networks, etc. there is a need to… (more)

Subjects/Keywords: Computer science; dimension reduction; high dimesion; interactive; interface; three experts; visualization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Albrecht, G. H. (2015). Interactive High Dimensional Data Analysis using the Three Experts. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/58h8g8h2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Thesis, University of California – Santa Cruz. Accessed October 21, 2019. http://www.escholarship.org/uc/item/58h8g8h2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Albrecht, Georg Hans. “Interactive High Dimensional Data Analysis using the Three Experts.” 2015. Web. 21 Oct 2019.

Vancouver:

Albrecht GH. Interactive High Dimensional Data Analysis using the Three Experts. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2019 Oct 21]. Available from: http://www.escholarship.org/uc/item/58h8g8h2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Albrecht GH. Interactive High Dimensional Data Analysis using the Three Experts. [Thesis]. University of California – Santa Cruz; 2015. Available from: http://www.escholarship.org/uc/item/58h8g8h2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – Berkeley

6. Bean, Derek. Non-Gaussian Component Analysis.

Degree: Statistics, 2014, University of California – Berkeley

 Extracting relevant low-dimensional information from high-dimensional data is a common pre-processing task with an extensive history in Statistics. Dimensionality reduction can facilitate data visualization and… (more)

Subjects/Keywords: Statistics; Characteristic Function; High-dimension; Non-Gaussian Component Analysis; Projection Pursuit

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bean, D. (2014). Non-Gaussian Component Analysis. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/2s32627s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bean, Derek. “Non-Gaussian Component Analysis.” 2014. Thesis, University of California – Berkeley. Accessed October 21, 2019. http://www.escholarship.org/uc/item/2s32627s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bean, Derek. “Non-Gaussian Component Analysis.” 2014. Web. 21 Oct 2019.

Vancouver:

Bean D. Non-Gaussian Component Analysis. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2019 Oct 21]. Available from: http://www.escholarship.org/uc/item/2s32627s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bean D. Non-Gaussian Component Analysis. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/2s32627s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

7. Wang, Ye. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .

Degree: 2018, Duke University

  Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the… (more)

Subjects/Keywords: Statistics; dimension reduction; high dimensional; manifold learning; MCMC; scalable inference

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Y. (2018). Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16853

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Thesis, Duke University. Accessed October 21, 2019. http://hdl.handle.net/10161/16853.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Web. 21 Oct 2019.

Vancouver:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10161/16853.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16853

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Iowa State University

8. Qin, Yingli. Statistical inference for high-dimensional data.

Degree: 2009, Iowa State University

High-dimensional data, where the number of variables p is large compared to the sample size n, are widely available from microarray studies, finance and many… (more)

Subjects/Keywords: high-dimension; test for means; two-sample; Statistics and Probability

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Qin, Y. (2009). Statistical inference for high-dimensional data. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/10984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Qin, Yingli. “Statistical inference for high-dimensional data.” 2009. Thesis, Iowa State University. Accessed October 21, 2019. https://lib.dr.iastate.edu/etd/10984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Qin, Yingli. “Statistical inference for high-dimensional data.” 2009. Web. 21 Oct 2019.

Vancouver:

Qin Y. Statistical inference for high-dimensional data. [Internet] [Thesis]. Iowa State University; 2009. [cited 2019 Oct 21]. Available from: https://lib.dr.iastate.edu/etd/10984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qin Y. Statistical inference for high-dimensional data. [Thesis]. Iowa State University; 2009. Available from: https://lib.dr.iastate.edu/etd/10984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

9. Qiao, Mu. Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means.

Degree: 2017, Penn State University

 We develop a new method for high dimensional data visualization via the Gaussian mixture model (GMM) with the component means constrained in a pre-selected subspace.… (more)

Subjects/Keywords: visualization; high dimensionality; dimension reduction; subspace; Gaussian mixture model

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Qiao, M. (2017). Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14721muq103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Qiao, Mu. “Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means.” 2017. Thesis, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/14721muq103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Qiao, Mu. “Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means.” 2017. Web. 21 Oct 2019.

Vancouver:

Qiao M. Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means. [Internet] [Thesis]. Penn State University; 2017. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/14721muq103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qiao M. Visual Analytics Through Gaussian Mixture Models with Subspace Constrained Component Means. [Thesis]. Penn State University; 2017. Available from: https://etda.libraries.psu.edu/catalog/14721muq103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

10. Tang, Yang. Model-based clustering of high-dimensional binary data .

Degree: 2013, University of Guelph

 We present a mixture of latent trait models with common slope parameters (MCLT) for high dimensional binary data, a data type for which few established… (more)

Subjects/Keywords: binary data; clustering; high dimension; latent variables; mixture models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tang, Y. (2013). Model-based clustering of high-dimensional binary data . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tang, Yang. “Model-based clustering of high-dimensional binary data .” 2013. Thesis, University of Guelph. Accessed October 21, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tang, Yang. “Model-based clustering of high-dimensional binary data .” 2013. Web. 21 Oct 2019.

Vancouver:

Tang Y. Model-based clustering of high-dimensional binary data . [Internet] [Thesis]. University of Guelph; 2013. [cited 2019 Oct 21]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tang Y. Model-based clustering of high-dimensional binary data . [Thesis]. University of Guelph; 2013. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Arizona

11. Wauters, John. Independence Screening in High-Dimensional Data .

Degree: 2016, University of Arizona

High-dimensional data, data in which the number of dimensions exceeds the number of observations, is increasingly common in statistics. The term "ultra-high dimensional" is defined… (more)

Subjects/Keywords: feature screening; high-dimensional data; independence screening; modeling; dimension reduction

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wauters, J. (2016). Independence Screening in High-Dimensional Data . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/623083

Chicago Manual of Style (16th Edition):

Wauters, John. “Independence Screening in High-Dimensional Data .” 2016. Masters Thesis, University of Arizona. Accessed October 21, 2019. http://hdl.handle.net/10150/623083.

MLA Handbook (7th Edition):

Wauters, John. “Independence Screening in High-Dimensional Data .” 2016. Web. 21 Oct 2019.

Vancouver:

Wauters J. Independence Screening in High-Dimensional Data . [Internet] [Masters thesis]. University of Arizona; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10150/623083.

Council of Science Editors:

Wauters J. Independence Screening in High-Dimensional Data . [Masters Thesis]. University of Arizona; 2016. Available from: http://hdl.handle.net/10150/623083

12. Shi, Xiaofeng. Large Portfolios' Risks and High-Dimensional Factor Models .

Degree: PhD, 2014, Princeton University

 This dissertation explores two important topics on high-dimensional factor models. We first consider the problem of estimating and assessing the risk of a large portfolio.… (more)

Subjects/Keywords: Factor Model; High Dimension; Penalized Estimation; Risk Management

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shi, X. (2014). Large Portfolios' Risks and High-Dimensional Factor Models . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp013x816p84p

Chicago Manual of Style (16th Edition):

Shi, Xiaofeng. “Large Portfolios' Risks and High-Dimensional Factor Models .” 2014. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp013x816p84p.

MLA Handbook (7th Edition):

Shi, Xiaofeng. “Large Portfolios' Risks and High-Dimensional Factor Models .” 2014. Web. 21 Oct 2019.

Vancouver:

Shi X. Large Portfolios' Risks and High-Dimensional Factor Models . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp013x816p84p.

Council of Science Editors:

Shi X. Large Portfolios' Risks and High-Dimensional Factor Models . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp013x816p84p


Princeton University

13. Han, Jiequn. Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations .

Degree: PhD, 2018, Princeton University

 Curse of dimensionality has been a notorious difficulty in scientific computing. Recent advances in machine learning, especially in deep learning, have ushered some new hope… (more)

Subjects/Keywords: deep learning; high dimension; molecular dynamics; partial differential equations

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Han, J. (2018). Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01v405sd07c

Chicago Manual of Style (16th Edition):

Han, Jiequn. “Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations .” 2018. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01v405sd07c.

MLA Handbook (7th Edition):

Han, Jiequn. “Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations .” 2018. Web. 21 Oct 2019.

Vancouver:

Han J. Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01v405sd07c.

Council of Science Editors:

Han J. Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01v405sd07c


Duke University

14. Oh, Dong Hwan. Copulas for High Dimensions: Models, Estimation, Inference, and Applications .

Degree: 2014, Duke University

  The dissertation consists of four chapters that concern topics on copulas for high dimensions. Chapter 1 proposes a new general model for high dimension(more)

Subjects/Keywords: Economics; Statistics; Copula; Dependence; Factor Copula; High Dimension; High Frequency data; Volatility

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Oh, D. H. (2014). Copulas for High Dimensions: Models, Estimation, Inference, and Applications . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/8735

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Oh, Dong Hwan. “Copulas for High Dimensions: Models, Estimation, Inference, and Applications .” 2014. Thesis, Duke University. Accessed October 21, 2019. http://hdl.handle.net/10161/8735.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Oh, Dong Hwan. “Copulas for High Dimensions: Models, Estimation, Inference, and Applications .” 2014. Web. 21 Oct 2019.

Vancouver:

Oh DH. Copulas for High Dimensions: Models, Estimation, Inference, and Applications . [Internet] [Thesis]. Duke University; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10161/8735.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oh DH. Copulas for High Dimensions: Models, Estimation, Inference, and Applications . [Thesis]. Duke University; 2014. Available from: http://hdl.handle.net/10161/8735

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université de Grenoble

15. Mazo, Gildas. Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas.

Degree: Docteur es, Mathématiques appliquées, 2014, Université de Grenoble

Ces dernières décennies, nous avons assisté à l'émergence du concept de copule en modélisation statistique. Les copules permettent de faire une analyse séparée des marges… (more)

Subjects/Keywords: Copules; Grande dimension; Inférence; Valeurs extrêmes; Modèles à facteurs; Copulas; High dimension; Inference; Extreme values; Factor models; 510

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mazo, G. (2014). Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2014GRENM058

Chicago Manual of Style (16th Edition):

Mazo, Gildas. “Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas.” 2014. Doctoral Dissertation, Université de Grenoble. Accessed October 21, 2019. http://www.theses.fr/2014GRENM058.

MLA Handbook (7th Edition):

Mazo, Gildas. “Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas.” 2014. Web. 21 Oct 2019.

Vancouver:

Mazo G. Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas. [Internet] [Doctoral dissertation]. Université de Grenoble; 2014. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2014GRENM058.

Council of Science Editors:

Mazo G. Construction et estimation de copules en grande dimension : Construction and estimation of high-dimensional copulas. [Doctoral Dissertation]. Université de Grenoble; 2014. Available from: http://www.theses.fr/2014GRENM058

16. Perthame, Emeline. Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension.

Degree: Docteur es, Mathématiques et applications, 2015, Rennes 1

Les données à haut-débit, par leur grande dimension et leur hétérogénéité, ont motivé le développement de méthodes statistiques pour la sélection de variables. En effet,… (more)

Subjects/Keywords: Statistique; Grande dimension; Sélection de variables; Dépendance; Régression; Modèle linéaire généralisé; Statistics; High dimension; Variable selection; Dependence; Regression; Generalized linear model

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Perthame, E. (2015). Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension. (Doctoral Dissertation). Rennes 1. Retrieved from http://www.theses.fr/2015REN1S122

Chicago Manual of Style (16th Edition):

Perthame, Emeline. “Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension.” 2015. Doctoral Dissertation, Rennes 1. Accessed October 21, 2019. http://www.theses.fr/2015REN1S122.

MLA Handbook (7th Edition):

Perthame, Emeline. “Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension.” 2015. Web. 21 Oct 2019.

Vancouver:

Perthame E. Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension. [Internet] [Doctoral dissertation]. Rennes 1; 2015. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2015REN1S122.

Council of Science Editors:

Perthame E. Stabilité de la sélection de variables pour la régression et la classification de données corrélées en grande dimension : Stability of variable selection in regression and classification issues for correlated data in high dimension. [Doctoral Dissertation]. Rennes 1; 2015. Available from: http://www.theses.fr/2015REN1S122

17. Ling, Julien. An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières.

Degree: Docteur es, Sciences de gestion, 2018, Paris Sciences et Lettres

Cette thèse vise à analyser le risque systémique sur les marchés futures de matières premières. En effet, plusieurs travaux de recherche mettent en évidence l'importance… (more)

Subjects/Keywords: Risque systémique; Matières premières; Financiarisation; Graphe; Grande dimension; Systemic risk; Commodities; Financialization; Graph; High dimension; 658.15

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ling, J. (2018). An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières. (Doctoral Dissertation). Paris Sciences et Lettres. Retrieved from http://www.theses.fr/2018PSLED022

Chicago Manual of Style (16th Edition):

Ling, Julien. “An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières.” 2018. Doctoral Dissertation, Paris Sciences et Lettres. Accessed October 21, 2019. http://www.theses.fr/2018PSLED022.

MLA Handbook (7th Edition):

Ling, Julien. “An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières.” 2018. Web. 21 Oct 2019.

Vancouver:

Ling J. An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières. [Internet] [Doctoral dissertation]. Paris Sciences et Lettres; 2018. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2018PSLED022.

Council of Science Editors:

Ling J. An empirical analysis of systemic risk in commodity futures markets : Une analyse empirique du risque systémique sur les marchés futures de matières premières. [Doctoral Dissertation]. Paris Sciences et Lettres; 2018. Available from: http://www.theses.fr/2018PSLED022

18. Lafond, Jean. Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels.

Degree: Docteur es, Mathématiques appliquées, 2016, Paris Saclay

Dans cette thèse nous nous intéressons aux méthodes de complétion de matrices de faible rang et étudions certains problèmes reliés. Un premier ensemble de résultats… (more)

Subjects/Keywords: Statistique en grande dimension; Complétion de matrice; Apprentissage à grande échelle; High dimension statistics; Matrix completion; Large scale optimization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lafond, J. (2016). Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLT002

Chicago Manual of Style (16th Edition):

Lafond, Jean. “Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels.” 2016. Doctoral Dissertation, Paris Saclay. Accessed October 21, 2019. http://www.theses.fr/2016SACLT002.

MLA Handbook (7th Edition):

Lafond, Jean. “Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels.” 2016. Web. 21 Oct 2019.

Vancouver:

Lafond J. Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2016SACLT002.

Council of Science Editors:

Lafond J. Matrix completion : statistical and computational aspects : Complétion de matrice : aspects statistiques et computationnels. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLT002

19. Ribaud, Mélina. Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization.

Degree: Docteur es, Mathématiques, 2018, Lyon

Dans le secteur de l'automobile, les turbomachines sont des machines tournantes participant au refroidissement des moteurs des voitures. Leur performance dépend de multiples paramètres géométriques… (more)

Subjects/Keywords: Krigeage; Algorithme; Grande dimension; Noyau de covariance; Optimisation robuste; Kriging; Algorithm; High dimension; Covariance kernel; Robust optimization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ribaud, M. (2018). Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2018LYSEC026

Chicago Manual of Style (16th Edition):

Ribaud, Mélina. “Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization.” 2018. Doctoral Dissertation, Lyon. Accessed October 21, 2019. http://www.theses.fr/2018LYSEC026.

MLA Handbook (7th Edition):

Ribaud, Mélina. “Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization.” 2018. Web. 21 Oct 2019.

Vancouver:

Ribaud M. Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization. [Internet] [Doctoral dissertation]. Lyon; 2018. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2018LYSEC026.

Council of Science Editors:

Ribaud M. Krigeage pour la conception de turbomachines : grande dimension et optimisation multi-objectif robuste : Kriging for turbomachineries conception : high dimension and multi-objective robust optimization. [Doctoral Dissertation]. Lyon; 2018. Available from: http://www.theses.fr/2018LYSEC026

20. Sebbar, Mehdi. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.

Degree: Docteur es, Mathématiques appliquées, 2017, Paris Saclay

 Dans ce mémoire de thèse, nous abordons deux thèmes, le clustering en haute dimension d'une part et l'estimation de densités de mélange d'autre part. Le… (more)

Subjects/Keywords: Clustering; Agrégation; Grande dimension; Estimation de densité; Mélange de gaussiennes; Gaussian mixtures; Clustering; High dimension; Density estimation; Aggregation; 519; 62

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sebbar, M. (2017). On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2017SACLG003

Chicago Manual of Style (16th Edition):

Sebbar, Mehdi. “On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.” 2017. Doctoral Dissertation, Paris Saclay. Accessed October 21, 2019. http://www.theses.fr/2017SACLG003.

MLA Handbook (7th Edition):

Sebbar, Mehdi. “On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.” 2017. Web. 21 Oct 2019.

Vancouver:

Sebbar M. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2017SACLG003.

Council of Science Editors:

Sebbar M. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. [Doctoral Dissertation]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLG003

21. Ndaoud, Mohamed. Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension.

Degree: Docteur es, Mathématiques fondamentales, 2019, Paris Saclay

Cette thèse traite les problèmes statistiques suivants : la sélection de variables dans le modèle de régression linéaire en grande dimension, le clustering dans le… (more)

Subjects/Keywords: Grande dimension; Regression linéaire; Clustering; Transition de phase; Linear regression; High dimension; Clustering; Phase transition; 510; 62J05; 62H12; 62C20

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ndaoud, M. (2019). Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2019SACLG005

Chicago Manual of Style (16th Edition):

Ndaoud, Mohamed. “Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension.” 2019. Doctoral Dissertation, Paris Saclay. Accessed October 21, 2019. http://www.theses.fr/2019SACLG005.

MLA Handbook (7th Edition):

Ndaoud, Mohamed. “Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension.” 2019. Web. 21 Oct 2019.

Vancouver:

Ndaoud M. Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension. [Internet] [Doctoral dissertation]. Paris Saclay; 2019. [cited 2019 Oct 21]. Available from: http://www.theses.fr/2019SACLG005.

Council of Science Editors:

Ndaoud M. Contributions to variable selection, clustering and statistical estimation inhigh dimension : Quelques contributions à la sélection de variables, au clustering et à l’estimation statistique en grande dimension. [Doctoral Dissertation]. Paris Saclay; 2019. Available from: http://www.theses.fr/2019SACLG005


University of Georgia

22. Hilafu, Haileab. On dimension reduction and feature selection in high dimensions.

Degree: PhD, Statistics, 2014, University of Georgia

 Suffi cient Dimension Reduction (SDR) is a dimension reduction paradigm for reducing the dimension of the predictor vector without losing regression information. Classical inverse regression… (more)

Subjects/Keywords: Dimension Reduction Subspace; Feature Selection; Central Subspace; High Dimensional Data; Partial Least Squares; Regularization.

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hilafu, H. (2014). On dimension reduction and feature selection in high dimensions. (Doctoral Dissertation). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/hilafu_haileab_201405_phd

Chicago Manual of Style (16th Edition):

Hilafu, Haileab. “On dimension reduction and feature selection in high dimensions.” 2014. Doctoral Dissertation, University of Georgia. Accessed October 21, 2019. http://purl.galileo.usg.edu/uga_etd/hilafu_haileab_201405_phd.

MLA Handbook (7th Edition):

Hilafu, Haileab. “On dimension reduction and feature selection in high dimensions.” 2014. Web. 21 Oct 2019.

Vancouver:

Hilafu H. On dimension reduction and feature selection in high dimensions. [Internet] [Doctoral dissertation]. University of Georgia; 2014. [cited 2019 Oct 21]. Available from: http://purl.galileo.usg.edu/uga_etd/hilafu_haileab_201405_phd.

Council of Science Editors:

Hilafu H. On dimension reduction and feature selection in high dimensions. [Doctoral Dissertation]. University of Georgia; 2014. Available from: http://purl.galileo.usg.edu/uga_etd/hilafu_haileab_201405_phd


Temple University

23. Spirko, Lauren Nicole. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.

Degree: PhD, 2017, Temple University

Statistics

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes, providing insight into the… (more)

Subjects/Keywords: Statistics; Biostatistics;

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Spirko, L. N. (2017). Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,466860

Chicago Manual of Style (16th Edition):

Spirko, Lauren Nicole. “Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.” 2017. Doctoral Dissertation, Temple University. Accessed October 21, 2019. http://digital.library.temple.edu/u?/p245801coll10,466860.

MLA Handbook (7th Edition):

Spirko, Lauren Nicole. “Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.” 2017. Web. 21 Oct 2019.

Vancouver:

Spirko LN. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2019 Oct 21]. Available from: http://digital.library.temple.edu/u?/p245801coll10,466860.

Council of Science Editors:

Spirko LN. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. [Doctoral Dissertation]. Temple University; 2017. Available from: http://digital.library.temple.edu/u?/p245801coll10,466860


Lincoln University

24. Senay, Senait Dereje. Modelling invasive species-landscape interactions using high resolution, spatially explicit models.

Degree: 2014, Lincoln University

 Invasive species can cause a wide range of damages from destruction of indigenous and productive ecosystems to introduction of vectors to human and animal diseases.… (more)

Subjects/Keywords: SDMs; invasive species; dispersal; spatial modelling; high resolution; model uncertainty; dimension reduction

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Senay, S. D. (2014). Modelling invasive species-landscape interactions using high resolution, spatially explicit models. (Thesis). Lincoln University. Retrieved from http://hdl.handle.net/10182/6385

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Senay, Senait Dereje. “Modelling invasive species-landscape interactions using high resolution, spatially explicit models.” 2014. Thesis, Lincoln University. Accessed October 21, 2019. http://hdl.handle.net/10182/6385.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Senay, Senait Dereje. “Modelling invasive species-landscape interactions using high resolution, spatially explicit models.” 2014. Web. 21 Oct 2019.

Vancouver:

Senay SD. Modelling invasive species-landscape interactions using high resolution, spatially explicit models. [Internet] [Thesis]. Lincoln University; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10182/6385.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Senay SD. Modelling invasive species-landscape interactions using high resolution, spatially explicit models. [Thesis]. Lincoln University; 2014. Available from: http://hdl.handle.net/10182/6385

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – Berkeley

25. Lei, Jing. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.

Degree: Statistics, 2010, University of California – Berkeley

 State space models are powerful modeling tools for stochastic dynamical systems and have been an important research area in the statistics community in the last… (more)

Subjects/Keywords: Statistics; chaotic dynamical systems; high dimension data; Kalman filter; particle filter; robustness; state space models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lei, J. (2010). Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/3tm9052d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lei, Jing. “Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.” 2010. Thesis, University of California – Berkeley. Accessed October 21, 2019. http://www.escholarship.org/uc/item/3tm9052d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lei, Jing. “Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.” 2010. Web. 21 Oct 2019.

Vancouver:

Lei J. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2019 Oct 21]. Available from: http://www.escholarship.org/uc/item/3tm9052d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lei J. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/3tm9052d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Kentucky

26. Kong, Xiaoli. High Dimensional Multivariate Inference Under General Conditions.

Degree: 2018, University of Kentucky

 In this dissertation, we investigate four distinct and interrelated problems for high-dimensional inference of mean vectors in multi-groups. The first problem concerned is the profile… (more)

Subjects/Keywords: Profile analysis; MANOVA; High-dimension; Repeated measure; Non-parametric; Rank transforms; Multivariate Analysis; Statistical Methodology

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kong, X. (2018). High Dimensional Multivariate Inference Under General Conditions. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/33

Chicago Manual of Style (16th Edition):

Kong, Xiaoli. “High Dimensional Multivariate Inference Under General Conditions.” 2018. Doctoral Dissertation, University of Kentucky. Accessed October 21, 2019. https://uknowledge.uky.edu/statistics_etds/33.

MLA Handbook (7th Edition):

Kong, Xiaoli. “High Dimensional Multivariate Inference Under General Conditions.” 2018. Web. 21 Oct 2019.

Vancouver:

Kong X. High Dimensional Multivariate Inference Under General Conditions. [Internet] [Doctoral dissertation]. University of Kentucky; 2018. [cited 2019 Oct 21]. Available from: https://uknowledge.uky.edu/statistics_etds/33.

Council of Science Editors:

Kong X. High Dimensional Multivariate Inference Under General Conditions. [Doctoral Dissertation]. University of Kentucky; 2018. Available from: https://uknowledge.uky.edu/statistics_etds/33


University of Pennsylvania

27. Guo, Zijian. Statistical Inference For High-Dimensional Linear Models.

Degree: 2017, University of Pennsylvania

High-dimensional linear models play an important role in the analysis of modern data sets. Although the estimation problem has been well understood, there is still… (more)

Subjects/Keywords: Accuracy Assessment; Confidence Intervals; High-dimension; Instrumental Variable; Linear Models; Statistics and Probability

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Guo, Z. (2017). Statistical Inference For High-Dimensional Linear Models. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/2320

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Guo, Zijian. “Statistical Inference For High-Dimensional Linear Models.” 2017. Thesis, University of Pennsylvania. Accessed October 21, 2019. https://repository.upenn.edu/edissertations/2320.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Guo, Zijian. “Statistical Inference For High-Dimensional Linear Models.” 2017. Web. 21 Oct 2019.

Vancouver:

Guo Z. Statistical Inference For High-Dimensional Linear Models. [Internet] [Thesis]. University of Pennsylvania; 2017. [cited 2019 Oct 21]. Available from: https://repository.upenn.edu/edissertations/2320.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Guo Z. Statistical Inference For High-Dimensional Linear Models. [Thesis]. University of Pennsylvania; 2017. Available from: https://repository.upenn.edu/edissertations/2320

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Kentucky

28. Villasante Tezanos, Alejandro G. COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION.

Degree: 2019, University of Kentucky

 This dissertation focuses on the problem of making high-dimensional inference for two or more groups. High-dimensional means both the sample size (n) and dimension (p)… (more)

Subjects/Keywords: Multivariate Analysis; High Dimension; Statistical Tests; Multivariate Analysis; Statistical Methodology; Statistical Models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Villasante Tezanos, A. G. (2019). COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/42

Chicago Manual of Style (16th Edition):

Villasante Tezanos, Alejandro G. “COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION.” 2019. Doctoral Dissertation, University of Kentucky. Accessed October 21, 2019. https://uknowledge.uky.edu/statistics_etds/42.

MLA Handbook (7th Edition):

Villasante Tezanos, Alejandro G. “COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION.” 2019. Web. 21 Oct 2019.

Vancouver:

Villasante Tezanos AG. COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION. [Internet] [Doctoral dissertation]. University of Kentucky; 2019. [cited 2019 Oct 21]. Available from: https://uknowledge.uky.edu/statistics_etds/42.

Council of Science Editors:

Villasante Tezanos AG. COMPOSITE NONPARAMETRIC TESTS IN HIGH DIMENSION. [Doctoral Dissertation]. University of Kentucky; 2019. Available from: https://uknowledge.uky.edu/statistics_etds/42


Stellenbosch University

29. Stulumani, Agrippa. Classification in high dimensional data using sparse techniques.

Degree: MCom, Statistics and Actuarial Science, 2019, Stellenbosch University

ENGLISH SUMMARY : Traditional classification techniques fail in the analysis of high-dimensional data. In response, new classification techniques and accompanying theory have recently emerged. These… (more)

Subjects/Keywords: High dimensional data; Mathematical statistics; Sparse classification; Sparse grids; Dimension reduction (Statistics); UCTD

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Stulumani, A. (2019). Classification in high dimensional data using sparse techniques. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105792

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Stulumani, Agrippa. “Classification in high dimensional data using sparse techniques.” 2019. Thesis, Stellenbosch University. Accessed October 21, 2019. http://hdl.handle.net/10019.1/105792.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Stulumani, Agrippa. “Classification in high dimensional data using sparse techniques.” 2019. Web. 21 Oct 2019.

Vancouver:

Stulumani A. Classification in high dimensional data using sparse techniques. [Internet] [Thesis]. Stellenbosch University; 2019. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10019.1/105792.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stulumani A. Classification in high dimensional data using sparse techniques. [Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105792

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Arizona State University

30. Yu, Renwei. Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space.

Degree: MS, Computer Science, 2011, Arizona State University

 K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism… (more)

Subjects/Keywords: Computer Science; high dimension; K nearest neighbor; large scale; locality sensitive hash

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yu, R. (2011). Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/9178

Chicago Manual of Style (16th Edition):

Yu, Renwei. “Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space.” 2011. Masters Thesis, Arizona State University. Accessed October 21, 2019. http://repository.asu.edu/items/9178.

MLA Handbook (7th Edition):

Yu, Renwei. “Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space.” 2011. Web. 21 Oct 2019.

Vancouver:

Yu R. Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space. [Internet] [Masters thesis]. Arizona State University; 2011. [cited 2019 Oct 21]. Available from: http://repository.asu.edu/items/9178.

Council of Science Editors:

Yu R. Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space. [Masters Thesis]. Arizona State University; 2011. Available from: http://repository.asu.edu/items/9178

[1] [2] [3] [4]

.