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- 2001 – 2005 (11)

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Cornell University

1. Zawack, Kelson. A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM.

Degree: PhD, Biometry, 2017, Cornell University

URL: http://hdl.handle.net/1813/51621

► Surveillance is a key component of controlling antimicrobial resistance. In the United States this function is carried out by the National Antimicrobial Monitoring System. This…
(more)

Subjects/Keywords: Computational Biology; Epidemiology; Statistics; Antimicrobial Resistance

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

APA (6^{th} Edition):

Zawack, K. (2017). A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/51621

Chicago Manual of Style (16^{th} Edition):

Zawack, Kelson. “A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM.” 2017. Doctoral Dissertation, Cornell University. Accessed October 25, 2020. http://hdl.handle.net/1813/51621.

MLA Handbook (7^{th} Edition):

Zawack, Kelson. “A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM.” 2017. Web. 25 Oct 2020.

Vancouver:

Zawack K. A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/1813/51621.

Council of Science Editors:

Zawack K. A COMPREHENSIVE ANALYSIS OF THE UNITED STATES' NATIONAL ANTIMICROBIAL RESISTANCE MONITERING SYSTEM. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51621

2. Nunkesser, Robin. Algorithms for regression and classification.

Degree: 2009, Technische Universität Dortmund

URL: http://hdl.handle.net/2003/26047

► Regression and classification are statistical techniques that may be used to extract rules and patterns out of data sets. Analyzing the involved algorithms comprises interdisciplinary…
(more)

Subjects/Keywords: Association studies; Computational statistics; Robust regression; 004

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APA (6^{th} Edition):

Nunkesser, R. (2009). Algorithms for regression and classification. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/26047

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Nunkesser, Robin. “Algorithms for regression and classification.” 2009. Thesis, Technische Universität Dortmund. Accessed October 25, 2020. http://hdl.handle.net/2003/26047.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Nunkesser, Robin. “Algorithms for regression and classification.” 2009. Web. 25 Oct 2020.

Vancouver:

Nunkesser R. Algorithms for regression and classification. [Internet] [Thesis]. Technische Universität Dortmund; 2009. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2003/26047.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nunkesser R. Algorithms for regression and classification. [Thesis]. Technische Universität Dortmund; 2009. Available from: http://hdl.handle.net/2003/26047

Not specified: Masters Thesis or Doctoral Dissertation

Montana State University

3. Sturlaugson, Liessman Eric. Extensions to modeling and inference in continuous time Bayesian networks.

Degree: PhD, College of Engineering, 2014, Montana State University

URL: https://scholarworks.montana.edu/xmlui/handle/1/9368

► The continuous time Bayesian network (CTBN) enables reasoning about complex systems in continuous time by representing a system as a factored, finite-state, continuous-time Markov process.…
(more)

Subjects/Keywords: Markov processes.; Computational complexity.; Graphical modeling (Statistics).

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APA (6^{th} Edition):

Sturlaugson, L. E. (2014). Extensions to modeling and inference in continuous time Bayesian networks. (Doctoral Dissertation). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/9368

Chicago Manual of Style (16^{th} Edition):

Sturlaugson, Liessman Eric. “Extensions to modeling and inference in continuous time Bayesian networks.” 2014. Doctoral Dissertation, Montana State University. Accessed October 25, 2020. https://scholarworks.montana.edu/xmlui/handle/1/9368.

MLA Handbook (7^{th} Edition):

Sturlaugson, Liessman Eric. “Extensions to modeling and inference in continuous time Bayesian networks.” 2014. Web. 25 Oct 2020.

Vancouver:

Sturlaugson LE. Extensions to modeling and inference in continuous time Bayesian networks. [Internet] [Doctoral dissertation]. Montana State University; 2014. [cited 2020 Oct 25]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9368.

Council of Science Editors:

Sturlaugson LE. Extensions to modeling and inference in continuous time Bayesian networks. [Doctoral Dissertation]. Montana State University; 2014. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9368

Massey University

4. Anwar, Muhammad Nafees. Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 .

Degree: 2012, Massey University

URL: http://hdl.handle.net/10179/4287

► The class imbalance problem is a challenge in the statistical, machine learn- ing and data mining domains. Examples include fraud/intrusion detection, medical diagnosis/monitoring, bioinformatics, text…
(more)

Subjects/Keywords: Computational complexity; Class imbalance; Classification; Sampling; Statistics

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APA (6^{th} Edition):

Anwar, M. N. (2012). Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 . (Thesis). Massey University. Retrieved from http://hdl.handle.net/10179/4287

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Anwar, Muhammad Nafees. “Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 .” 2012. Thesis, Massey University. Accessed October 25, 2020. http://hdl.handle.net/10179/4287.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Anwar, Muhammad Nafees. “Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 .” 2012. Web. 25 Oct 2020.

Vancouver:

Anwar MN. Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 . [Internet] [Thesis]. Massey University; 2012. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/10179/4287.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Anwar MN. Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012 . [Thesis]. Massey University; 2012. Available from: http://hdl.handle.net/10179/4287

Not specified: Masters Thesis or Doctoral Dissertation

University of Sydney

5. Greenaway, Mark Jonathan. Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection .

Degree: 2019, University of Sydney

URL: http://hdl.handle.net/2123/20233

► Approximate Bayesian inference methods offer methodology for fitting Bayesian models as fast alternatives to Markov Chain Monte Carlo methods that sometimes have only a slight…
(more)

Subjects/Keywords: variational approximations; model selection; computational statistics

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APA (6^{th} Edition):

Greenaway, M. J. (2019). Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20233

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Greenaway, Mark Jonathan. “Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection .” 2019. Thesis, University of Sydney. Accessed October 25, 2020. http://hdl.handle.net/2123/20233.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Greenaway, Mark Jonathan. “Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection .” 2019. Web. 25 Oct 2020.

Vancouver:

Greenaway MJ. Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection . [Internet] [Thesis]. University of Sydney; 2019. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2123/20233.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Greenaway MJ. Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection . [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/20233

Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

6. Bozhilova, Lyuba Ventsislavova. Confidence in protein interaction networks.

Degree: PhD, 2020, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:c28f1db6-0ce8-4d36-8bea-2ed310cbdb25 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.813573

► Protein interaction networks are a commonly used tool in bioinformatics, e.g. for the purposes of gene function prediction or drug target identification. They are built…
(more)

Subjects/Keywords: Network analysis; Statistics; Computational biology; Bioinformatics

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APA (6^{th} Edition):

Bozhilova, L. V. (2020). Confidence in protein interaction networks. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:c28f1db6-0ce8-4d36-8bea-2ed310cbdb25 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.813573

Chicago Manual of Style (16^{th} Edition):

Bozhilova, Lyuba Ventsislavova. “Confidence in protein interaction networks.” 2020. Doctoral Dissertation, University of Oxford. Accessed October 25, 2020. http://ora.ox.ac.uk/objects/uuid:c28f1db6-0ce8-4d36-8bea-2ed310cbdb25 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.813573.

MLA Handbook (7^{th} Edition):

Bozhilova, Lyuba Ventsislavova. “Confidence in protein interaction networks.” 2020. Web. 25 Oct 2020.

Vancouver:

Bozhilova LV. Confidence in protein interaction networks. [Internet] [Doctoral dissertation]. University of Oxford; 2020. [cited 2020 Oct 25]. Available from: http://ora.ox.ac.uk/objects/uuid:c28f1db6-0ce8-4d36-8bea-2ed310cbdb25 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.813573.

Council of Science Editors:

Bozhilova LV. Confidence in protein interaction networks. [Doctoral Dissertation]. University of Oxford; 2020. Available from: http://ora.ox.ac.uk/objects/uuid:c28f1db6-0ce8-4d36-8bea-2ed310cbdb25 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.813573

Youngstown State University

7. Khavari, Sepideh. Predicting Human and Animal Protein Subcellular Location.

Degree: MSin Mathematics, Department of Mathematics and Statistics, 2016, Youngstown State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1472463855

► An important objective in cell biology is to determine the subcellular location of different proteins and their functions in the cell. Identifying the subcellular location…
(more)

Subjects/Keywords: Biology; Statistics; Protein; Subcellular location; Computational predictors

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APA (6^{th} Edition):

Khavari, S. (2016). Predicting Human and Animal Protein Subcellular Location. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu1472463855

Chicago Manual of Style (16^{th} Edition):

Khavari, Sepideh. “Predicting Human and Animal Protein Subcellular Location.” 2016. Masters Thesis, Youngstown State University. Accessed October 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1472463855.

MLA Handbook (7^{th} Edition):

Khavari, Sepideh. “Predicting Human and Animal Protein Subcellular Location.” 2016. Web. 25 Oct 2020.

Vancouver:

Khavari S. Predicting Human and Animal Protein Subcellular Location. [Internet] [Masters thesis]. Youngstown State University; 2016. [cited 2020 Oct 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1472463855.

Council of Science Editors:

Khavari S. Predicting Human and Animal Protein Subcellular Location. [Masters Thesis]. Youngstown State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1472463855

University of California – Berkeley

8. Bouchard-Côté, Alexandre. Probabilistic Models of Evolution and Language Change.

Degree: Computer Science, 2010, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/3593s5m7

► Both linguistics and biology face scientific questions that require reconstructing phylogenies and ancestral sequences from a collection of modern descendants. In linguistics, these ancestral sequences…
(more)

Subjects/Keywords: Computer science; Statistics; Artificial intelligence; Computational biology; Computational linguistics; Historical linguistics; Inference; Machine learning; Phylogenetics

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APA (6^{th} Edition):

Bouchard-Côté, A. (2010). Probabilistic Models of Evolution and Language Change. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/3593s5m7

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bouchard-Côté, Alexandre. “Probabilistic Models of Evolution and Language Change.” 2010. Thesis, University of California – Berkeley. Accessed October 25, 2020. http://www.escholarship.org/uc/item/3593s5m7.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bouchard-Côté, Alexandre. “Probabilistic Models of Evolution and Language Change.” 2010. Web. 25 Oct 2020.

Vancouver:

Bouchard-Côté A. Probabilistic Models of Evolution and Language Change. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2020 Oct 25]. Available from: http://www.escholarship.org/uc/item/3593s5m7.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bouchard-Côté A. Probabilistic Models of Evolution and Language Change. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/3593s5m7

Not specified: Masters Thesis or Doctoral Dissertation

Rice University

9.
Mousavi, Ali.
Data-Driven *Computational* Sensing.

Degree: PhD, Engineering, 2018, Rice University

URL: http://hdl.handle.net/1911/105779

► Great progress has been made on sensing, perception, and signal processing over the last decades through the design of algorithms matched to the underlying physics…
(more)

Subjects/Keywords: Computational Sensing; Deep Learning; Machine Learning; Statistics; Inverse Problem; LASSO; Computational Imaging

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APA (6^{th} Edition):

Mousavi, A. (2018). Data-Driven Computational Sensing. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105779

Chicago Manual of Style (16^{th} Edition):

Mousavi, Ali. “Data-Driven Computational Sensing.” 2018. Doctoral Dissertation, Rice University. Accessed October 25, 2020. http://hdl.handle.net/1911/105779.

MLA Handbook (7^{th} Edition):

Mousavi, Ali. “Data-Driven Computational Sensing.” 2018. Web. 25 Oct 2020.

Vancouver:

Mousavi A. Data-Driven Computational Sensing. [Internet] [Doctoral dissertation]. Rice University; 2018. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/1911/105779.

Council of Science Editors:

Mousavi A. Data-Driven Computational Sensing. [Doctoral Dissertation]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105779

University of California – Berkeley

10. Bloniarz, Adam. Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics.

Degree: Statistics, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/32f0g2w9

► Many scientific fields have been changed by rapid technological progress in data collection, storage, and processing. This has greatly expanded the role of *statistics* in…
(more)

Subjects/Keywords: Statistics; Neurosciences; Biostatistics; Causal inference; Computational Neuroscience; Distributed computation; High-dimensional statistics

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APA (6^{th} Edition):

Bloniarz, A. (2016). Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/32f0g2w9

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bloniarz, Adam. “Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics.” 2016. Thesis, University of California – Berkeley. Accessed October 25, 2020. http://www.escholarship.org/uc/item/32f0g2w9.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bloniarz, Adam. “Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics.” 2016. Web. 25 Oct 2020.

Vancouver:

Bloniarz A. Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2020 Oct 25]. Available from: http://www.escholarship.org/uc/item/32f0g2w9.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bloniarz A. Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/32f0g2w9

Not specified: Masters Thesis or Doctoral Dissertation

Utah State University

11.
Apedaile, Thomas J.
* Computational* Topics in Lie Theory and Representation Theory.

Degree: MS, Mathematics and Statistics, 2014, Utah State University

URL: https://digitalcommons.usu.edu/etd/2156

► The computer algebra system Maple contains a basic set of commands for working with Lie algebras. The purpose of this thesis was to extend…
(more)

Subjects/Keywords: Computational; Theory; Lie; Representation; Theory; Applied Statistics; Physical Sciences and Mathematics; Statistics and Probability

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APA (6^{th} Edition):

Apedaile, T. J. (2014). Computational Topics in Lie Theory and Representation Theory. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/2156

Chicago Manual of Style (16^{th} Edition):

Apedaile, Thomas J. “Computational Topics in Lie Theory and Representation Theory.” 2014. Masters Thesis, Utah State University. Accessed October 25, 2020. https://digitalcommons.usu.edu/etd/2156.

MLA Handbook (7^{th} Edition):

Apedaile, Thomas J. “Computational Topics in Lie Theory and Representation Theory.” 2014. Web. 25 Oct 2020.

Vancouver:

Apedaile TJ. Computational Topics in Lie Theory and Representation Theory. [Internet] [Masters thesis]. Utah State University; 2014. [cited 2020 Oct 25]. Available from: https://digitalcommons.usu.edu/etd/2156.

Council of Science Editors:

Apedaile TJ. Computational Topics in Lie Theory and Representation Theory. [Masters Thesis]. Utah State University; 2014. Available from: https://digitalcommons.usu.edu/etd/2156

University of Michigan

12. Lu, Zhiyuan. Large Data Approaches to Thresholding Problems.

Degree: PhD, Statistics, 2019, University of Michigan

URL: http://hdl.handle.net/2027.42/153384

► Statistical models with discontinuities have seen much use in a variety of situations, in practical fields such as statistical process control, processing gene data, and…
(more)

Subjects/Keywords: change point; adaptive sampling; computational time; Statistics and Numeric Data; Science

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APA (6^{th} Edition):

Lu, Z. (2019). Large Data Approaches to Thresholding Problems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153384

Chicago Manual of Style (16^{th} Edition):

Lu, Zhiyuan. “Large Data Approaches to Thresholding Problems.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 25, 2020. http://hdl.handle.net/2027.42/153384.

MLA Handbook (7^{th} Edition):

Lu, Zhiyuan. “Large Data Approaches to Thresholding Problems.” 2019. Web. 25 Oct 2020.

Vancouver:

Lu Z. Large Data Approaches to Thresholding Problems. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2027.42/153384.

Council of Science Editors:

Lu Z. Large Data Approaches to Thresholding Problems. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153384

Iowa State University

13. Zhou, Hua. Branching process models for HIV-1 drug resistant mutants.

Degree: 2003, Iowa State University

URL: https://lib.dr.iastate.edu/rtd/20104

► Drug therapy for human immunodeficiency virus type 1 (HIV-1) infection often fails because of the appearance of resistant viral mutants. Thus knowledge on the abundance…
(more)

Subjects/Keywords: Statistics; Bioinformatics and computational biology

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APA (6^{th} Edition):

Zhou, H. (2003). Branching process models for HIV-1 drug resistant mutants. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/rtd/20104

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhou, Hua. “Branching process models for HIV-1 drug resistant mutants.” 2003. Thesis, Iowa State University. Accessed October 25, 2020. https://lib.dr.iastate.edu/rtd/20104.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhou, Hua. “Branching process models for HIV-1 drug resistant mutants.” 2003. Web. 25 Oct 2020.

Vancouver:

Zhou H. Branching process models for HIV-1 drug resistant mutants. [Internet] [Thesis]. Iowa State University; 2003. [cited 2020 Oct 25]. Available from: https://lib.dr.iastate.edu/rtd/20104.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhou H. Branching process models for HIV-1 drug resistant mutants. [Thesis]. Iowa State University; 2003. Available from: https://lib.dr.iastate.edu/rtd/20104

Not specified: Masters Thesis or Doctoral Dissertation

McMaster University

14. Blostein, Martin. An Efficient Implementation of a Robust Clustering Algorithm.

Degree: MSc, 2016, McMaster University

URL: http://hdl.handle.net/11375/20598

►

Clustering and classification are fundamental problems in statistical and machine learning, with a broad range of applications. A common approach is the Gaussian mixture model,… (more)

Subjects/Keywords: clustering; classification; statistical learning; machine learning; robust; computational statistics; mixture models

Record Details Similar Records

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APA (6^{th} Edition):

Blostein, M. (2016). An Efficient Implementation of a Robust Clustering Algorithm. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20598

Chicago Manual of Style (16^{th} Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Masters Thesis, McMaster University. Accessed October 25, 2020. http://hdl.handle.net/11375/20598.

MLA Handbook (7^{th} Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Web. 25 Oct 2020.

Vancouver:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/11375/20598.

Council of Science Editors:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20598

University of North Texas

15. Krämer, Reiner. Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”.

Degree: 2015, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc801959/

► The use of algorithms in compositional practice has been in use for centuries. With the advent of computers, formalized procedures have become an important part…
(more)

Subjects/Keywords: Music theory; composition; algorithm; machine learning; computational music analysis; statistics

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APA (6^{th} Edition):

Krämer, R. (2015). Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc801959/

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Krämer, Reiner. “Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”.” 2015. Thesis, University of North Texas. Accessed October 25, 2020. https://digital.library.unt.edu/ark:/67531/metadc801959/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Krämer, Reiner. “Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”.” 2015. Web. 25 Oct 2020.

Vancouver:

Krämer R. Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”. [Internet] [Thesis]. University of North Texas; 2015. [cited 2020 Oct 25]. Available from: https://digital.library.unt.edu/ark:/67531/metadc801959/.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Krämer R. Algorithmic Music Analysis: a Case Study of a Prelude From David Cope’s “From Darkness, Light”. [Thesis]. University of North Texas; 2015. Available from: https://digital.library.unt.edu/ark:/67531/metadc801959/

Not specified: Masters Thesis or Doctoral Dissertation

Colorado School of Mines

16. Simonson, Peter. Computationally efficient methods for block average models.

Degree: PhD, Applied Mathematics and Statistics, 2020, Colorado School of Mines

URL: http://hdl.handle.net/11124/174205

► In many applications, spatial data are typically collected at areal levels (i.e., block data), while inferences and predictions are desired about the variable at points…
(more)

Subjects/Keywords: computational efficiency; spatial statistics; Fourier transforms; block average models

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

APA (6^{th} Edition):

Simonson, P. (2020). Computationally efficient methods for block average models. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/174205

Chicago Manual of Style (16^{th} Edition):

Simonson, Peter. “Computationally efficient methods for block average models.” 2020. Doctoral Dissertation, Colorado School of Mines. Accessed October 25, 2020. http://hdl.handle.net/11124/174205.

MLA Handbook (7^{th} Edition):

Simonson, Peter. “Computationally efficient methods for block average models.” 2020. Web. 25 Oct 2020.

Vancouver:

Simonson P. Computationally efficient methods for block average models. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2020. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/11124/174205.

Council of Science Editors:

Simonson P. Computationally efficient methods for block average models. [Doctoral Dissertation]. Colorado School of Mines; 2020. Available from: http://hdl.handle.net/11124/174205

Boston University

17. Deng, Xinyi. Point process modeling and estimation: advances in the analysis of dynamic neural spiking data.

Degree: PhD, Mathematics & Statistics, 2016, Boston University

URL: http://hdl.handle.net/2144/17719

► A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete…
(more)

Subjects/Keywords: Statistics; Computational neuroscience; Point processes; State-space models

Record Details Similar Records

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APA (6^{th} Edition):

Deng, X. (2016). Point process modeling and estimation: advances in the analysis of dynamic neural spiking data. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/17719

Chicago Manual of Style (16^{th} Edition):

Deng, Xinyi. “Point process modeling and estimation: advances in the analysis of dynamic neural spiking data.” 2016. Doctoral Dissertation, Boston University. Accessed October 25, 2020. http://hdl.handle.net/2144/17719.

MLA Handbook (7^{th} Edition):

Deng, Xinyi. “Point process modeling and estimation: advances in the analysis of dynamic neural spiking data.” 2016. Web. 25 Oct 2020.

Vancouver:

Deng X. Point process modeling and estimation: advances in the analysis of dynamic neural spiking data. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2144/17719.

Council of Science Editors:

Deng X. Point process modeling and estimation: advances in the analysis of dynamic neural spiking data. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/17719

University of Southern California

18. Ravi, Sujith. Deciphering natural language.

Degree: PhD, Computer Science, 2011, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/448537/rec/1791

► Most state-of-the-art techniques used in natural language processing (NLP) are supervised and require labeled training data. For example, statistical language translation requires huge amounts of…
(more)

Subjects/Keywords: natural language processing; machine learning; computational decipherment; artificial intelligence; statistics

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APA (6^{th} Edition):

Ravi, S. (2011). Deciphering natural language. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/448537/rec/1791

Chicago Manual of Style (16^{th} Edition):

Ravi, Sujith. “Deciphering natural language.” 2011. Doctoral Dissertation, University of Southern California. Accessed October 25, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/448537/rec/1791.

MLA Handbook (7^{th} Edition):

Ravi, Sujith. “Deciphering natural language.” 2011. Web. 25 Oct 2020.

Vancouver:

Ravi S. Deciphering natural language. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2020 Oct 25]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/448537/rec/1791.

Council of Science Editors:

Ravi S. Deciphering natural language. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/448537/rec/1791

University of Kentucky

19. Wang, Hongyuan. Statistical Inference on Dynamical Systems.

Degree: 2016, University of Kentucky

URL: https://uknowledge.uky.edu/statistics_etds/22

► The ordinary differential equation (ODE) is one representative and popular tool in modeling dynamical systems, which are widely implemented in physics, biology, economics, chemistry and…
(more)

Subjects/Keywords: Dynamical System; ODE; Computational Statistics; EM Algorithm; Statistical Models

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APA (6^{th} Edition):

Wang, H. (2016). Statistical Inference on Dynamical Systems. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/22

Chicago Manual of Style (16^{th} Edition):

Wang, Hongyuan. “Statistical Inference on Dynamical Systems.” 2016. Doctoral Dissertation, University of Kentucky. Accessed October 25, 2020. https://uknowledge.uky.edu/statistics_etds/22.

MLA Handbook (7^{th} Edition):

Wang, Hongyuan. “Statistical Inference on Dynamical Systems.” 2016. Web. 25 Oct 2020.

Vancouver:

Wang H. Statistical Inference on Dynamical Systems. [Internet] [Doctoral dissertation]. University of Kentucky; 2016. [cited 2020 Oct 25]. Available from: https://uknowledge.uky.edu/statistics_etds/22.

Council of Science Editors:

Wang H. Statistical Inference on Dynamical Systems. [Doctoral Dissertation]. University of Kentucky; 2016. Available from: https://uknowledge.uky.edu/statistics_etds/22

Uppsala University

20. Zacharouli, Markella-Achilleia. Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method.

Degree: Biology Education Centre, 2020, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716

► Breast cancer is the most common type of cancer among women worldwide. Multiple studies have reported the role of tumor-immune interactions and mechanisms that…
(more)

Subjects/Keywords: breast cancer; bioinformatics; statistics; immunotherapy; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi)

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APA (6^{th} Edition):

Zacharouli, M. (2020). Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zacharouli, Markella-Achilleia. “Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method.” 2020. Thesis, Uppsala University. Accessed October 25, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zacharouli, Markella-Achilleia. “Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method.” 2020. Web. 25 Oct 2020.

Vancouver:

Zacharouli M. Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method. [Internet] [Thesis]. Uppsala University; 2020. [cited 2020 Oct 25]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zacharouli M. Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method. [Thesis]. Uppsala University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

21. Kennedy, Christopher Garrett. Fast high dimensional approximation via random embeddings.

Degree: PhD, Mathematics, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/72842

► In the big data era, dimension reduction techniques have been a key tool in making high dimensional geometric problems tractable. This thesis focuses on two…
(more)

Subjects/Keywords: Data structures and algorithms; Computational geometry; Statistics theory; Information theory

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APA (6^{th} Edition):

Kennedy, C. G. (2018). Fast high dimensional approximation via random embeddings. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72842

Chicago Manual of Style (16^{th} Edition):

Kennedy, Christopher Garrett. “Fast high dimensional approximation via random embeddings.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed October 25, 2020. http://hdl.handle.net/2152/72842.

MLA Handbook (7^{th} Edition):

Kennedy, Christopher Garrett. “Fast high dimensional approximation via random embeddings.” 2018. Web. 25 Oct 2020.

Vancouver:

Kennedy CG. Fast high dimensional approximation via random embeddings. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2152/72842.

Council of Science Editors:

Kennedy CG. Fast high dimensional approximation via random embeddings. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/72842

22.
McDermaid, Adam.
Statistical Algorithms and Bioinformatics Tools Development for *Computational* Analysis of High-throughput Transcriptomic Data.

Degree: PhD, Mathematics and Statistics, 2018, South Dakota State University

URL: https://openprairie.sdstate.edu/etd/2645

► Next-Generation Sequencing technologies allow for a substantial increase in the amount of data available for various biological studies. In order to effectively and efficiently…
(more)

Subjects/Keywords: algorithm development; computational tools; RNA-Sequencing; Biometry; Mathematics; Statistics and Probability

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APA (6^{th} Edition):

McDermaid, A. (2018). Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data. (Doctoral Dissertation). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/2645

Chicago Manual of Style (16^{th} Edition):

McDermaid, Adam. “Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data.” 2018. Doctoral Dissertation, South Dakota State University. Accessed October 25, 2020. https://openprairie.sdstate.edu/etd/2645.

MLA Handbook (7^{th} Edition):

McDermaid, Adam. “Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data.” 2018. Web. 25 Oct 2020.

Vancouver:

McDermaid A. Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data. [Internet] [Doctoral dissertation]. South Dakota State University; 2018. [cited 2020 Oct 25]. Available from: https://openprairie.sdstate.edu/etd/2645.

Council of Science Editors:

McDermaid A. Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data. [Doctoral Dissertation]. South Dakota State University; 2018. Available from: https://openprairie.sdstate.edu/etd/2645

University of Oxford

23. Lienart, Thibaut. Inference on Markov random fields : methods and applications.

Degree: PhD, 2017, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748731

► This thesis considers the problem of performing inference on undirected graphical models with continuous state spaces. These models represent conditional independence structures that can appear…
(more)

Subjects/Keywords: 006.3; Machine learning; Bayesian statistics; Computational statistics; Sampling Methods; Expectation Propagation; Markov Random Fields; Bouncy Particle Sampler; Computational Bayesian Statistics; Variational Inference; Bayesian Machine Learning

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

APA (6^{th} Edition):

Lienart, T. (2017). Inference on Markov random fields : methods and applications. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748731

Chicago Manual of Style (16^{th} Edition):

Lienart, Thibaut. “Inference on Markov random fields : methods and applications.” 2017. Doctoral Dissertation, University of Oxford. Accessed October 25, 2020. http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748731.

MLA Handbook (7^{th} Edition):

Lienart, Thibaut. “Inference on Markov random fields : methods and applications.” 2017. Web. 25 Oct 2020.

Vancouver:

Lienart T. Inference on Markov random fields : methods and applications. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2020 Oct 25]. Available from: http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748731.

Council of Science Editors:

Lienart T. Inference on Markov random fields : methods and applications. [Doctoral Dissertation]. University of Oxford; 2017. Available from: http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748731

University of Cambridge

24. Javid, Kamran. Physical modelling of galaxy clusters and Bayesian inference in astrophysics.

Degree: PhD, 2019, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/293473

► This thesis is concerned with the modelling of galaxy clusters, applying these models to real and simulated data using Bayesian inference, and the development of…
(more)

Subjects/Keywords: Mathematical modelling; Computational Astrophysics; Computational Statistics; Bayesian inference; Applied statistics; Monte Carlo methods; Markov Chain Monte Carlo; Data analysis; Bayesian statistics; Galaxy cluster modelling

Record Details Similar Records

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

APA (6^{th} Edition):

Javid, K. (2019). Physical modelling of galaxy clusters and Bayesian inference in astrophysics. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293473

Chicago Manual of Style (16^{th} Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics.” 2019. Doctoral Dissertation, University of Cambridge. Accessed October 25, 2020. https://www.repository.cam.ac.uk/handle/1810/293473.

MLA Handbook (7^{th} Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics.” 2019. Web. 25 Oct 2020.

Vancouver:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Oct 25]. Available from: https://www.repository.cam.ac.uk/handle/1810/293473.

Council of Science Editors:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293473

University of Cambridge

25. Javid, Kamran. Physical modelling of galaxy clusters and Bayesian inference in astrophysics.

Degree: PhD, 2019, University of Cambridge

URL: https://doi.org/10.17863/CAM.40616 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782759

► This thesis is concerned with the modelling of galaxy clusters, applying these models to real and simulated data using Bayesian inference, and the development of…
(more)

Subjects/Keywords: Mathematical modelling; Computational Astrophysics; Computational Statistics; Bayesian inference; Applied statistics; Monte Carlo methods; Markov Chain Monte Carlo; Data analysis; Bayesian statistics; Galaxy cluster modelling

Record Details Similar Records

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

APA (6^{th} Edition):

Javid, K. (2019). Physical modelling of galaxy clusters and Bayesian inference in astrophysics. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.40616 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782759

Chicago Manual of Style (16^{th} Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics.” 2019. Doctoral Dissertation, University of Cambridge. Accessed October 25, 2020. https://doi.org/10.17863/CAM.40616 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782759.

MLA Handbook (7^{th} Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics.” 2019. Web. 25 Oct 2020.

Vancouver:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Oct 25]. Available from: https://doi.org/10.17863/CAM.40616 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782759.

Council of Science Editors:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://doi.org/10.17863/CAM.40616 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782759

McMaster University

26. Deng, Wei Q. A statistical framework to detect gene-environment interactions influencing complex traits.

Degree: MSc, 2013, McMaster University

URL: http://hdl.handle.net/11375/15260

►

Advancements in human genomic technology have helped to improve our understanding of how genetic variation plays a central role in the mechanism of disease… (more)

Subjects/Keywords: genetic epidemiology; gene-environment interactions; variance heterogeneity; genetics; Applied Statistics; Bioinformatics; Biostatistics; Computational Biology; Genetics; Genomics; Statistical Methodology; Applied Statistics

Record Details Similar Records

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APA (6^{th} Edition):

Deng, W. Q. (2013). A statistical framework to detect gene-environment interactions influencing complex traits. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/15260

Chicago Manual of Style (16^{th} Edition):

Deng, Wei Q. “A statistical framework to detect gene-environment interactions influencing complex traits.” 2013. Masters Thesis, McMaster University. Accessed October 25, 2020. http://hdl.handle.net/11375/15260.

MLA Handbook (7^{th} Edition):

Deng, Wei Q. “A statistical framework to detect gene-environment interactions influencing complex traits.” 2013. Web. 25 Oct 2020.

Vancouver:

Deng WQ. A statistical framework to detect gene-environment interactions influencing complex traits. [Internet] [Masters thesis]. McMaster University; 2013. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/11375/15260.

Council of Science Editors:

Deng WQ. A statistical framework to detect gene-environment interactions influencing complex traits. [Masters Thesis]. McMaster University; 2013. Available from: http://hdl.handle.net/11375/15260

Iowa State University

27. Zhang, Zhongqi. Statistical analysis of gene expression profiles.

Degree: 2004, Iowa State University

URL: https://lib.dr.iastate.edu/rtd/1135

► Function divergence after gene duplication has been considered to be an important mechanism for the evolution of new functions. Although gene expression profiles have been…
(more)

Subjects/Keywords: Bioinformatics and computational biology; Statistics; Statistics and Probability

Record Details Similar Records

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

APA (6^{th} Edition):

Zhang, Z. (2004). Statistical analysis of gene expression profiles. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/rtd/1135

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhang, Zhongqi. “Statistical analysis of gene expression profiles.” 2004. Thesis, Iowa State University. Accessed October 25, 2020. https://lib.dr.iastate.edu/rtd/1135.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhang, Zhongqi. “Statistical analysis of gene expression profiles.” 2004. Web. 25 Oct 2020.

Vancouver:

Zhang Z. Statistical analysis of gene expression profiles. [Internet] [Thesis]. Iowa State University; 2004. [cited 2020 Oct 25]. Available from: https://lib.dr.iastate.edu/rtd/1135.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang Z. Statistical analysis of gene expression profiles. [Thesis]. Iowa State University; 2004. Available from: https://lib.dr.iastate.edu/rtd/1135

Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

28. Recknor, Justin C. New methods for designing and analyzing microarray experiments for the detection of differential expression.

Degree: 2006, Iowa State University

URL: https://lib.dr.iastate.edu/rtd/3049

► This thesis is divided into three sections all pertaining to microarray experimental design and analysis. Microarrays are a tool used in biological research which enables…
(more)

Subjects/Keywords: Statistics; Bioinformatics and computational biology; Bioinformatics; Statistics and Probability

Record Details Similar Records

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APA (6^{th} Edition):

Recknor, J. C. (2006). New methods for designing and analyzing microarray experiments for the detection of differential expression. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/rtd/3049

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Recknor, Justin C. “New methods for designing and analyzing microarray experiments for the detection of differential expression.” 2006. Thesis, Iowa State University. Accessed October 25, 2020. https://lib.dr.iastate.edu/rtd/3049.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Recknor, Justin C. “New methods for designing and analyzing microarray experiments for the detection of differential expression.” 2006. Web. 25 Oct 2020.

Vancouver:

Recknor JC. New methods for designing and analyzing microarray experiments for the detection of differential expression. [Internet] [Thesis]. Iowa State University; 2006. [cited 2020 Oct 25]. Available from: https://lib.dr.iastate.edu/rtd/3049.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Recknor JC. New methods for designing and analyzing microarray experiments for the detection of differential expression. [Thesis]. Iowa State University; 2006. Available from: https://lib.dr.iastate.edu/rtd/3049

Not specified: Masters Thesis or Doctoral Dissertation

University of Kentucky

29.
Weyenberg, Grady S.
* STATISTICS* IN THE BILLERA-HOLMES-VOGTMANN TREESPACE.

Degree: 2015, University of Kentucky

URL: https://uknowledge.uky.edu/statistics_etds/12

► This dissertation is an effort to adapt two classical non-parametric statistical techniques, kernel density estimation (KDE) and principal components analysis (PCA), to the Billera-Holmes-Vogtmann (BHV)…
(more)

Subjects/Keywords: Phylogenetic trees; Non-parametric statistics; Outlier Detection; Kernel Density Estimation; Principal Components Analysis; Applied Statistics; Computational Biology; Statistical Methodology

Record Details Similar Records

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

APA (6^{th} Edition):

Weyenberg, G. S. (2015). STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/12

Chicago Manual of Style (16^{th} Edition):

Weyenberg, Grady S. “STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE.” 2015. Doctoral Dissertation, University of Kentucky. Accessed October 25, 2020. https://uknowledge.uky.edu/statistics_etds/12.

MLA Handbook (7^{th} Edition):

Weyenberg, Grady S. “STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE.” 2015. Web. 25 Oct 2020.

Vancouver:

Weyenberg GS. STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE. [Internet] [Doctoral dissertation]. University of Kentucky; 2015. [cited 2020 Oct 25]. Available from: https://uknowledge.uky.edu/statistics_etds/12.

Council of Science Editors:

Weyenberg GS. STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE. [Doctoral Dissertation]. University of Kentucky; 2015. Available from: https://uknowledge.uky.edu/statistics_etds/12

30. Bernholt, Thorsten. Effiziente Algorithmen und Komplexität in der robusten Statistik.

Degree: 2006, Technische Universität Dortmund

URL: http://hdl.handle.net/2003/23027

► Ein Ausgangspunkt der robusten Statistik ist, dass der Least-Squares-Schätzer zwar einfach zu berechnen ist, aber Probleme mit Ausreißern hat. Es genügt, dass ein Punkt des…
(more)

Subjects/Keywords: Algorithmen; Algorithmische Geometrie; algorithms; computational geometry; outlier; Robuste Statistik; robust statistics; time series; Zeitreihen; 004

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APA (6^{th} Edition):

Bernholt, T. (2006). Effiziente Algorithmen und Komplexität in der robusten Statistik. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/23027

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bernholt, Thorsten. “Effiziente Algorithmen und Komplexität in der robusten Statistik.” 2006. Thesis, Technische Universität Dortmund. Accessed October 25, 2020. http://hdl.handle.net/2003/23027.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bernholt, Thorsten. “Effiziente Algorithmen und Komplexität in der robusten Statistik.” 2006. Web. 25 Oct 2020.

Vancouver:

Bernholt T. Effiziente Algorithmen und Komplexität in der robusten Statistik. [Internet] [Thesis]. Technische Universität Dortmund; 2006. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2003/23027.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bernholt T. Effiziente Algorithmen und Komplexität in der robusten Statistik. [Thesis]. Technische Universität Dortmund; 2006. Available from: http://hdl.handle.net/2003/23027

Not specified: Masters Thesis or Doctoral Dissertation