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You searched for subject:(computational statistics). Showing records 1 – 30 of 183 total matches.

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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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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


Rice University

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

Degree: PhD, Engineering, 2018, Rice University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

  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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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/

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):

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/.

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

MLA Handbook (7th 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/.

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

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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th Edition):

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

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):

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.

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

MLA Handbook (7th 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.

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

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

[1] [2] [3] [4] [5] [6] [7]

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