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You searched for subject:(Bayesian quantile regression). Showing records 1 – 16 of 16 total matches.

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University of Texas – Austin

1. Xu, Jing, M.S. in Statistics. Predict house prices using quantile regression.

Degree: Statistics, 2018, University of Texas – Austin

Quantile Regression Model (QRM), introduced by Koenker and Bassett in 1978, is a well-established and widely used technique in theoretical and applied statistics. QRM is… (more)

Subjects/Keywords: Quantile regression; Variable selection; Bayesian Quantile Regression; Quantile regression forest

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APA (6th Edition):

Xu, Jing, M. S. i. S. (2018). Predict house prices using quantile regression. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67630

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

Chicago Manual of Style (16th Edition):

Xu, Jing, M S in Statistics. “Predict house prices using quantile regression.” 2018. Thesis, University of Texas – Austin. Accessed August 23, 2019. http://hdl.handle.net/2152/67630.

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

MLA Handbook (7th Edition):

Xu, Jing, M S in Statistics. “Predict house prices using quantile regression.” 2018. Web. 23 Aug 2019.

Vancouver:

Xu, Jing MSiS. Predict house prices using quantile regression. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/2152/67630.

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

Council of Science Editors:

Xu, Jing MSiS. Predict house prices using quantile regression. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67630

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


Brunel University

2. Bin Muhd Noor, Nik Nooruhafidzi. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.

Degree: PhD, 2017, Brunel University

 Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the… (more)

Subjects/Keywords: Quantile regression; Bayesian quantile regression; Logistic quantile regression; Logistic regression; Pipeline coating defect size estimation

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APA (6th Edition):

Bin Muhd Noor, N. N. (2017). Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926

Chicago Manual of Style (16th Edition):

Bin Muhd Noor, Nik Nooruhafidzi. “Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.” 2017. Doctoral Dissertation, Brunel University. Accessed August 23, 2019. http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926.

MLA Handbook (7th Edition):

Bin Muhd Noor, Nik Nooruhafidzi. “Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines.” 2017. Web. 23 Aug 2019.

Vancouver:

Bin Muhd Noor NN. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2019 Aug 23]. Available from: http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926.

Council of Science Editors:

Bin Muhd Noor NN. Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines. [Doctoral Dissertation]. Brunel University; 2017. Available from: http://bura.brunel.ac.uk/handle/2438/16392 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764926


Baylor University

3. [No author]. Applications of Bayesian quantile regression and sample size determination.

Degree: 2018, Baylor University

Bayesian statistical methods reverse the philosophy of traditional statistical practice by treating parameters as random, rather than fixed. In so doing, Bayesian methods are able… (more)

Subjects/Keywords: Bayesian methods. Quantile regression. Sample size determination.

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APA (6th Edition):

author], [. (2018). Applications of Bayesian quantile regression and sample size determination. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10372

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):

author], [No. “Applications of Bayesian quantile regression and sample size determination. ” 2018. Thesis, Baylor University. Accessed August 23, 2019. http://hdl.handle.net/2104/10372.

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

MLA Handbook (7th Edition):

author], [No. “Applications of Bayesian quantile regression and sample size determination. ” 2018. Web. 23 Aug 2019.

Vancouver:

author] [. Applications of Bayesian quantile regression and sample size determination. [Internet] [Thesis]. Baylor University; 2018. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/2104/10372.

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

Council of Science Editors:

author] [. Applications of Bayesian quantile regression and sample size determination. [Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10372

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


University of Sydney

4. Dong, Xiaodan. Bayesian Analysis of Reserving Models and Applications .

Degree: 2014, University of Sydney

 This thesis focuses on developing models for loss reserving in insurance applications. In the first chapter, a Bayesian approach is presented in order to model… (more)

Subjects/Keywords: Bayesian; loss reserving; GB2; Quantile regression; copula

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APA (6th Edition):

Dong, X. (2014). Bayesian Analysis of Reserving Models and Applications . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/13435

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):

Dong, Xiaodan. “Bayesian Analysis of Reserving Models and Applications .” 2014. Thesis, University of Sydney. Accessed August 23, 2019. http://hdl.handle.net/2123/13435.

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

MLA Handbook (7th Edition):

Dong, Xiaodan. “Bayesian Analysis of Reserving Models and Applications .” 2014. Web. 23 Aug 2019.

Vancouver:

Dong X. Bayesian Analysis of Reserving Models and Applications . [Internet] [Thesis]. University of Sydney; 2014. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/2123/13435.

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

Council of Science Editors:

Dong X. Bayesian Analysis of Reserving Models and Applications . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/13435

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


University of New South Wales

5. Rodrigues, Thais Carvalho Valadares. Pyramid Quantile Regression.

Degree: Mathematics & Statistics, 2017, University of New South Wales

Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile(more)

Subjects/Keywords: Crossing quantile regression; Extremal quantile regression; Gaussian process regression; Monotonicity; Nonparametric quantile regression; O'Sullivan penalised splines; Simultaneous quantile regression; O’Sullivan penalised splines , Simultaneous quantile regression; Asymmetric Laplace distribution , Bayesian quantile pyramid , B-Splines , Crossing quantile regression; Extremal quantile regression , Gaussian process regression , Monotonicity , Nonparametric quantile regression

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

APA (6th Edition):

Rodrigues, T. C. V. (2017). Pyramid Quantile Regression. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Rodrigues, Thais Carvalho Valadares. “Pyramid Quantile Regression.” 2017. Doctoral Dissertation, University of New South Wales. Accessed August 23, 2019. http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true.

MLA Handbook (7th Edition):

Rodrigues, Thais Carvalho Valadares. “Pyramid Quantile Regression.” 2017. Web. 23 Aug 2019.

Vancouver:

Rodrigues TCV. Pyramid Quantile Regression. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Aug 23]. Available from: http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true.

Council of Science Editors:

Rodrigues TCV. Pyramid Quantile Regression. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58446 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46045/SOURCE02?view=true


University of South Carolina

6. Kindo, Bereket P. Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression.

Degree: PhD, Statistics, 2016, University of South Carolina

  This dissertation proposes multinomial probit Bayesian additive regression trees (MPBART), ordered multiclass Bayesian additive classification trees (O-MBACT) and Bayesian quantile additive regression trees (BayesQArt)… (more)

Subjects/Keywords: Physical Sciences and Mathematics; Statistics and Probability; Bayesian Ensemble; Regression Trees; Multinomial Probit; Quantile Regression

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APA (6th Edition):

Kindo, B. P. (2016). Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/3916

Chicago Manual of Style (16th Edition):

Kindo, Bereket P. “Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression.” 2016. Doctoral Dissertation, University of South Carolina. Accessed August 23, 2019. https://scholarcommons.sc.edu/etd/3916.

MLA Handbook (7th Edition):

Kindo, Bereket P. “Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression.” 2016. Web. 23 Aug 2019.

Vancouver:

Kindo BP. Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression. [Internet] [Doctoral dissertation]. University of South Carolina; 2016. [cited 2019 Aug 23]. Available from: https://scholarcommons.sc.edu/etd/3916.

Council of Science Editors:

Kindo BP. Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression. [Doctoral Dissertation]. University of South Carolina; 2016. Available from: https://scholarcommons.sc.edu/etd/3916


University of Alberta

7. Hassan, Imran. Hierarchical Quantile Regression.

Degree: MS, Department of Mathematical and Statistical Sciences, 2014, University of Alberta

Quantile regression supplements the ordinary least squares regression and provides a complete view of a relationship between a response variable and a set of covariates.… (more)

Subjects/Keywords: quantile regression; Markov Chain Monte Carlo; asymmetric Laplace distribution; data cloning; Bayesian statistics; hierarchical models

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APA (6th Edition):

Hassan, I. (2014). Hierarchical Quantile Regression. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/m326m3245

Chicago Manual of Style (16th Edition):

Hassan, Imran. “Hierarchical Quantile Regression.” 2014. Masters Thesis, University of Alberta. Accessed August 23, 2019. https://era.library.ualberta.ca/files/m326m3245.

MLA Handbook (7th Edition):

Hassan, Imran. “Hierarchical Quantile Regression.” 2014. Web. 23 Aug 2019.

Vancouver:

Hassan I. Hierarchical Quantile Regression. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2019 Aug 23]. Available from: https://era.library.ualberta.ca/files/m326m3245.

Council of Science Editors:

Hassan I. Hierarchical Quantile Regression. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/m326m3245


Clemson University

8. Tu, Shiyi. Objective Bayesian analysis on the quantile regression.

Degree: PhD, Mathematical Science, 2015, Clemson University

 The dissertation consists of two distinct but related research projects. First of all, we study the Bayesian analysis on the two-piece location-scale models, which contain… (more)

Subjects/Keywords: asymmetric Laplace distribution; Bayesian analysis; Objective priors; quantile regression; Shannon's entropy; Statistics and Probability

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APA (6th Edition):

Tu, S. (2015). Objective Bayesian analysis on the quantile regression. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1544

Chicago Manual of Style (16th Edition):

Tu, Shiyi. “Objective Bayesian analysis on the quantile regression.” 2015. Doctoral Dissertation, Clemson University. Accessed August 23, 2019. https://tigerprints.clemson.edu/all_dissertations/1544.

MLA Handbook (7th Edition):

Tu, Shiyi. “Objective Bayesian analysis on the quantile regression.” 2015. Web. 23 Aug 2019.

Vancouver:

Tu S. Objective Bayesian analysis on the quantile regression. [Internet] [Doctoral dissertation]. Clemson University; 2015. [cited 2019 Aug 23]. Available from: https://tigerprints.clemson.edu/all_dissertations/1544.

Council of Science Editors:

Tu S. Objective Bayesian analysis on the quantile regression. [Doctoral Dissertation]. Clemson University; 2015. Available from: https://tigerprints.clemson.edu/all_dissertations/1544


University of Southern California

9. Chang, Chih-Chieh. Bayesian multilevel quantile regression for longitudinal data.

Degree: PhD, Biostatistics, 2015, University of Southern California

 Conventional mixed effects regression focuses only on effects on the conditional mean, which may be inappropriate when the interest is in testing the effects on… (more)

Subjects/Keywords: Bayesian; multilevel modeling; mixed-effect modeling; quantile regression; longitudinal data; childhood obesity

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APA (6th Edition):

Chang, C. (2015). Bayesian multilevel quantile regression for longitudinal data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043

Chicago Manual of Style (16th Edition):

Chang, Chih-Chieh. “Bayesian multilevel quantile regression for longitudinal data.” 2015. Doctoral Dissertation, University of Southern California. Accessed August 23, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043.

MLA Handbook (7th Edition):

Chang, Chih-Chieh. “Bayesian multilevel quantile regression for longitudinal data.” 2015. Web. 23 Aug 2019.

Vancouver:

Chang C. Bayesian multilevel quantile regression for longitudinal data. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2019 Aug 23]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043.

Council of Science Editors:

Chang C. Bayesian multilevel quantile regression for longitudinal data. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/532589/rec/1043

10. Chang, Chao. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.

Degree: PhD, Mathematics, 2015, Washington University in St. Louis

  We propose new nonparametric Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models. All the existing quantile regression methods based on DPMs… (more)

Subjects/Keywords: Dirichlet Process Mixture, Nonparametric Bayesian, Posterior Consistency, Quantile Regression; Mathematics

…My family. viii ABSTRACT OF THE DISSERTATION Nonparametric Bayesian Quantile Regression… …Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models… …the idea of Bayesian quantile regression using Dirichlet process mixture models where our… …Bayesian quantile regression method was proposed in [118]. This parametric approach… …there are other branches of nonparametric Bayesian techniques for quantile regression. [… 

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APA (6th Edition):

Chang, C. (2015). Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/art_sci_etds/458

Chicago Manual of Style (16th Edition):

Chang, Chao. “Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.” 2015. Doctoral Dissertation, Washington University in St. Louis. Accessed August 23, 2019. https://openscholarship.wustl.edu/art_sci_etds/458.

MLA Handbook (7th Edition):

Chang, Chao. “Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.” 2015. Web. 23 Aug 2019.

Vancouver:

Chang C. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2015. [cited 2019 Aug 23]. Available from: https://openscholarship.wustl.edu/art_sci_etds/458.

Council of Science Editors:

Chang C. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. [Doctoral Dissertation]. Washington University in St. Louis; 2015. Available from: https://openscholarship.wustl.edu/art_sci_etds/458


University of South Florida

11. Zhang, Hanze. Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies.

Degree: 2017, University of South Florida

 In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection,… (more)

Subjects/Keywords: HIV longitudinal-survival data; quantile regression; nonlinear mixed-effects joint models; partially linear mixed-effects models; Bayesian inference; below detection; measurement error; skewed distributions; Biostatistics

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APA (6th Edition):

Zhang, H. (2017). Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7456

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, Hanze. “Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies.” 2017. Thesis, University of South Florida. Accessed August 23, 2019. https://scholarcommons.usf.edu/etd/7456.

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

MLA Handbook (7th Edition):

Zhang, Hanze. “Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies.” 2017. Web. 23 Aug 2019.

Vancouver:

Zhang H. Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies. [Internet] [Thesis]. University of South Florida; 2017. [cited 2019 Aug 23]. Available from: https://scholarcommons.usf.edu/etd/7456.

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

Council of Science Editors:

Zhang H. Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies. [Thesis]. University of South Florida; 2017. Available from: https://scholarcommons.usf.edu/etd/7456

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

12. Hashem, Hussein Abdulahman. Regularized and robust regression methods for high dimensional data.

Degree: PhD, 2014, Brunel University

 Recently, variable selection in high-dimensional data has attracted much research interest. Classical stepwise subset selection methods are widely used in practice, but when the number… (more)

Subjects/Keywords: 519.5; Group lasso; Quantile regression; Binary regression; Tobit regression; Bayesian regression

Bayesian regularized quantile regression methods with classical methods on simulated data. 2.4.1… …Comparison of Bayesian quantile regression methods with frequentist methods, for low (left… …over 40 replications) of Bayesian binary quantile regression with group lasso (… …Bayesian tobit quantile regression with group lasso ( and Bayesian tobit quantile regression… …regression coefficients for Bayesian tobit quantile regression with group lasso ( and Bayesian… 

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APA (6th Edition):

Hashem, H. A. (2014). Regularized and robust regression methods for high dimensional data. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/9197 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629960

Chicago Manual of Style (16th Edition):

Hashem, Hussein Abdulahman. “Regularized and robust regression methods for high dimensional data.” 2014. Doctoral Dissertation, Brunel University. Accessed August 23, 2019. http://bura.brunel.ac.uk/handle/2438/9197 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629960.

MLA Handbook (7th Edition):

Hashem, Hussein Abdulahman. “Regularized and robust regression methods for high dimensional data.” 2014. Web. 23 Aug 2019.

Vancouver:

Hashem HA. Regularized and robust regression methods for high dimensional data. [Internet] [Doctoral dissertation]. Brunel University; 2014. [cited 2019 Aug 23]. Available from: http://bura.brunel.ac.uk/handle/2438/9197 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629960.

Council of Science Editors:

Hashem HA. Regularized and robust regression methods for high dimensional data. [Doctoral Dissertation]. Brunel University; 2014. Available from: http://bura.brunel.ac.uk/handle/2438/9197 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629960

13. Aristodemou, Katerina. New regression methods for measures of central tendency.

Degree: PhD, 2014, Brunel University

 Measures of central tendency have been widely used for summarising statistical data, with the mean being the most popular summary statistic. However, in reallife applications… (more)

Subjects/Keywords: 519.5; Mode regression; Bayesian inference; Big data; Gamma distribution; Binary quantile regression

…Mean Regression . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.0.2 Quantile… …Quantile Regression . . . . . . . . . . . . . . . . . . . 8 1.2 Contributions… …Bayesian Mode Regression 13 2.1 Introduction… …13 2.2 Bayesian Mode Regression . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1… …Mode Estimation and Classical Mode Regression . . . . . . . 18 2.2.2 Bayesian Inference… 

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APA (6th Edition):

Aristodemou, K. (2014). New regression methods for measures of central tendency. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/9268 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629975

Chicago Manual of Style (16th Edition):

Aristodemou, Katerina. “New regression methods for measures of central tendency.” 2014. Doctoral Dissertation, Brunel University. Accessed August 23, 2019. http://bura.brunel.ac.uk/handle/2438/9268 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629975.

MLA Handbook (7th Edition):

Aristodemou, Katerina. “New regression methods for measures of central tendency.” 2014. Web. 23 Aug 2019.

Vancouver:

Aristodemou K. New regression methods for measures of central tendency. [Internet] [Doctoral dissertation]. Brunel University; 2014. [cited 2019 Aug 23]. Available from: http://bura.brunel.ac.uk/handle/2438/9268 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629975.

Council of Science Editors:

Aristodemou K. New regression methods for measures of central tendency. [Doctoral Dissertation]. Brunel University; 2014. Available from: http://bura.brunel.ac.uk/handle/2438/9268 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629975

14. Tong, Xin. Bayesian Semiparametric Quantile Regression for Clustered Data.

Degree: PhD, Health Promotion, Education and Behavior, 2016, University of South Carolina

  Traditional frequentist quantile regression makes few assumptions on the form of the error distribution and thus is able to accommodate non-normal errors. However, inference… (more)

Subjects/Keywords: Medicine and Health Sciences; Other Public Health; Public Health; Bayesian; semiparametric; quantile regression; clustered data

…provides a natural way to deal with the Bayesian quantile regression [Yu and Moyeed, 2001… …Bayesian quantile regression model for clustered interval-censored data with parametric error… …some recommendations on how to proceed in this Bayesian nonparametric quantile regression… …quantile regression. Yu and Moyeed [2001] proposed a Bayesian approximate 8… …Chapter 2 A Bayesian Nonparametric Quantile Regression for Repeated Measures with a Flexible… 

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APA (6th Edition):

Tong, X. (2016). Bayesian Semiparametric Quantile Regression for Clustered Data. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/3364

Chicago Manual of Style (16th Edition):

Tong, Xin. “Bayesian Semiparametric Quantile Regression for Clustered Data.” 2016. Doctoral Dissertation, University of South Carolina. Accessed August 23, 2019. https://scholarcommons.sc.edu/etd/3364.

MLA Handbook (7th Edition):

Tong, Xin. “Bayesian Semiparametric Quantile Regression for Clustered Data.” 2016. Web. 23 Aug 2019.

Vancouver:

Tong X. Bayesian Semiparametric Quantile Regression for Clustered Data. [Internet] [Doctoral dissertation]. University of South Carolina; 2016. [cited 2019 Aug 23]. Available from: https://scholarcommons.sc.edu/etd/3364.

Council of Science Editors:

Tong X. Bayesian Semiparametric Quantile Regression for Clustered Data. [Doctoral Dissertation]. University of South Carolina; 2016. Available from: https://scholarcommons.sc.edu/etd/3364

15. Feng, Yang. Bayesian quantile linear regression.

Degree: PhD, 0329, 2011, University of Illinois – Urbana-Champaign

Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is… (more)

Subjects/Keywords: Bayesian inference; Markov chain Monte Carlo (MCMC); Quantile regression; Linearly interpolated density (LID)

…the alternative method for the Bayesian quantile regression problem. 2.3.1 MCMC without… …quntile regression. 1 1.1 Introduction of quantile regression As early as 1755, Boscovich… …x28;1, 1, ..., 1)′ . The above quantile regression n problem is equivalent to a linear… …problem. can obtain β(τ 1.2 1.2.1 Inference for quantile regression Inference based on… …joint asymptotic distribution of the m quantile regression estimators ζˆn = (βˆn (τ1… 

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

APA (6th Edition):

Feng, Y. (2011). Bayesian quantile linear regression. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24348

Chicago Manual of Style (16th Edition):

Feng, Yang. “Bayesian quantile linear regression.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 23, 2019. http://hdl.handle.net/2142/24348.

MLA Handbook (7th Edition):

Feng, Yang. “Bayesian quantile linear regression.” 2011. Web. 23 Aug 2019.

Vancouver:

Feng Y. Bayesian quantile linear regression. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Aug 23]. Available from: http://hdl.handle.net/2142/24348.

Council of Science Editors:

Feng Y. Bayesian quantile linear regression. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24348

16. Lenormand, Maxime. Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model.

Degree: Docteur es, Informatique, 2012, Université Blaise-Pascale, Clermont-Ferrand II

Le but de cette thèse est de développer des outils statistiques permettant d'initialiser et de calibrer les modèles de microsimulation dynamique stochastique, en partant de… (more)

Subjects/Keywords: Microsimulation; Modèle Complexe; Modèle Individus Centré; Modèle Stochastique; Calibration; Initialisation; Population Synthétique; Iterative Proportional Updating; Modèle de Réseaux de Navettage; Modèle de Déplacement; Loi de Gravité; Mobilité Humaine; Réseau Spatial; Besoin Minimal; Service de Proximité; Régression Quantile; Municipalité Rurale; Calcul Bayésien Approché; Population Monte Carlo; Sequential Monte Carlo; Microsimulation; Complex Model; Individual Based Models; Stochastic Models; Calibration; Initialisation; Synthetic Population; Sample-Free; Iterative Proportional Updating; Network Generation Models; Commuting Patterns; Commuting Networks; Gravity Law; Human Mobility; Spatial Networks; Minimum Requirement; Proximity Service Jobs; Quantile Regression; Rural Municipality; Approximate Bayesian Computation; Population Monte Carlo; Sequential Monte Carlo

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lenormand, M. (2012). Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2012CLF22315

Chicago Manual of Style (16th Edition):

Lenormand, Maxime. “Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model.” 2012. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed August 23, 2019. http://www.theses.fr/2012CLF22315.

MLA Handbook (7th Edition):

Lenormand, Maxime. “Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model.” 2012. Web. 23 Aug 2019.

Vancouver:

Lenormand M. Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2012. [cited 2019 Aug 23]. Available from: http://www.theses.fr/2012CLF22315.

Council of Science Editors:

Lenormand M. Initialiser et calibrer un modèle de microsimulation dynamique stochastique : application au modèle SimVillages : Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : application to the SimVillages Model. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2012. Available from: http://www.theses.fr/2012CLF22315

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