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book.bib
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%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for 김영근 at 2019-06-22 14:04:23 +0900
%% Saved with string encoding Unicode (UTF-8)
@book{Gelman:2013aa,
Author = {Andrew Gelman and John B. Carlin and Hal S. Stern and David B. Dunson and Aki Vehtari and Donald B. Rubin},
Date-Added = {2019-06-22 13:57:54 +0900},
Date-Modified = {2019-06-22 13:57:54 +0900},
Edition = {3},
Isbn = {1439840954},
Month = {Nov},
Publisher = {CRC Press},
Title = {Bayesian Data Analysis},
Year = {2013}}
@book{gimdalw:2013aa,
Author = {Dal Ho Kim},
Date-Added = {2019-06-22 13:57:54 +0900},
Date-Modified = {2019-06-22 14:04:19 +0900},
Edition = {2},
Isbn = {8973381628},
Month = {Aug},
Title = {Bayesian Statistics (using R and WinBUGS)},
Year = {2013}}
@book{Wickham:2019aa,
Author = {Hadley Wickham},
Date-Added = {2019-06-20 00:53:55 +0900},
Date-Modified = {2019-06-20 00:53:55 +0900},
Isbn = {1351201298},
Month = {Jun},
Publisher = {CRC Press},
Title = {Advanced R, Second Edition},
Year = {2019}}
@book{Efron:1994aa,
Author = {Bradley Efron and R.J. Tibshirani},
Date-Added = {2019-05-19 13:47:37 +0900},
Date-Modified = {2019-05-19 13:47:37 +0900},
Isbn = {0412042312},
Month = {May},
Publisher = {CRC Press},
Title = {An Introduction to the Bootstrap},
Year = {1994}}
@article{Bilmes:1998tg,
Abstract = {We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.},
Author = {Bilmes, Jeff A.},
Date-Added = {2019-05-18 14:27:57 +0900},
Date-Modified = {2019-05-18 14:27:57 +0900},
File = {{16CDE4D6-C4AF-4651-90CF-C881023139C9.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/16/16CDE4D6-C4AF-4651-90CF-C881023139C9.pdf:application/pdf}},
Journal = {International Computer Science Institute},
Keywords = {clustering},
Local-Url = {file://localhost/Users/younggeun/Dropbox/Papers/Library.papers3/Files/16/16CDE4D6-C4AF-4651-90CF-C881023139C9.pdf},
Month = apr,
Number = {4},
Pages = {126},
Rating = {0},
Read = {Yes},
Title = {{A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models}},
Uri = {\url{papers3://publication/uuid/F861E45F-27B5-4DEB-A2F9-CAD3BA116E95}},
Url = {http://www.ee.iisc.ac.in/new/people/faculty/prasantg/downloads/GP-GMM.pdf},
Year = {1998},
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@article{Chib:1995de,
Abstract = {We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementing the algorithm with block-at-a-time scans. In the latter situation, many different algorithms, including the Gibbs sampler, are shown to be special cases of the Metropolis-Hastings algorithm. The methods are illustrated with examples.},
Author = {Chib, Siddhartha and Greenberg, Edward},
Date-Added = {2019-05-18 14:27:57 +0900},
Date-Modified = {2019-05-18 14:27:57 +0900},
Doi = {10.1080/00031305.1995.10476177},
File = {{900141DF-037C-485F-9184-64B538398D1F.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/90/900141DF-037C-485F-9184-64B538398D1F.pdf:application/pdf;900141DF-037C-485F-9184-64B538398D1F.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/90/900141DF-037C-485F-9184-64B538398D1F.pdf:application/pdf}},
Journal = {The American Statistician},
Language = {English},
Local-Url = {file://localhost/Users/younggeun/Dropbox/Papers/Library.papers3/Files/90/900141DF-037C-485F-9184-64B538398D1F.pdf},
Month = nov,
Number = {4},
Pages = {327--335},
Rating = {0},
Title = {{Understanding the metropolis-hastings algorithm}},
Uri = {\url{papers3://publication/doi/10.1080/00031305.1995.10476177}},
Url = {http://www.tandfonline.com/doi/abs/10.1080/00031305.1995.10476177},
Volume = {49},
Year = {1995},
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Bdsk-Url-2 = {https://doi.org/10.1080/00031305.1995.10476177}}
@article{Efron:1983bw,
Abstract = {This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.},
Author = {Efron, Bradley and Gong, Gail},
Date-Added = {2019-05-18 14:27:57 +0900},
Date-Modified = {2019-05-18 14:27:57 +0900},
Doi = {10.2307/2685844?refreqid=search-gateway:8506a3ddcbc35f60726e7c34838cb5cd},
File = {{E64BD318-AC6C-4276-9710-3BBC28CB9540.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/E6/E64BD318-AC6C-4276-9710-3BBC28CB9540.pdf:application/pdf;E64BD318-AC6C-4276-9710-3BBC28CB9540.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/E6/E64BD318-AC6C-4276-9710-3BBC28CB9540.pdf:application/pdf}},
Journal = {The American Statistician},
Language = {English},
Local-Url = {file://localhost/Users/younggeun/Dropbox/Papers/Library.papers3/Files/E6/E64BD318-AC6C-4276-9710-3BBC28CB9540.pdf},
Month = feb,
Number = {1},
Pages = {36--48},
Publisher = {American Statistical Association},
Rating = {0},
Read = {Yes},
Title = {{A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation}},
Uri = {\url{papers3://publication/doi/10.2307/2685844?refreqid=search-gateway:8506a3ddcbc35f60726e7c34838cb5cd}},
Volume = {37},
Year = {1983},
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Bdsk-Url-1 = {https://doi.org/10.2307/2685844?refreqid=search-gateway:8506a3ddcbc35f60726e7c34838cb5cd}}
@article{McGrath:2005kx,
Abstract = {The common "F test" for testing the equality of two population variances is very sensitive to the normality assumption, yet many introductory statistics books do not stress this sensitivity. Several alternative dispersion tests have been developed that do not assume normality, but these are rarely discussed other than in nonparametric statistics texts. Additionally, these tests generally require tables of critical values or software to calculate p values. We propose a simple graphical procedure, requiring no tables or software, that compares absolute deviations of one sample to another. With equal sample sizes, if one sample has the five largest absolute deviations. we conclude that its population has,greater dispersion. We believe the simplicity of this test makes it a viable alternative to the often misleading F test and to other tests.},
Author = {McGrath, Richard N and Yeh, Arthur B},
Date-Added = {2019-04-20 22:28:56 +0900},
Date-Modified = {2019-04-20 22:28:56 +0900},
Doi = {10.1198/000313005X22248},
File = {{4BC2AEFE-DEEF-47C7-897E-1892442F14BC.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/4B/4BC2AEFE-DEEF-47C7-897E-1892442F14BC.pdf:application/pdf;4BC2AEFE-DEEF-47C7-897E-1892442F14BC.pdf:/Users/younggeun/Dropbox/Papers/Library.papers3/Files/4B/4BC2AEFE-DEEF-47C7-897E-1892442F14BC.pdf:application/pdf}},
Journal = {The American Statistician},
Language = {English},
Local-Url = {file://localhost/Users/younggeun/Dropbox/Papers/Library.papers3/Files/4B/4BC2AEFE-DEEF-47C7-897E-1892442F14BC.pdf},
Month = feb,
Number = {1},
Pages = {47--53},
Rating = {0},
Read = {Yes},
Title = {{A Quick, Compact, Two-Sample Dispersion Test}},
Uri = {\url{papers3://publication/doi/10.1198/000313005X22248}},
Url = {http://www.tandfonline.com/doi/abs/10.1198/000313005X22248},
Volume = {59},
Year = {2005},
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@book{Rizzo:2007aa,
Author = {Maria L. Rizzo},
Date-Added = {2019-03-03 15:59:28 +0900},
Date-Modified = {2019-03-03 16:00:11 +0900},
Isbn = {1584885459},
Month = {Nov},
Publisher = {Chapman and Hall/CRC},
Title = {Statistical Computing with R},
Year = {2007}}
@book{xie2015,
Address = {Boca Raton, Florida},
Author = {Yihui Xie},
Edition = {2nd},
Note = {ISBN 978-1498716963},
Publisher = {Chapman and Hall/CRC},
Title = {Dynamic Documents with {R} and knitr},
Url = {http://yihui.name/knitr/},
Year = {2015},
Bdsk-Url-1 = {http://yihui.name/knitr/}}