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book.bib
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@article{Davis1959,
doi = {10.2307/2309786},
year = {1959},
volume = {66},
number = {10},
pages = {849--869},
author = {Philip J. Davis},
title = {Leonhard Euler's Integral: A Historical Profile of the Gamma Function: In Memoriam: Milton Abramowitz},
journal = {The American Mathematical Monthly}
}
@Article{fivethirtyeight2018,
author = {Albert Y. Kim and Chester Ismay and Jennifer Chunn},
title = {The {fivethirtyeight} R Package: 'Tame Data' Principles for
Introductory Statistics and Data Science Courses},
journal = {Technology Innovations in Statistics Education},
year = {2018},
volume = {11},
issue = {1},
url = {https://escholarship.org/uc/item/0rx1231m},
}
@Book{xie2016,
title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2016},
note = {ISBN 978-1138700109},
url = {https://bookdown.org/yihui/bookdown},
}
@Book{xie2020,
title = {{R} Markdown Cookbook},
author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2020},
note = {ISBN 9780367563837},
url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}
@Book{xie2018,
title = {{R} Markdown: The Definitive Guide},
author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2018},
note = {ISBN 9781138359338},
url = {https://bookdown.org/yihui/rmarkdown},
}
@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {https://yihui.org/knitr/},
}
@Book{Gelman2013,
title = {Bayesian Data Analysis},
author = {Andrew Gelman and John Carlin and Hal Stern and David Dunson and Aki Vehtari and Donald Rubin},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2013},
edition = {3rd},
note = {ISBN 978-1439840955},
url = {https://users.aalto.fi/~ave/BDA3.pdf},
}
@Book{Friendly2016,
title = {Discrete Data Analysis with {R}: Visualization and Modeling Techniques for Categorical and Count Data},
author = {Michael Friendly and David Meyer},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2016},
edition = {1st},
note = {ISBN 978-1498725835},
url = {http://ddar.datavis.ca/},
}
@Book{Friendly2000,
title = {Visualizing Categorical Data},
author = {Michael Friendly},
publisher = {SAS Institute Inc.},
address = {Carey, NC},
year = {2000},
edition = {1st},
note = {ISBN 1-58025-660-0},
url = {https://www.datavis.ca/papers/casm/casm.html},
}
@Book{Unwin2015,
title = {Graphical Data Analysis with {R}},
author = {Antony Unwin},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {1st},
note = {ISBN 978-1498715232},
url = {http://www.gradaanwr.net/},
}
@Book{Hadley2017,
title = {{R} for Data Science},
author = {Hadley Wickham and Garrett Grolemund},
publisher = {O'Reilly Media, Inc.},
address = {Sebastopol, California},
year = {2017},
edition = {1st},
note = {ISBN 978-1491910399},
url = {https://r4ds.had.co.nz/},
}
@Book{msg2021,
title = {现代统计图形},
author = {赵鹏 and 谢益辉 and 黄湘云},
publisher = {人民邮电出版社},
address = {北京},
year = {2021},
note = {ISBN 978-7-115-56690-4},
url = {https://bookdown.org/xiangyun/msg},
}
@Book{Chen2019,
title = {数据可视化},
author = {陈为 and 沈则潜 and 陶煜波},
publisher = {电子工业出版社},
address = {北京},
year = {2019},
edition = {第二版},
note = {ISBN 978-7-121-35727-5},
url = {https://zjuvag.org/},
}
@Book{Paul2018,
title = {{R} Graphics},
author = {Paul Murrell},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2018},
edition = {3rd},
note = {ISBN 978-0367780692},
url = {https://www.stat.auckland.ac.nz/~paul/RG3e/},
}
@Book{Oscar2018,
title = {Displaying Time Series, Spatial, and Space-Time Data with {R}},
author = {Oscar Perpinan Lamigueiro},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2018},
edition = {2nd},
note = {ISBN 978-1138089983},
url = {https://oscarperpinan.github.io/bookvis/},
}
@Book{Wilke2019,
title = {Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures},
author = {Claus O. Wilke},
publisher = {O'Reilly Media, Inc.},
address = {Sebastopol, California},
year = {2019},
note = {ISBN 978-1492031086},
url = {https://clauswilke.com/dataviz/},
}
@Book{Chang2018,
title = {{R} Graphics Cookbook: Practical Recipes for Visualizing Data},
author = {Winston Chang},
publisher = {O'Reilly Media, Inc.},
year = {2018},
address = {Sebastopol, California},
edition = {2nd},
note = {ISBN 978-1491978603},
url = {https://r-graphics.org/},
}
@Book{Healy2019,
title = {Data Visualization: A Practical Introduction},
author = {Kieran Healy},
publisher = {Princeton University Press},
address = {Princeton, New Jersey},
year = {2019},
note = {ISBN 978-0691181615},
url = {https://socviz.co/},
}
@Book{Sievert2020,
title = {Interactive web-based data visualization with {R}, {plotly}, and {shiny}},
author = {Carson Sievert},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2020},
edition = {1st},
note = {ISBN 978-1138331457},
url = {https://plotly-r.com/},
}
@Book{Rahlf2019,
author = {Thomas Rahlf},
title = {Data Visualisation with {R}},
publisher = {Springer Nature},
address = {New York, NY},
year = {2019},
edition = {2nd},
url = {https://www.datavisualisation-r.com/},
note = {ISBN 978-3-030-28443-5},
}
@Book{Unwin2006,
author = {Antony Unwin, Martin Theus, Heike Hofmann},
title = {Graphics of Large Datasets: Visualizing a Million},
publisher = {Springer-Verlag},
address = {New York, NY},
year = {2006},
note = {ISBN 978-0387-32906-2},
}
@Book{Chen2008,
author = {Chun-houh Chen and Wolfgang H{\"a}rdle and Antony Unwin},
title = {Handbook of Data Visualization},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
year = {2008},
note = {ISBN 978-3-540-33036-3},
}
@Book{Wickham2016,
author = {Hadley Wickham},
title = {{ggplot2}: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag},
address = {New York},
edition = {2nd},
year = {2016},
note = {ISBN 978-3319242774},
url = {https://ggplot2-book.org/},
}
@Book{Sarkar2008,
title = {{lattice}: Multivariate Data Visualization with {R}},
author = {Deepayan Sarkar},
publisher = {Springer},
address = {New York},
year = {2008},
note = {ISBN 978-0-387-75968-5},
url = {http://lmdvr.r-forge.r-project.org},
}
@Book{KernSmooth1995,
title = {Kernel Smoothing},
author = {M. P. Wand and M. C. Jones},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {1995},
edition = {1st},
note = {ISBN 978-0412552700},
url = {http://matt-wand.utsacademics.info/webWJbook/},
}
@Article{Amrhein2019,
title = {Retire statistical significance},
author = {Amrhein, V. and Greenland, S. and McShane, B.},
journal = {Nature},
year = {2019},
volumne = {567},
pages = {305–307},
doi = {10.1038/d41586-019-00857-9},
}
@article{Wasserstein2019,
author = {Ronald L. Wasserstein and Allen L. Schirm and Nicole A. Lazar},
title = {Moving to a World Beyond $p<0.05$},
journal = {The American Statistician},
volume = {73},
number = {sup1},
pages = {1-19},
year = {2019},
doi = {10.1080/00031305.2019.1583913},
}
@Article{Lawrence2001,
title = {Interval Estimation for a Binomial Proportion},
author = {Lawrence D. Brown and T. Tony Cai and Anirban DasGupta},
journal = {Statistical Science},
year = {2001},
volumne = {16},
number = {2},
pages = {101--133},
url = {https://projecteuclid.org/euclid.ss/1009213286},
}
@Article{Rigby2005,
title = {Generalized additive models for location, scale and shape},
author = {Robert A Rigby and D Mikis Stasinopoulos},
journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
year = {2005},
volumne = {54},
number = {3},
pages = {507--554},
doi = {10.1111/j.1467-9876.2005.00510.x},
}
@Manual{sm2021,
title = {{sm}: nonparametric smoothing methods},
author = {A. W. Bowman and A. Azzalini},
note = {R package version 2.2.5.6},
address = {University of Glasgow, UK and Universit\`a di Padova, Italia},
year = {2021},
url = {http://www.stats.gla.ac.uk/~adrian/sm/},
}
@Article{Azzalini1990,
title = {A look at some data on the Old Faithful geyser},
author = {Adelchi Azzalini and Adrian W Bowman},
journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
year = {1990},
volumne = {39},
number = {3},
pages = {357--365},
doi = {10.2307/2347385},
}
@Article{Wilson1927,
author = {Wilson, Edwin B.},
title = {Probable inference, the law of succession, and statistical inference},
journal = {Journal of the American Statistical Association},
volume = {22},
number = {158},
pages = {209-212},
year = {1927},
month = {6},
doi = {10.1080/01621459.1927.10502953},
}
@Article{Thrun2020,
doi = {10.1371/journal.pone.0238835},
year = {2020},
volume = {15},
number = {10},
pages = {e0238835},
author = {Michael C. Thrun and Tino Gehlert and Alfred Ultsch},
title = {Analyzing the fine structure of distributions},
journal = {{PLoS ONE}}
}
@Article{Clopper1934,
author = {Clopper, C. J. and Pearson, E. S.},
title = {The Use of Confidence or Fiducial Limits Illustrated In The Case of The Binomial},
journal = {Biometrika},
volume = {26},
number = {4},
pages = {404-413},
year = {1934},
month = {12},
doi = {10.1093/biomet/26.4.404},
}
@Article{Anscombe1973,
doi = {10.2307/2682899},
year = {1973},
volume = {27},
number = {1},
pages = {17},
author = {F. J. Anscombe},
title = {Graphs in Statistical Analysis},
journal = {The American Statistician}
}
@Article{plotrix2006,
year = {2006},
title = {{plotrix}: a package in the red light district of {R}},
journal = {R-News},
volume = {6},
number = {4},
pages = {8-12},
author = {Lemon Jim},
url = {https://www.r-project.org/doc/Rnews/Rnews_2006-4.pdf},
}
@Article{scatterplot3d2003,
doi = {10.18637/jss.v008.i11},
year = {2003},
publisher = {Foundation for Open Access Statistic},
volume = {8},
number = {11},
author = {Uwe Ligges and Martin M\"{a}chler},
title = {{scatterplot3d}: An {R} Package for Visualizing Multivariate Data},
journal = {Journal of Statistical Software}
}
@article{misc3d2008,
doi = {10.18637/jss.v028.i01},
year = {2008},
publisher = {Foundation for Open Access Statistic},
volume = {28},
number = {1},
author = {Dai Feng and Luke Tierney},
title = {Computing and Displaying Isosurfaces in {R}},
journal = {Journal of Statistical Software}
}
@Manual{plot3D2021,
title = {{plot3D}: Plotting Multi-Dimensional Data},
author = {Karline Soetaert},
year = {2021},
note = {R package version 1.4},
url = {https://CRAN.R-project.org/package=plot3D},
}
@Article{Geyer2005,
author = {Geyer, Charles J. and Meeden, Glen D.},
url = {https://www.jstor.org/stable/20061193},
journal = {Statistical Science},
month = {11},
number = {4},
pages = {358--366},
title = {Fuzzy and Randomized Confidence Intervals and P-Values},
volume = {20},
year = {2005}
}
@Article{Blyth1960,
URL = {https://www.jstor.org/stable/2333308},
author = {Colin R. Blyth and David W. Hutchinson},
journal = {Biometrika},
number = {3/4},
pages = {381--391},
title = {Table of Neyman-Shortest Unbiased Confidence Intervals for the Binomial Parameter},
volume = {47},
year = {1960}
}
@Book{Chen2016,
title = {统计学漫话},
author = {陈希孺 and 苏淳},
publisher = {中国科学技术大学出版社},
address = {合肥, 安徽},
year = {2016},
edition = {第二版},
note = {ISBN 978-7312037511},
}
@Book{Chen2000,
title = {数理统计学简史},
author = {陈希孺},
publisher = {湖南教育出版社},
address = {长沙},
year = {2000},
edition = {第一版},
note = {ISBN 978-7312037511},
}
@Book{Lin2014,
title = {Past, Present, and Future of Statistical Science},
year = {2014},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
editor = {Xihong Lin and Christian Genest and David L. Banks and Geert Molenberghs and David W. Scott and Jane-Ling Wang},
note = {ISBN 978-1482204964},
}
@Book{Johnson1997,
doi = {10.1002/9781118150719},
year = {1997},
publisher = {John Wiley {\&} Sons, Inc.},
editor = {Norman L. Johnson and Samuel Kotz},
title = {Leading Personalities in Statistical Sciences: From The Seventeenth Century to The Present},
}
@Book{Salsburg2001,
year = {2001},
note = {ISBN 0-7167-4106-7},
publisher = {W. H. Freeman and Company},
author = {David Salsburg},
address = {New York, NY},
title = {The Lady Tasting Tea: How Statistics Revolutionized Science In The Twentieth Century},
}
@Book{Heyde2001,
doi = {10.1007/978-1-4613-0179-0},
url = {https://doi.org/10.1007/978-1-4613-0179-0},
year = {2001},
publisher = {Springer New York},
address = {New York, NY},
editor = {C. C. Heyde and E. Seneta and P. Cr{\'{e}}pel and S. E. Fienberg and J. Gani},
title = {Statisticians of The Centuries},
}
@Article{Jeff1986,
title = {从历史发展看中国统计发展方向},
author = {吴建福},
journal = {数理统计与管理},
year = {1986},
volumne = {1},
doi = {10.13860/j.cnki.sltj.1986.01.001},
number = {1},
pages = {1--7},
}
@Article{Jeff2011,
title = {统计学者的工作及风范：灵感、抱负、雄心},
author = {吴建福},
journal = {应用概率统计},
year = {2011},
volumne = {27},
number = {2},
pages = {113--123},
}
@Book{Fang2016,
title = {漫漫修远攻算路---方开泰自述},
author = {方开泰},
publisher = {湖南教育出版社},
address = {长沙},
year = {2016},
edition = {第一版},
note = {ISBN 978-7553946832},
}
@Book{Xu2010,
title = {道德文章垂范人间：纪念许宝騄先生百年诞辰},
author = {许宝騄先生纪念文集编委会编},
publisher = {北京大学出版社},
address = {北京},
year = {2010},
edition = {第一版},
note = {ISBN 978-7301171899},
}
@Book{Reid1982,
title = {Neyman},
author = {Constance Reid},
year = {1982},
publisher = {Springer-Verlag},
address = {New York, NY},
isbn = {978-1-4612-5754-7},
doi = {10.1007/978-1-4612-5754-7},
}
@Book{Lehmann2011,
doi = {10.1007/978-1-4419-9500-1},
year = {2011},
publisher = {Springer New York},
author = {Erich L. Lehmann},
title = {Fisher, Neyman, and the Creation of Classical Statistics},
}
@article{Meyer06,
author = {David Meyer and Achim Zeileis and Kurt Hornik},
title = {The Strucplot Framework: Visualizing Multi-way Contingency Tables with {vcd}},
journal = {journal of Statistical Software},
volume = {17},
number = {3},
pages = {1--48},
day = {19},
month = {10},
year = {2006},
url = {https://www.jstatsoft.org/v17/i03},
}
@article{Benjamin2017,
doi = {10.1038/s41562-017-0189-z},
year = {2017},
month = sep,
publisher = {Springer Science and Business Media {LLC}},
volume = {2},
number = {1},
pages = {6--10},
author = {Daniel J. Benjamin and James O. Berger and Magnus Johannesson and Brian A. Nosek and E.-J. Wagenmakers and Richard Berk and Kenneth A. Bollen and Bj\"{o}rn Brembs and Lawrence Brown and Colin Camerer and David Cesarini and Christopher D. Chambers and Merlise Clyde and Thomas D. Cook and Paul De Boeck and Zoltan Dienes and Anna Dreber and Kenny Easwaran and Charles Efferson and Ernst Fehr and Fiona Fidler and Andy P. Field and Malcolm Forster and Edward I. George and Richard Gonzalez and Steven Goodman and Edwin Green and Donald P. Green and Anthony G. Greenwald and Jarrod D. Hadfield and Larry V. Hedges and Leonhard Held and Teck Hua Ho and Herbert Hoijtink and Daniel J. Hruschka and Kosuke Imai and Guido Imbens and John P. A. Ioannidis and Minjeong Jeon and James Holland Jones and Michael Kirchler and David Laibson and John List and Roderick Little and Arthur Lupia and Edouard Machery and Scott E. Maxwell and Michael McCarthy and Don A. Moore and Stephen L. Morgan and Marcus Munaf{\'{o}} and Shinichi Nakagawa and Brendan Nyhan and Timothy H. Parker and Luis Pericchi and Marco Perugini and Jeff Rouder and Judith Rousseau and Victoria Savalei and Felix D. Sch\"{o}nbrodt and Thomas Sellke and Betsy Sinclair and Dustin Tingley and Trisha Van Zandt and Simine Vazire and Duncan J. Watts and Christopher Winship and Robert L. Wolpert and Yu Xie and Cristobal Young and Jonathan Zinman and Valen E. Johnson},
title = {Redefine statistical significance},
journal = {Nature Human Behaviour}
}
@Book{McNulty2021,
title = {Handbook of Regression Modeling in People Analytics: With Examples in {R} and {Python}},
author = {Keith McNulty},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-1032041742},
url = {https://peopleanalytics-regression-book.org/},
}
@Book{Steif2022,
title = {Public Policy Analytics: Code and Context for Data Science in Government},
author = {Ken Steif},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2022},
note = {ISBN 978-0367507619},
url = {https://urbanspatial.github.io/PublicPolicyAnalytics/},
}
@Book{Moraga2019,
title = {Geospatial Health Data: Modeling and Visualization with {R-INLA} and Shiny},
author = {Paula Moraga},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2019},
note = {ISBN 978-0367357955},
url = {https://www.paulamoraga.com/book-geospatial/},
}
@Book{Paez2021,
title = {An Introduction to Spatial Data Analysis and Statistics: A Course in {R}},
author = {Antonio Paez},
publisher = {McMaster Invisible Press},
address = {Hamilton, Ontario},
year = {2021},
isbn = {978-1-7778515-0-7},
url = {https://www.spatial-analysis-r.org/},
}
@Book{Walker2022,
title = {Analyzing US Census Data: Methods, Maps, and Models in {R}},
author = {Kyle Walker},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2022},
url = {https://walker-data.com/census-r/},
}
@Book{Biecek2021,
author = {Przemyslaw Biecek and Tomasz Burzykowski},
title = {Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in {R} and {Python}},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
isbn = {978-0367135591},
url = {https://ema.drwhy.ai/},
}
@Book{Miguel2020,
title = {Causal Inference: What If},
author = {Miguel A Hern{\'a}n and James M Robins},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2020},
note = {ISBN 978-0367357955},
url = {https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/},
}
@Book{Irizarry2019,
title = {Introduction to Data Science: Data Analysis and Prediction Algorithms with {R}},
author = {Rafael A. Irizarry},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2019},
note = {ISBN 978-0367357986},
url = {https://rafalab.github.io/dsbook/},
}
@Book{Urdinez2021,
title = {R for Political Data Science: A Practical Guide},
author = {Francisco Urdinez and Andres Cruz},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-0367818890},
}
@Book{Lawson2021,
title = {Using {R} for Bayesian Spatial and Spatio-Temporal Health Modeling},
author = {Andrew B. Lawson},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-0367490126},
}
@Book{Haining2021,
title = {Modelling Spatial and Spatial-Temporal Data: A Bayesian approach},
author = {Robert P. Haining and Guangquan Li},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-1032175003},
url = {https://www.sptmbook.com/},
}
@Book{Wikle2019,
title = {Spatio-Temporal Statistics with {R}},
author = {Christopher K. Wikle and Andrew Zammit-Mangion and Noel Cressie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2019},
note = {ISBN 978-1138711136},
url = {https://spacetimewithr.org/},
}
@Book{Andrew2021,
title = {Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology},
author = {Andrew B. Lawson},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
edition = {3rd},
note = {ISBN 978-0367781224},
}
@Book{Garcia2021,
title = {Behavior Analysis with Machine Learning Using {R}},
author = {Enrique Garcia Ceja},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-1032067049},
url = {https://enriquegit.github.io/behavior-free/},
}
@Book{Brostrom2021,
title = {Event History Analysis with {R}},
author = {Göran Broström},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
edition = {2nd},
note = {ISBN 978-1138587717},
url = {http://ehar.se/r/ehar2/},
}
@Book{Machlis2018,
title = {Practical {R} for Mass Communication and Journalism},
author = {Sharon Machlis},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2018},
edition = {1st},
note = {ISBN 978-1315191119},
url = {https://www.machlis.com/R4Journalists/},
}
@Book{Molnar2020,
title = {Interpretable Machine Learning: A Guide for Making Black Box Models Explainable},
author = {Christoph Molnar},
publisher = {Lulu Press, Inc.},
isbn = {978-0244768522},
year = {2020},
url = {https://christophm.github.io/interpretable-ml-book/},
}
@Book{Estrellado2020,
title = {Data Science in Education Using {R}},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2020},
author = {Ryan A. Estrellado and Emily A. Freer and Jesse Mostipak and Joshua M. Rosenberg and Isabella C. Velásquez},
url = {https://datascienceineducation.com/},
isbn = {9780367422257},
}
@book{Mcauley2022,
title = {Personalized Machine Learning},
author = {Julian McAuley},
year = {2022},
publisher = {Cambridge University Press},
address = {Cambridge},
url = {https://cseweb.ucsd.edu/~jmcauley/pml/pml_book.pdf}
}
@Book{Xiao2021,
title = {机器翻译：基础与模型},
publisher = {电子工业出版社},
year = {2021},
author = {肖桐 and 朱靖波},
address = {北京},
url = {https://github.com/NiuTrans/MTBook},
isbn = {9787121335198},
}
@Book{Zhao2017,
title = {深度学习},
publisher = {人民邮电出版社},
year = {2017},
author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
address = {北京},
Translator = {赵申剑 and 黎彧君 and 李凯 and 符天凡},
url = {https://github.com/exacity/deeplearningbook-chinese},
isbn = {9787115461476},
}
@Book{Aston2019,
title = {动手学深度学习},
publisher = {人民邮电出版社},
year = {2019},
author = {Aston Zhang and Mu Li and Zachary C. Lipton and Alexander J. Smola},
address = {北京},
url = {https://github.com/d2l-ai/d2l-zh},
isbn = {9787115490841},
}
@Book{Zhang2019,
title = {机器学习基础},
publisher = {机械工业出版社},
year = {2019},
author = {Mehryar Mohri and Afshin Rostamizadeh and Ameet Talwalkar},
address = {北京},
Translator = {张文生等译},
isbn = {9787111622185},
}
@Book{Neyman1987,
title = {耐曼---现代统计学家},
publisher = {上海翻译出版公司},
year = {1987},
author = {Constance Reid},
address = {上海},
Translator = {姚慕生等},
editor = {袁运开 and 张瑞琨 and 应兴国},
isbn = {7-80514-000-6},
}
@Book{Chen1987,
title = {统计发展史},
publisher = {立信会计图书用品社},
year = {1987},
author = {陈善林 and 张浙},
address = {上海},
isbn = {7-5429-0009-9},
}
@Book{Qiu2020,
title = {神经网络与深度学习},
publisher = {机械工业出版社},
year = {2020},
author = {邱锡鹏},
address = {北京},
isbn = {9787111649687},
url = {https://nndl.github.io/},
}
@Book{Zhou2016,
title = {机器学习},
publisher = {清华大学出版社},
year = {2016},
author = {周志华},
address = {北京},
url = {https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/MLbook2016.htm/},
isbn = {978-7-302-206853-6},
}
@Book{Goodfellow2016,
author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
title = {Deep Learning},
year = {2016},
publisher = {MIT Press},
url = {https://www.deeplearningbook.org/},
isbn = {9780262035613},
}
@Book{Theodoridis2020,
title = {Machine Learning: A Bayesian and Optimization Perspective},
publisher = {Academic Press},
address = {London Wall, London},
year = {2020},
author = {Sergio Theodoridis},
edition = {2nd},
isbn = {9780128188033},
}
@Book{pml2022,
author = {Kevin P. Murphy},
title = {Probabilistic Machine Learning: An introduction},
publisher = {MIT Press},
year = {2022},
address = {Cambridge, Massachusetts},
url = {https://probml.github.io/pml-book/book1.html}
}
@Book{pml2023,
author = {Kevin P. Murphy},
title = {Probabilistic Machine Learning: Advanced Topics},
publisher = {MIT Press},
year = {2023},
address = {Cambridge, Massachusetts},
url = {https://probml.github.io/pml-book/book2.html}
}
@Book{Bishop2006,
title = {Pattern Recognition and Machine Learning},
publisher = {Springer},
year = {2006},
author = {Christopher Bishop},
address = {New York, NY},
url = {https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/},
isbn = {9780387310732},
}
@Book{Mohri2018,
title = {Foundations of Machine Learning},
publisher = {MIT Press},
address = {Cambridge, Massachusetts},
year = {2018},
author = {Mehryar Mohri and Afshin Rostamizadeh and Ameet Talwalkar},
edition = {2nd},
url = {https://cs.nyu.edu/~mohri/mlbook/},
isbn = {9780262039406},
}
@Book{Blum2020,
title = {Foundations of Data Science},
publisher = {Cambridge University Press},
address = {Cambridge},
year = {2020},
author = {Avrim Blum and John Hopcroft and Ravindran Kannan},
url = {https://www.cs.cornell.edu/jeh/book.pdf},
isbn = {9781108755528},
doi = {10.1017/9781108755528.011},
}
@Book{Hastie2009,
title = {The Elements of Statistical Learning: Data Mining, Inference, and Prediction},
publisher = {Springer},
year = {2009},
author = {Trevor Hastie and Rob Tibshirani and Jerome Friedman},
edition = {2nd},
address = {New York, NY},
url = {https://web.stanford.edu/~hastie/ElemStatLearn/},
isbn = {9780387848570},
}
@Book{Fagerland2017,
title = {Statistical Analysis of Contingency Tables},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2017},
author = {Morten W. Fagerland and Stian Lydersen and Petter Laake},
url = {https://contingencytables.com/},
}
@Book{Cohen1988,
title = {Statistical Power Analysis for the Behavioral Sciences},
author = {Jacob Cohen},
publisher = {Lawrence Erlbaum Associates},
address = {Hillsdale, NJ},
year = {1988},
edition = {2nd},
note = {ISBN 0-8058-0283-5},
url = {https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf},
}
@Book{Wainwright2019,
doi = {10.1017/9781108627771},
year = {2019},
publisher = {Cambridge University Press},
address = {Cambridge},
author = {Martin J. Wainwright},
title = {High-Dimensional Statistics: A Non-Asymptotic Viewpoint}
}
@Book{MacKay2003,
url = {https://www.inference.org.uk/itprnn/book.pdf},
isbn = {9780521642989},
year = {2003},
publisher = {Cambridge University Press},
address = {Cambridge},
author = {David J. C. MacKay},
title = {Information Theory, Inference and Learning Algorithms}
}
@Book{Poldrack2021,
year = {2021},
author = {Russell A. Poldrack},
title = {Statistical Thinking for the 21st Century},
url = {https://statsthinking21.github.io/statsthinking21-core-site/},
}
@article{Breiman2001,
URL = {http://www.jstor.org/stable/2676681},
abstract = {There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.},
author = {Leo Breiman},
journal = {Statistical Science},
number = {3},
pages = {199--215},
publisher = {Institute of Mathematical Statistics},
title = {Statistical Modeling: The Two Cultures},
volume = {16},
year = {2001}
}
@article{Yu2013,
author = {Bin Yu},
title = {Stability},
volume = {19},
journal = {Bernoulli},
number = {4},
publisher = {Bernoulli Society for Mathematical Statistics and Probability},
pages = {1484 -- 1500},
abstract = {Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in stability of statistical results relative to “reasonable” perturbations to data and to the model used. Jacknife, bootstrap, and cross-validation are based on perturbations to data, while robust statistics methods deal with perturbations to models. In this article, a case is made for the importance of stability in statistics. Firstly, we motivate the necessity of stability for interpretable and reliable encoding models from brain fMRI signals. Secondly, we find strong evidence in the literature to demonstrate the central role of stability in statistical inference, such as sensitivity analysis and effect detection. Thirdly, a smoothing parameter selector based on estimation stability (ES), ES-CV, is proposed for Lasso, in order to bring stability to bear on cross-validation (CV). ES-CV is then utilized in the encoding models to reduce the number of predictors by 60% with almost no loss (1.3%) of prediction performance across over 2,000 voxels. Last, a novel “stability” argument is seen to drive new results that shed light on the intriguing interactions between sample to sample variability and heavier tail error distribution (e.g., double-exponential) in high-dimensional regression models with $p$ predictors and $n$ independent samples. In particular, when $p/n\rightarrow\kappa\in(0.3,1)$ and the error distribution is double-exponential, the Ordinary Least Squares (OLS) is a better estimator than the Least Absolute Deviation (LAD) estimator.},
keywords = {cross-validation, double exponential error, estimation stability, fMRI, high-dim regression, Lasso, movie reconstruction, robust statistics, stability},
year = {2013},
doi = {10.3150/13-BEJSP14},
}
@article{Donoho2017,
doi = {10.1080/10618600.2017.1384734},
year = {2017},
month = oct,
volume = {26},
number = {4},
pages = {745--766},
author = {David Donoho},
title = {50 Years of Data Science},
journal = {Journal of Computational and Graphical Statistics}
}
@article{Benjamini1995,
URL = {https://www.jstor.org/stable/2346101},
abstract = {The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses-the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.},
author = {Yoav Benjamini and Yosef Hochberg},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
number = {1},
pages = {289--300},
title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
volume = {57},
year = {1995}
}
@article {Murdoch2019,
author = {Murdoch W. James and Singh Chandan and Kumbier Karl and Abbasi-Asl Reza and Yu Bin},
title = {Definitions, methods, and applications in interpretable machine learning},
volume = {116},
number = {44},
pages = {22071--22080},
year = {2019},
doi = {10.1073/pnas.1900654116},
publisher = {National Academy of Sciences},