茆诗松, 程依明, and 濮晓龙, 高等数理统计, 2nd ed. 北京: 高等教育出版社, 2006.
B. D. Ripley, “Statistical methods need software: A view of statistical computing,” Opening Lecture Royal Statistical Society
. Plymouth, Sep. 04, 2002. Accessed: Nov. 09, 2019. [Online]. Available: https://www.stats.ox.ac.uk/~ripley/RSS2002.pdf
J. M. Chambers, “S, R, and Data Science,” The R Journal
, vol. 12, no. 1, pp. 462–476, 2020, doi: 10.32614/RJ-2020-028
Y. Xie, Dynamic documents with R and knitr
, 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC, 2015.Available: https://yihui.org/knitr/
H. Wickham, ggplot2: Elegant graphics for data analysis
, 2nd ed. New York: Springer-Verlag, 2016.Available: https://ggplot2-book.org/
P. Murrell, “Integrating grid graphics output with base graphics output,” R News, vol. 3, no. 2, pp. 7–12, 2003.
P. Murrell and R. Ihaka, “An approach to providing mathematical annotation in plots,” Journal of Computational and Graphical Statistics, vol. 9, no. 3, pp. 582–599, 2000.
Y. Qiu, “showtext: Using system fonts in R graphics,” The R Journal
, vol. 7, no. 1, pp. 99–108, Jun. 2015, doi: 10.32614/RJ-2015-008
E. Torres-Manzanera, Xkcd: Plotting ggplot2 graphics in an XKCD style. 2018.
R. Stauffer, G. J. Mayr, M. Dabernig, and A. Zeileis, “Somewhere over the rainbow: How to make effective use of colors in meteorological visualizations,” Bulletin of the American Meteorological Society
, vol. 96, no. 2, pp. 203–216, 2009, doi: 10.1175/BAMS-D-13-00155.1
A. Zeileis, K. Hornik, and P. Murrell, “Escaping RGBland: Selecting colors for statistical graphics,” Computational Statistics & Data Analysis
, vol. 53, no. 9, pp. 3259–3270, 2009, doi: 10.1016/j.csda.2008.11.033
A. Zeileis et al.
, “colorspace: A toolbox for manipulating and assessing colors and palettes,”
arXiv.org E-Print Archive, arXiv 1903.06490, 2019.Available: http://arxiv.org/abs/1903.06490
Z. Gu, R. Eils, and M. Schlesner, “Complex heatmaps reveal patterns and correlations in multidimensional genomic data,” Bioinformatics, 2016.
P. Kampstra, “beanplot: A boxplot alternative for visual comparison of distributions,” Journal of Statistical Software
, vol. 28, no. 1, pp. 1–9, 2008,Available: http://www.jstatsoft.org/v28/c01/
Y. Tang, “Autoplotly: An r package for automatic generation of interactive visualizations for statistical results,” Journal of Open Source Software
, vol. 3, 2018,Available: https://doi.org/10.21105/joss.00657
Y. Tang, M. Horikoshi, and W. Li, “ggfortify: Unified interface to visualize statistical results of popular r packages,” The R Journal
, vol. 8, no. 2, pp. 474–485, 2016, doi: 10.32614/RJ-2016-060
Y. Xie, “animation: An R package for creating animations and demonstrating statistical methods,” Journal of Statistical Software
, vol. 53, no. 1, pp. 1–27, 2013,Available: http://www.jstatsoft.org/v53/i01/
X. Pu and M. Kay, “A probabilistic grammar of graphics,”
in Proceedings of the 2020 CHI conference on human factors in computing systems
, 2020, pp. 1–13. doi: 10.1145/3313831.3376466
P. Kasprzak, L. Mitchell, O. Kravchuk, and A. Timmins, “Six Years of Shiny in Research - Collaborative Development of Web Tools in R,” The R Journal
, vol. 12, no. 2, pp. 155–162, 2021, doi: 10.32614/RJ-2021-004
M. L. Eaton, “Chapter 8: The wishart distribution,”
in Multivariate statistics
, vol. 53, Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007, pp. 302–333. doi: 10.1214/lnms/1196285114
陈希孺, 数理统计引论. 北京: 科学出版社, 1981.
C. J. Clopper and E. S. Pearson, “The use of confidence or fiducial limits illustrated in the case of the binomial,” Biometrika
, vol. 26, no. 4, pp. 404–413, Dec. 1934, doi: 10.1093/biomet/26.4.404
J. Cohen, “The earth is round (\(p < .05\)),” American Psychologist
, vol. 49, no. 12, pp. 997–1003, 1994, doi: 10.1037/0003-066x.49.12.997
E. B. Wilson, “Probable inference, the law of succession, and statistical inference,” Journal of the American Statistical Association
, vol. 22, no. 158, pp. 209–212, Jun. 1927, doi: 10.1080/01621459.1927.10502953
T. W. Epps and L. B. Pulley, “A test for normality based on the empirical characteristic function,” Biometrika
, vol. 70, no. 3, pp. 723–726, 1983, doi: 10.2307/2336512
P. Lafaye de Micheaux and V. A. Tran, “PoweR: A reproducible research tool to ease monte carlo power simulation studies for goodness-of-fit tests in R,” Journal of Statistical Software
, vol. 69, no. 3, pp. 1–42, 2016, doi: 10.18637/jss.v069.i03
"Student", “The probable error of a mean,” Biometrika, vol. 6, pp. 1–25, 1908.
C. C. Heyde, E. Seneta, P. Crépel, S. E. Fienberg, and J. Gani, Statisticians of the centuries
. New York, NY: Springer-Verlag, 2001. doi: 10.1007/978-1-4613-0179-0
P. L. HSU, “Contribution to the theory of "student’s" \(T\)-test as applied to the problem of two samples,” Statistical Research Memoirs, vol. 2, pp. 1–24, 1938.
S.-H. Kim and A. S. Cohen, “On the behrens-fisher problem: A review,” Journal of Educational and Behavioral Statistics
, vol. 23, no. 4, pp. 356–377, 1998, doi: 10.2307/1165281
P. L. HSU, Collected papers. New York, NY: Springer-Verlag, 1983.
T. Hothorn, K. Hornik, M. A. van de Wiel, and A. Zeileis, “Implementing a class of permutation tests: The coin package,” Journal of Statistical Software
, vol. 28, no. 8, pp. 1–23, 2008, doi: 10.18637/jss.v028.i08
A. Kuznetsova, P. B. Brockhoff, and R. H. B. Christensen, “lmerTest package: Tests in linear mixed effects models,” Journal of Statistical Software
, vol. 82, no. 13, pp. 1–26, 2017, doi: 10.18637/jss.v082.i13
茆诗松, 周纪芗, and 陈颖, 试验设计, 1st ed. 北京: 中国统计出版社, 2004.
R. I. Kabacoff, R in action: Data analysis and graphics with r
, 2nd ed. Shelter Island, NY: Manning PUblications Co., 2015.Available: https://github.com/kabacoff/RiA2
P. Berger and R. Maurer, Experimental design: With application in management, engineering, and the sciences., 1st ed. Duxbury, 2002.
P. Berger, R. Maurer, and G. B. Celli, Experimental design: With application in management, engineering, and the sciences.
, 2nd ed. New York, NY: Springer International Publishing, 2018. doi: 10.1007/978-3-319-64583-4
M. Fritz and P. D. Berger, Improving the user experience through practical data analytics: Gain meaningful insight and increase your bottom line, 1st ed. Morgan Kaufmann, 2015.
G. E. P. Box, J. S. Hunter, and W. G. Hunter, Statistics for experimenters: Design, innovation, and discovery, 2nd ed. Hoboken, New Jersey: John Wiley & Sons, Inc, 2005.
R. Kohavi, D. Tang, and Y. Xu, Trustworthy online controlled experiments: A practical guide to a/b testing
. Cambridge, United Kingdom: Cambridge University Press, 2020.Available: https://experimentguide.com/
G. Beall, “The transformation of data from entomological field experiments so that the analysis of variance becomes applicable,” Biometrika
, vol. 32, no. 3/4, pp. 243–262, 1942, doi: 10.2307/2332128
D. W. Hosmer and S. Lemeshow, Applied logistic regression, Second. New York, NY: John Wiley & Sons, 2000.
K. E. Train, Discrete choice methods with simulation, Second. New York, NY: Cambridge University Press, 2009.
H. Ishwaran and J. S. Rao, “Spike and slab variable selection: Frequentist and bayesian strategies,” Ann. Statist.
, vol. 33, no. 2, pp. 730–773, 2005,Available: http://arXiv.org/abs/math/0505633v1
A. Hasan, Z. Wang, and A. S. Mahani, “Fast estimation of multinomial logit models: R package mnlogit,” Journal of Statistical Software
, vol. 75, no. 3, pp. 1–24, 2016, doi: 10.18637/jss.v075.i03
B.-H. Mevik and R. Wehrens, “The pls package: Principal component and partial least squares regression in r,” Journal of Statistical Software
, vol. 18, no. 2, pp. 1–23, 2007, doi: 10.18637/jss.v018.i02
P. F. Thall and S. C. Vail, “Some covariance models for longitudinal count data with overdispersion,” Biometrics
, vol. 46, no. 3, pp. 657–671, 1990,Available: https://www.jstor.org/stable/2532086
M. A. Espeland and S. L. Hui, “A general approach to analyzing epidemiologic data that contain misclassification errors,” Biometrics
, vol. 43, no. 4, pp. 1001–1012, 1987,Available: https://www.jstor.org/stable/2531553
D. S. Young, Handbook of regression methods. Boca Raton, FL: Chapman; Hall/CRC, 2017.
M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied linear statistical models, Fifth. New York, NY: McGraw-Hill/Irwin, 2005.
A. E. Gelfand, S. E. Hills, A. Racine-Poon, and A. F. M. Smith, “Illustration of bayesian inference in normal data models using gibbs sampling,” Journal of the American Statistical Association
, vol. 85, no. 412, pp. 972–985, 1990, doi: 10.1080/01621459.1990.10474968
Terry M. Therneau and Patricia M. Grambsch, Modeling survival data: Extending the Cox model. New York: Springer, 2000.
D. R. Brillinger, Time series: Data analysis and theory. Philadelphia, PA, USA: Society for Industrial; Applied Mathematics, 2001.
R. A. Maronna, R. D. Martin, and V. J. Yohai, Robust statistics, theory and methods. John Wiley & Sons, Ltd, 2006.
P. R. Winters, “Forecasting sales by exponentially weighted moving averages,” Management Science
, vol. 6, no. 3, pp. 324–342, 1960, doi: 10.1287/mnsc.6.3.324
C. C. Holt, “Forecasting seasonals and trends by exponentially weighted moving averages,” International Journal of Forecasting
, vol. 20, no. 1, pp. 5–10, 2004, doi: 10.1016/j.ijforecast.2003.09.015
D. Lüdecke, P. Waggoner, and D. Makowski, “insight: A unified interface to access information from model objects in r,” Journal of Open Source Software
, vol. 4, no. 38, p. 1412, 2019, doi: 10.21105/joss.01412
D. Makowski, M. Ben-Shachar, and D. Lüdecke, “bayestestR: Describing effects and their uncertainty, existence and significance within the bayesian framework,” Journal of Open Source Software
, vol. 4, no. 40, p. 1541, 2019, doi: 10.21105/joss.01541
L. Breiman, “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Journal of the American Statistical Association
, vol. 16, no. 3, pp. 199–231, Dec. 2001, doi: 10.1214/ss/1009213726
N. L. Johnson and S. Kotz, Leading personalities in statistical sciences: From the seventeenth century to the present. New York, NY: John Wiley & Sons, 1997.
A. J. Dobson, An introduction to statistical modelling
, 1st ed. London: Chapman; Hall/CRC, 1983. doi: 10.1007/978-1-4899-3174-0
A. Fu, B. Narasimhan, and S. Boyd, “CVXR: An R package for disciplined convex optimization,” Journal of Statistical Software
, vol. 94, no. 14, pp. 1–34, 2020, doi: 10.18637/jss.v094.i14
M. Binois and V. Picheny, “GPareto: An R package for gaussian-process-based multi-objective optimization and analysis,” Journal of Statistical Software
, vol. 89, no. 8, pp. 1–30, 2019, doi: 10.18637/jss.v089.i08
S. Theußl, F. Schwendinger, and K. Hornik, “ROI: An extensible R optimization infrastructure,” Journal of Statistical Software
, vol. 94, no. 15, pp. 1–64, 2020, doi: 10.18637/jss.v094.i15
M. Gilli, D. Maringer, and E. Schumann, Numerical methods and optimization in finance
, Second. Waltham, MA, USA: Elsevier/Academic Press, 2019.Available: http://www.enricoschumann.net/NMOF/
J. C. Nash, “On best practice optimization methods in r,” Journal of Statistical Software
, vol. 60, no. 2, pp. 1–14, 2014, doi: 10.18637/jss.v060.i02
R. Varadhan and P. Gilbert, “BB: An R package for solving a large system of nonlinear equations and for optimizing a high-dimensional nonlinear objective function,” Journal of Statistical Software
, vol. 32, no. 4, pp. 1–26, 2009,Available: https://www.jstatsoft.org/v32/i04/
K. Deb, “Multi-objective optimization,”
in Search methodologies: Introductory tutorials in optimization and decision support techniques
, E. K. Burke and G. Kendall, Eds. Boston, MA: Springer US, 2005, pp. 273–316. doi: 10.1007/0-387-28356-0_10
R. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society. Series B (Methodological)
, vol. 58, no. 1, pp. 267–288, 1996,Available: http://www.jstor.org/stable/2346178
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” The Annals of Statistics
, vol. 32, no. 2, pp. 407–499, 2004, doi: 10.1214/009053604000000067
Y. Kim, H. Choi, and H.-S. Oh, “Smoothly clipped absolute deviation on high dimensions,” Journal of the American Statistical Association
, vol. 103, no. 484, pp. 1665–1673, 2008, doi: 10.1198/016214508000001066
C.-H. Zhang, “Nearly unbiased variable selection under minimax concave penalty,” The Annals of Statistics
, vol. 38, no. 2, pp. 894–942, 2010, doi: 10.1214/09-AOS729
K. Soetaert and F. Meysman, “Reactive transport in aquatic ecosystems: Rapid model prototyping in the open source software R,” Environmental Modelling & Software, vol. 32, pp. 49–60, 2012.
D. C. Hoaglin and R. E. Welsch, “The hat matrix in regression and ANOVA,” The American Statistician
, vol. 32, no. 1, pp. 17–22, 1978,Available: https://www.jstor.org/stable/2683469
M. M. Andersen and S. Højsgaard, “Ryacas: A computer algebra system in R,” Journal of Open Source Software
, vol. 4, no. 42, 2019,Available: https://doi.org/10.21105/joss.01763
A. Meurer et al.
, “SymPy: Symbolic computing in python,” PeerJ Computer Science
, vol. 3, p. e103, Jan. 2017, doi: 10.7717/peerj-cs.103
R. Ihaka and R. Gentleman, “R: A language for data analysis and graphics,” Journal of Computational and Graphical Statistics, vol. 5, no. 3, pp. 299–314, 1996.
F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
S. Raschka and V. Mirjalili, Python machine learning, 2nd ed. Birmingham, UK: Packt Publishing, 2017.