Прикладной статистический анализ данных (курс лекций, 2017)

Материал из MachineLearning.

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Версия 05:05, 10 февраля 2017

Литература

1. Основная литература

1. Вальд, А. Последовательный анализ. — М.: Физматлит, 1960.
2. Кобзарь, А.И. Прикладная математическая статистика. — М.: Физматлит, 2006.
3. Лагутин, М.Б. Наглядная математическая статистика. — М.: П-центр, 2003.
4. Agresti, A. Categorical Data Analysis. — Hoboken: John Wiley & Sons, 2013.
5. Bilder, C.R., Loughin, T.M. Analysis of Categorical Data with R. — Boca Raton: Chapman and Hall/CRC, 2013.
6. Bonnini, S., Corain, L., Marozzi, M., Salmaso S. Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R. — Hoboken: John Wiley & Sons, 2014.
7. Bretz, F., Hothorn, T., Westfall, P. Multiple Comparisons Using R. — Boca Raton: Chapman and Hall/CRC, 2010.
8. Chihara, L., Hesterberg, T. Mathematical Statistics with Resampling and R — Hoboken: John Wiley & Sons, 2011.
9. Diez, D.M, Barr, C.D., Cetinkaya-Rundel, M., Dorazio, L. Advanced High School Statistics. — OpenIntro, 2015.
10. Hyndman, R.J., Athanasopoulos G. Forecasting: principles and practice. — OTexts, 2016. https://www.otexts.org/book/fpp
11. Kanji, G.K. 100 statistical tests. — London: SAGE Publications, 2006.
12. Mukhopadhyay, N., de Silva, B. M. Sequential methods and their applications. — Boca Raton: Chapman and Hall/CRC, 2009.
13. Olsson, U. Generalized Linear Models: An Applied Approach. — Lund: Studentlitteratur, 2004.
14. Pearl J., Glymour M., Jewell N.P. Causal Inference in Statistics: A Primer. — Chichester: John Wiley & Sons, 2016.
15. Tabachnick, B.G., Fidell, L.S. Using Multivariate Statistics. — Boston: Pearson Education, 2012.
16. Wooldridge, J. Introductory Econometrics: A Modern Approach. — Mason: South-Western Cengage Learning, 2013.

2. Дополнительная литература

1. Cameron, A.A., Trivedi, P.K. Regression Analysis of Count Data. — Cambridge: Cambridge University Press, 2013.
2. Dickhaus, T. Simultaneous Statistical Inference With Applications in the Life Sciences. — Heidelberg: Springer, 2014.
3. Good, P. Permutation, Parametric and Bootstrap Tests of Hypotheses: A Practical Guide to Resampling Methods for Testing Hypotheses. — New York: Springer, 2005.
4. Hosmer, D.W., Lemeshow S., Sturdivant, R.X. Applied Logistic Regression. — Hoboken: John Wiley & Sons, 2013.
5. Kirchgassner, G., Wolters, J., Hassler, U. Introduction to modern time series analysis. — Heidelberg: Springer, 2013.
6. Nagarajan, R., Scutari, M., Lèbre, S. Bayesian Networks in R with Applications in Systems Biology. — New York: Springer, 2013.