R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(3)
R3 on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(0)
https://qiita.com/kaizen_nagoya/items/a8eac9afbf16d2188901
What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
https://arxiv.org/abs/2012.00174
References
3
Anderlucci, L., Montanari, A., and Viroli, C. (2019). The importance of being clustered: Uncluttering the trends of statistics from 1970 to 2015. Statistical Science 34, 280–300.
https://arxiv.org/abs/1709.03563
References on 3
3.1
Ambroise, C. and G. Govaert (2000). Data Analysis, Classification, and Related Methods, Chapter EM Algorithm for Partially Known Labels, pp. 161–166. Berlin, Heidelberg: Springer Berlin Heidelberg.
https://link.springer.com/chapter/10.1007/978-3-642-59789-3_26
Reference on 3.1
3.1.1
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3.1.2
GANESALINGAM, S. and MCLACHLAN, G.J. (1978): The Efficiency of Linear Discriminant Function Based on Unclassified Initial Samples, Biometrika, 65, 658–662.
3.1.3
JORDAN, M.I. and JACOBS, R.A. (1994): Hierarchical Mixtures of Experts and the EM Algorithm, Neural Computation, 6, 181–214.
3.1.4
MCLACHLAN, G.J. and BASFORD, K.E. (1989): Misture Models. Inference and Applications to Clustering. Marcel Dekker, New York.
3.1.5
MCLACHLAN, G. and KRISHNAN, T. (1997): The EM Algorithm and Extensions. Wiley, New York.
3.1.6
O’NEIL, T.J. (1978): Normal Discrimination with Unclassified Observations, Journal of the American Statistical Association, 73, 821–826.
3.1.7
TITTERINGTON, D.M., SMITH, A.F. and MAKOV, U.E. (1985): Statistical Analysis of Finite Mixture Distributions. Wiley, New York.
3.2
Blei, D., A. Ng, and M. Jordan (2003). Latent dirichlet allocation. Journal of machine Learning research 3, 9931022.
3.3
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3.5
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3.6
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3.7
Dhillon, I. S. and D. S. Modha (2001). Concept decompositions for large sparse text data using clustering. Machine Learning 42(1/2), 143–175.
3.8
Diaconis, P. (1988). Group representations in probability and statistics. In Lecture notes, Volume 11 of Monograph Series. Institute of Mathematical Statistics.
3.9
Fligner, M. A. and J. S. Verducci (1986). Distance based ranking models. Journal of the Royal Statistical Society. Series B (Methodological) 48 (3), 359–369.
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Fraley, C. and A. Raftery (2002). Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association 97, 611–631.
3.10
Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd an- nual international ACM SIGIR conference on Research and development in information retrieval, 50–57.
3.11
Maitra, R. and I. P. Ramler (2010). A k-mean-directions algorithm for fast clustering of data on the sphere. Journal of Computational and Graphical Statistics 19 (2), 377–396.
3.12
Mallows, C. L. (1957). Non-Null Ranking Models. I. Biometrika 44(1/2), 359–369.
3.13
McLachlan, G. J. and D. Peel (2000). Finite Mixture Models. Wiley.
Murphy, T. B. and D. Martin (2003). Mixtures of distance-based models for ranking data. Computational Statistics & Data Analysis 41, 645–655.
3.14
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3.15
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3.16
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3.17
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3.18
Vandewalle, V., C. Biernacki, G. Celeux, and G. Govaert (2013). A predictive deviance criterion for selecting a generative model in semi-supervised classification. Computational Statistics & Data Analysis 64, 220–236.
3.19
Zhu, X., A. B. Goldberg, R. Brachman, and T. Dietterich (2009). Introduction to Semi- Supervised Learning. Morgan and Claypool Publishers.
参考資料(References)
Data Scientist の基礎(2)
https://qiita.com/kaizen_nagoya/items/8b2f27353a9980bf445c
岩波数学辞典 二つの版がCDに入ってお得
https://qiita.com/kaizen_nagoya/items/1210940fe2121423d777
岩波数学辞典
https://qiita.com/kaizen_nagoya/items/b37bfd303658cb5ee11e
アンの部屋（人名から学ぶ数学：岩波数学辞典）英語(24)
https://qiita.com/kaizen_nagoya/items/e02cbe23b96d5fb96aa1
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