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Lecture Notes in Statistics 111-120.rar
本附件包括：
- (Lecture Notes in Statistics 113) Rainer Schwabe (auth.)-Optimum Designs for Multi-Factor Models-Springer-Verlag New York (1996).pdf
- (Lecture Notes in Statistics 120) Roderick P. McDonald (auth.), Maia Berkane (eds.)-Latent Variable Modeling and Applications to Causality-Springer-Verlag New York (1997).pdf
- (Lecture Notes in Statistics 111) Leon Willenborg, Ton de Waal (auth.)-Statistical Disclosure Control in Practice-Springer-Verlag New York (1996).pdf
- (Lecture Notes in Statistics 112) Paul R. Cohen, Dawn E. Gregory, Lisa Ballesteros, Robert St. Amant (auth.), Doug Fisher, Hans-J. Lenz (eds.)-Learning from Data_ Artificial Intelligence and Statistic.pdf
- (Lecture Notes in Statistics 114) F. Thomas Bruss, Thomas S. Ferguson (auth.), C. C. Heyde, Yu V. Prohorov, Ronald Pyke, S. T. Rachev (eds.)-Athens Conference on Applied Probability and Time Series An.pdf
- (Lecture Notes in Statistics 116) Genshiro Kitagawa, Will Gersch (auth.)-Smoothness Priors Analysis of Time Series-Springer-Verlag New York (1996).pdf
- (Lecture Notes in Statistics 117) J. G. Dai, John H. Vande Vate (auth.), Paul Glasserman, Karl Sigman, David D. Yao (eds.)-Stochastic Networks-Springer-Verlag New York (1996).pdf
- (Lecture Notes in Statistics 118) Radford M. Neal (auth.)-Bayesian Learning for Neural Networks-Springer-Verlag New York (1996).pdf
- (Lecture Notes in Statistics 119) Masanao Aoki (auth.), Masanao Aoki, Arthur M. Havenner (eds.)-Applications of Computer Aided Time Series Modeling-Springer-Verlag New York (1997).pdf
81.67 MB
2017-9-9

R-optimal designs for multi-factor models with heteroscedastic errors.pdf
560.88 KB
2017-8-3

Multi-factor Models and Signal Processing Techniques.pdf
Multi-factor Models and Signal Processing Techniques
3.17 MB
2015-6-11