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1. Séries temporelles avec R : Méthodes et cas
by Yves Aragon
出版情報: Paris : Springer Paris, 2011
シリーズ名: Pratique R ; 0
オンライン: http://dx.doi.org/10.1007/978-2-8178-0208-4
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2. Analyse statistique des risques agro-environnementaux : Études de cas
by David Makowski, Hervé Monod
出版情報: Paris : Springer Paris, 2011
シリーズ名: Collection Statistique et probabilités appliquées ;
オンライン: http://dx.doi.org/10.1007/978-2-8178-0251-0
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3. Modeling Psychophysical Data in R
by Kenneth Knoblauch, Laurence T. Maloney
出版情報: New York, NY : Springer New York : Imprint: Springer, 2012
シリーズ名: Use R! ; 32
オンライン: http://dx.doi.org/10.1007/978-1-4614-4475-6
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目次情報: 続きを見る
A First Tour through R by Example
Modeling in R
Signal Detection Theory (SDT)
The Psychometric Function: Introduction
The Psychometric Function: Continuation
Classification Images
Maximum Likelihood Difference Scaling (MLDS)
Maximum Likelihood Conjoint Measurement (MLCM)
Mixed-Effect Models
Some Basics of R
Statistical Background
References
Index
A First Tour through R by Example
Modeling in R
Signal Detection Theory (SDT)
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4. Handbook of Computational Statistics : Concepts and Methods. 2nd ed. 2012. revised and updated
edited by James E. Gentle, Wolfgang Karl Härdle, Yuichi Mori
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012
シリーズ名: Springer Handbooks of Computational Statistics ;
オンライン: http://dx.doi.org/10.1007/978-3-642-21551-3
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目次情報: 続きを見る
Part I  Computational Statistics
Part II  Statistical Computing
Part III  Statistical Methodology
Part IV  Selected Applications
Part I  Computational Statistics
Part II  Statistical Computing
Part III  Statistical Methodology
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5. Linear Mixed-Effects Models Using R : A Step-by-Step Approach
by Andrzej Gałecki, Tomasz Burzykowski
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
シリーズ名: Springer Texts in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-1-4614-3900-4
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Introduction
Linear Models for Independent Observations
Linear Fixed-effects Models for Correlated Data
Linear Mixed-effects Models
Introduction
Linear Models for Independent Observations
Linear Fixed-effects Models for Correlated Data
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6. Essential Statistical Inference : Theory and Methods
by Dennis D. Boos, L. A. Stefanski
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
シリーズ名: Springer Texts in Statistics ; 120
オンライン: http://dx.doi.org/10.1007/978-1-4614-4818-1
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目次情報:
Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index
R-code Index
Subject Index.
Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index
R-code Index
Subject Index.
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7. Essentials of Monte Carlo Simulation : Statistical Methods for Building Simulation Models
by Nick T. Thomopoulos
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
オンライン: http://dx.doi.org/10.1007/978-1-4614-6022-0
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8. A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem : with Simulations and Examples in SAS®
by Tejas Desai
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
シリーズ名: SpringerBriefs in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-1-4614-6443-3
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Introduction
On Testing for Multivariate Normality
On Testing Equality of Covariance Matrices
On Heteroscedastic MANOVA
References
Introduction
On Testing for Multivariate Normality
On Testing Equality of Covariance Matrices
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9. An Introduction to Statistical Learning : with Applications in R
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
シリーズ名: Springer Texts in Statistics ; 103
オンライン: http://dx.doi.org/10.1007/978-1-4614-7138-7
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Introduction
Statistical Learning
Linear Regression
Classification
Resampling Methods
Linear Model Selection and Regularization
Moving Beyond Linearity
Tree-Based Methods
Support Vector Machines
Unsupervised Learning
Index
Introduction
Statistical Learning
Linear Regression
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10. Statistical Analysis of Network Data with R
by Eric D. Kolaczyk, Gábor Csárdi
出版情報: New York, NY : Springer New York : Imprint: Springer, 2014
シリーズ名: Use R! ; 65
オンライン: http://dx.doi.org/10.1007/978-1-4939-0983-4
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11. Statistical Methods for Ranking Data
by Mayer Alvo, Philip L.H. Yu
出版情報: New York, NY : Springer New York : Imprint: Springer, 2014
シリーズ名: Frontiers in Probability and the Statistical Sciences ;
オンライン: http://dx.doi.org/10.1007/978-1-4939-1471-5
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Introduction
Exploratory Analysis of Ranking Data
Correlation Analysis of Paired Ranking Data
Testing for randomness, agreement and interaction
Block Designs
General Theory of Hypothesis Testing
Testing for Ordered Alternatives
Probability Models for Ranking Data
Probit Models for Ranking Data
Decision Tree Models for Ranking Data
Extension of Distance-Based Models for Ranking Data
Appendix A: Ranking Data Sets
Appendix B: Limit Theorems
Appendix C: Review on Decision Trees
Introduction
Exploratory Analysis of Ranking Data
Correlation Analysis of Paired Ranking Data