Julian James Faraway
（2006年刊行，Chapman and Hall / CRC［Texts in Statistical Science Series 66］， ISBN:158488424→著者サイト）
【目次】
Preface v
1 Introduction 1
2 Binomial Data 25
2.1 Challenger Disaster Example 25
2.2 Binomial Regression Model 26
2.3 Inference 29
2.4 Tolerance Distribution 31
2.5 Interpreting Odds 31
2.6 Prospective and Retrospective Sampling 34
2.7 Choice of Link Function 36
2.8 Estimation Problems 38
2.9 Goodness of Fit 40
2.10 Prediction and Effective Doses 41
2.11 Overdispersion 43
2.12 Matched Case-Control Studies 483 Count Regression 55
3.1 Poisson Regression 55
3.2 Rate Models 61
3.3 Negative Binomial 634 Contingency Tables 69
4.1 Two-by-Two Tables 69
4.2 Larger Two-Way Tables 75
4.3 Matched Pairs 79
4.4 Three-Way Contingency Tables 81
4.5 Ordinal Variables 885 Multinomial Data 97
5.1 Multinomial Logit Model 97
5.2 Hierarchical or Nested Responses 103
5.3 Ordinal Multinomial Responses 1066 Generalized Linear Models 115
6.1 GLM Definition 115
6.2 Fitting a GLM 117
6.3 Hypothesis Tests 120
6.4 GLM Diagnostics 1237 Other GLMs 135
7.1 Gamma GLM 135
7.2 Inverse Gaussian GLM 142
7.3 Joint Modeling of the Mean and Dispersion 144
7.4 Quasi-Likelihood 1478 Random Effects 153
8.1 Estimation 154
8.2 Inference 158
8.3 Predicting Random Effects 161
8.4 Blocks as Random Effects 163
8.5 Split Plots 167
8.6 Nested Effects 170
8.7 Crossed Effects 172
8.8 Multilevel Models 1749 Repeated Measures and Longitudinal Data 185
9.1 Longitudinal Data 186
9.2 Repeated Measures 191
9.3 Multiple Response Multilevel Models 19510 Mixed Effect Models for Nonnormal Responses 201
10.1 Generalized Linear Mixed Models 201
10.2 Generalized Estimating Equations 20411 Nonparametric Regression 211
11.1 Kernel Estimators 213
11.2 Splines 217
11.3 Local Polynomials 221
11.4 Wavelets 222
11.5 Other Methods 226
11.6 Comparison of Methods 227
11.7 Multivariate Predictors 22812 Additive Models 231
12.1 Additive Models Using the gam Package 233
12.2 Additive Models Using mgcv 235
12.3 Generalized Additive Models 240
12.4 Alternating Conditional Expectations 241
12.5 Additivity and Variance Stabilization 244
12.6 Generalized Additive Mixed Models 246
12.7 Multivariate Adaptive Regression Splines 24713 Trees 253
13.1 Regression Trees 253
13.2 Tree Pruning 257
13.3 Classification Trees 26114 Neural Networks 269
14.1 Statistical Models as NNs 270
14.2 Feed-Forward Neural Network with One Hidden Layer 270
14.3 NN Application 272
14.4 Conclusion 276A Likelihood Theory 279
A.1 Maximum Likelihood 279
A.2 Hypothesis Testing 282B R Information 287
Bibliography 289
Index 297
