统计推断原理 英文版【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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Example 1.1 The normal mean3

Example 1.2 Linear regression4

Example 1.3 Linear regression in semiparametric form4

Example 1.4 Linear model4

Example 1.5 Normal theory nonlinear regression4

Example 1.6 Exponential distribution5

Example 1.7 Comparison of binomial probabilities5

Example 1.8 Location and related problems5

Example 1.9 A component of variance model11

Example 1.10 Markov models12

Example 2.1 Exponential distribution(ctd)19

Example 2.2 Linear model(ctd)19

Example 2.3 Uniform distribution20

Example 2.4 Binary fission20

Example 2.5 Binomial distribution21

Example 2.6 Fisher's hyperbola22

Example 2.7 Binary fission(ctd)23

Example 2.8 Binomial distribution(ctd)23

Example 2.9 Mean of a multivariate normal distribution27

Example 3.1 Test of a Poissonmean32

Example 3.2 Adequacy of Poisson model33

Example 3.3 More on the Poisson distribution34

Example 3.4 Test of symmetry38

Example 3.5 Nonparametric two-sample test39

Example 3.6 Ratio of normal means40

Example 3.7 Poisson-distributed signal with additive noise41

Example 4.1 Uniform distribution of known range47

Example 4.2 Two measuring instruments48

Example 4.3 Linear model49

Example 4.4 Two-by-two contingency table51

Example 4.5 Mantel-Haenszel procedure54

Example 4.6 Simple regression for binary data55

Example 4.7 Normal mean,variance unknown56

Example 4.8 Comparison of gamma distributions56

Example 4.9 Unacceptable conditioning56

Example 4.10 Location model57

Example 4.11 Normal mean,variance unknown(ctd)59

Example 4.12 Normal variance59

Example 4.13 Normal mean,variance unknown(ctd)60

Example 4.14 Components of variance61

Example 5.1 Exchange paradox67

Example 5.2 Two measuring instruments(ctd)68

Example 5.3 Rainy days in Gothenburg70

Example 5.4 The normal mean(ctd)71

Example 5.5 The noncentral chi-squared distribution74

Example 5.6 A set of binomial probabilities74

Example 5.7 Exponential regression75

Example 5.8 Components of variance(ctd)80

Example 5.9 Bias assessment82

Example 5.10 Selective reporting86

Example 5.11 Precision-based choice of sample size89

Example 5.12 Sampling the Poisson process90

Example 5.13 Multivariate normal distributions92

Example 6.1 Location model(ctd)98

Example 6.2 Exponential family98

Example 6.3 Transformation to near location form99

Example 6.4 Mixed parameterization of the exponential family112

Example 6.5 Proportional hazards Weibull model113

Example 6.6 A right-censored normal distribution118

Example 6.7 Random walk with an absorbing barrier119

Example 6.8 Curved exponential family model121

Example 6.9 Covariance selection model123

Example 6.10 Poisson-distributed signal with estimated background124

Example 7.1 An unbounded likelihood134

Example 7.2 Uniform distribution135

Example 7.3 Densities with power-law contact136

Example 7.4 Model of hidden periodicity138

Example 7.5 A special nonlinear regression139

Example 7.6 Informative nonresponse140

Example 7.7 Integer normal mean143

Example 7.8 Mixture of two normal distributions144

Example 7.9 Normal-theory linear model with many parameters145

Example 7.10 A non-normal illustration146

Example 7.11 Parametric model for right-censored failure data149

Example 7.12 A fairly general stochastic process151

Example 7.13 Semiparametric model for censored failure data151

Example 7.14 Lag one correlation of a stationary Gaussian time series153

Example 7.15 A long binary sequence153

Example 7.16 Case-control study154

Example 8.1 A new observation from a normal distribution162

Example 8.2 Exponential family165

Example 8.3 Correlation between different estimates165

Example 8.4 The sign test166

Example 8.5 Unbiased estimate of standard deviation167

Example 8.6 Summarization of binary risk comparisons171

Example 8.7 Brownian motion174

Example 9.1 Two-by-two contingency table190

1 Preliminaries1

Summary1

1.1 Starting point1

1.2 Role of formal theory of inference3

1.3 Some simple models3

1.4 Formulation of objectives7

1.5 Two broad approaches to statistical inference7

1.6 Some further discussion10

1.7 Parameters13

Notes 114

2 Some concepts and simple applications17

Summary17

2.1 Likelihood17

2.2 Sufficiency18

2.3 Exponential family20

2.4 Choice of priors for exponential family problems23

2.5 Simple frequentist discussion24

2.6 Pivots25

Notes 227

3 Significance tests30

Summary30

3.1 General remarks30

3.2 Simple significance test31

3.3 One-and two-sided tests35

3.4 Reladon with acceptance and rejection36

3.5 Formulation of alternatives and test statistics36

3.6 Relation with interval estimation40

3.7 Interpretation of significance tests41

3.8 Bayesian testing42

Notes 343

4 More complicated situations45

Summary45

4.1 General remarks45

4.2 General Bayesian formulation45

4.3 Frequentist analysis47

4.4 Some more general frequentist developments50

4.5 Some further Bayesian examples59

Notes462

5 Interpretations of uncertainty64

Summary64

5.1 General remarks64

5.2 Broad roles of probability65

5.3 Frequentist interpretation of upper limits66

5.4 Neyman-Pearson operational criteria68

5.5 Some general aspects of the frequentist approach68

5.6 Yet more on the frequentist approach69

5.7 Personalistic probability71

5.8 Impersonal degree of belief73

5.9 Reference priors76

5.10 Temporal coherency78

5.11 Degree of belief and frequency79

5.12 Statistical implementation of Bayesian analysis79

5.13 Model uncertainty84

5.14 Consistency of data and prior85

5.15 Relevance of frequentist assessment85

5.16 Sequential stopping88

5.17 A simple classification problem91

Notes 593

6 Asymptotic theory96

Summary96

6.1 General remarks96

6.2 Scalar parameter97

6.3 Multidimensional parameter107

6.4 Nuisance parameters109

6.5 Tests and model reduction114

6.6 Comparative discussion117

6.7 Profile likelihood as an information summarizer119

6.8 Constrained estimation120

6.9 Semi-asymptotic arguments124

6.10 Numerical-analytic aspects125

6.11 Higher-order asymptotics128

Notes 6130

7 Further aspects of maximum likelihood133

Summary133

7.1 Multimodal likelihoods133

7.2 Irregular form135

7.3 Singular information matrix139

7.4 Failure of model141

7.5 Unusual parameter space142

7.6 Modified likelihoods144

Notes 7159

8 Additional objectives161

Summary161

8.1 Prediction161

8.2 Decision analysis162

8.3 Point estimation163

8.4 Non-likelihood-based methods169

Notes 8175

9 Randomization-based analysis178

Summary178

9.1 General remarks178

9.2 Sampling a finite population179

9.3 Design of experiments184

Notes 9192

Appendix A：A brief history194

Appendix B：Apersonal view197

References201

Author index209

Subject index213