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1.

図書
図書
1. Chance & choice : memorabilia
Kai Lai Chung
出版情報: New Jersey : World Scientific, c2004  ix, 304 p., [12] p. of plates ; 26 cm
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2.

図書
図書
2. Inference for change-point and post-change means after a CUSUM test
Yanhong Wu
出版情報: New York, N.Y. : Springer, c2005  xiii, 158 p. ; 24 cm
シリーズ名: Lecture notes in statistics ; 180
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目次情報: 続きを見る
CUSUM Procedure
Change-point Estimation
Confidence Interval for Change-point
Infrence for Post-change Mean
Estimation After False Signal
Inference with Change in Variance
Sequential Classification and Segmentation
An Adaptive CUSUM Procedure
Dependent Observation Case
Other Methods and Remarks
CUSUM Procedure
Change-point Estimation
Confidence Interval for Change-point
3.

電子ブック
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3. Introduction to empirical processes and semiparametric inference (: electronic bk)
Michael R. Kosorok
出版情報: [Berlin] : SpringerLink, [20--]  1 online resource (xiv, 483 p.)
シリーズ名: Springer series in statistics
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目次情報: 続きを見る
Preface
Overview / I：
Introduction / 1：
An Overview of Empirical Processes / 2：
The Main Features / 2.1：
Empirical Process Techniques / 2.2：
Stochastic Convergence / 2.2.1：
Entropy for Glivenko-Cantelli and Donsker Theorems / 2.2.2：
Bootstrapping Empirical Processes / 2.2.3：
The Functional Delta Method / 2.2.4：
Z-Estimators / 2.2.5：
M-Estimators / 2.2.6：
Other Topics / 2.3：
Exercises / 2.4：
Notes / 2.5：
Overview of Semiparametric Inference / 3：
Semiparametric Models and Efficiency / 3.1：
Score Functions and Estimating Equations / 3.2：
Maximum Likelihood Estimation / 3.3：
Case Studies I / 3.4：
Linear Regression / 4.1：
Mean Zero Residuals / 4.1.1：
Median Zero Residuals / 4.1.2：
Counting Process Regression / 4.2：
The General Case / 4.2.1：
The Cox Model / 4.2.2：
The Kaplan-Meier Estimator / 4.3：
Efficient Estimating Equations for Regression / 4.4：
Simple Linear Regression / 4.4.1：
A Poisson Mixture Regression Model / 4.4.2：
Partly Linear Logistic Regression / 4.5：
Empirical Processes / 4.6：
Introduction to Empirical Processes / 5：
Preliminaries for Empirical Processes / 6：
Metric Spaces / 6.1：
Outer Expectation / 6.2：
Linear Operators and Functional Differentiation / 6.3：
Proofs / 6.4：
Stochastic Processes in Metric Spaces / 6.5：
Weak Convergence / 7.2：
General Theory / 7.2.1：
Spaces of Bounded Functions / 7.2.2：
Other Modes of Convergence / 7.3：
Empirical Process Methods / 7.4：
Maximal Inequalities / 8.1：
Orlicz Norms and Maxima / 8.1.1：
Maximal Inequalities for Processes / 8.1.2：
The Symmetrization Inequality and Measurability / 8.2：
Glivenko-Cantelli Results / 8.3：
Donsker Results / 8.4：
Entropy Calculations / 8.5：
Uniform Entropy / 9.1：
VC-Classes / 9.1.1：
BUEI Classes / 9.1.2：
Bracketing Entropy / 9.2：
Glivenko-Cantelli Preservation / 9.3：
Donsker Preservation / 9.4：
The Bootstrap for Donsker Classes / 9.5：
An Unconditional Multiplier Central Limit Theorem / 10.1.1：
Conditional Multiplier Central Limit Theorems / 10.1.2：
Bootstrap Central Limit Theorems / 10.1.3：
Continuous Mapping Results / 10.1.4：
The Bootstrap for Glivenko-Cantelli Classes / 10.2：
A Simple Z-Estimator Master Theorem / 10.3：
Additional Empirical Process Results / 10.4：
Bounding Moments and Tail Probabilities / 11.1：
Sequences of Functions / 11.2：
Contiguous Alternatives / 11.3：
Sums of Independent but not Identically Distributed Stochastic Processes / 11.4：
Central Limit Theorems / 11.4.1：
Bootstrap Results / 11.4.2：
Function Classes Changing with n / 11.5：
Dependent Observations / 11.6：
Main Results and Proofs / 11.7：
Examples / 12.2：
Composition / 12.2.1：
Integration / 12.2.2：
Product Integration / 12.2.3：
Inversion / 12.2.4：
Other Mappings / 12.2.5：
Consistency / 12.3：
The General Setting / 13.2：
Using Donsker Classes / 13.2.2：
A Master Theorem and the Bootstrap / 13.2.3：
Using the Delta Method / 13.3：
The Argmax Theorem / 13.4：
Rate of Convergence / 14.2：
Regular Euclidean M-Estimators / 14.4：
Non-Regular Examples / 14.5：
A Change-Point Model / 14.5.1：
Monotone Density Estimation / 14.5.2：
Case Studies II / 14.6：
Partly Linear Logistic Regression Revisited / 15.1：
The Two-Parameter Cox Score Process / 15.2：
The Proportional Odds Model Under Right Censoring / 15.3：
Nonparametric Maximum Likelihood Estimation / 15.3.1：
Existence / 15.3.2：
Score and Information Operators / 15.3.3：
Weak Convergence and Bootstrap Validity / 15.3.5：
Testing for a Change-point / 15.4：
Large p Small n Asymptotics for Microarrays / 15.5：
Assessing P-Value Approximations / 15.5.1：
Consistency of Marginal Empirical Distribution Functions / 15.5.2：
Inference for Marginal Sample Means / 15.5.3：
Semiparametric Inference / 15.6：
Introduction to Semiparametric Inference / 16：
Preliminaries for Semiparametric Inference / 17：
Projections / 17.1：
Hilbert Spaces / 17.2：
More on Banach Spaces / 17.3：
Tangent Sets and Regularity / 17.4：
Efficiency / 18.2：
Optimality of Tests / 18.3：
Efficient Inference for Finite-Dimensional Parameters / 18.4：
Efficient Score Equations / 19.1：
Profile Likelihood and Least-Favorable Submodels / 19.2：
The Cox Model for Right Censored Data / 19.2.1：
The Proportional Odds Model for Right Censored Data / 19.2.2：
The Cox Model for Current Status Data / 19.2.3：
Inference / 19.2.4：
Quadratic Expansion of the Profile Likelihood / 19.3.1：
The Profile Sampler / 19.3.2：
The Penalized Profile Sampler / 19.3.3：
Other Methods / 19.3.4：
Efficient Inference for Infinite-Dimensional Parameters / 19.4：
Semiparametric Maximum Likelihood Estimation / 20.1：
Weighted and Nonparametric Bootstraps / 20.2：
The Piggyback Bootstrap / 20.2.2：
Semiparametric M-Estimation / 20.2.3：
Semiparametric M-estimators / 21.1：
Motivating Examples / 21.1.1：
General Scheme for Semiparametric M-Estimators / 21.1.2：
Consistency and Rate of Convergence / 21.1.3：
[radical]n Consistency and Asymptotic Normality / 21.1.4：
Weighted M-Estimators and the Weighted Bootstrap / 21.2：
Entropy Control / 21.3：
Examples Continued / 21.4：
Cox Model with Current Status Data (Example 1, Continued) / 21.4.1：
Binary Regression Under Misspecified Link Function (Example 2, Continued) / 21.4.2：
Mixture Models (Example 3, Continued) / 21.4.3：
Penalized M-estimation / 21.5：
Two Other Examples / 21.5.1：
Case Studies III / 21.6：
The Proportional Odds Model Under Right Censoring Revisited / 22.1：
Efficient Linear Regression / 22.2：
Temporal Process Regression / 22.3：
A Partly Linear Model for Repeated Measures / 22.4：
References / 22.5：
Author Index
List of symbols
Subject Index
Preface
Overview / I：
Introduction / 1：
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