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The Jackknife and Bootstrap [[electronic resource] /] / by Jun Shao, Dongsheng Tu
The Jackknife and Bootstrap [[electronic resource] /] / by Jun Shao, Dongsheng Tu
Autore Shao Jun
Edizione [1st ed. 1995.]
Pubbl/distr/stampa New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Descrizione fisica 1 online resource (XVII, 517 p.)
Disciplina 519
Collana Springer Series in Statistics
Soggetto topico Applied mathematics
Engineering mathematics
Applications of Mathematics
ISBN 1-4612-0795-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 1.1 Statistics and Their Sampling Distributions -- 1.2 The Traditional Approach -- 1.3 The Jackknife -- 1.4 The Bootstrap -- 1.5 Extensions to Complex Problems -- 1.6 Scope of Our Studies -- 2. Theory for the Jackknife -- 2.1 Variance Estimation for Functions of Means -- 2.2 Variance Estimation for Functionals -- 2.3 The Delete-d Jackknife -- 2.4 Other Applications -- 2.5 Conclusions and Discussions -- 3. Theory for the Bootstrap -- 3.1 Techniques in Proving Consistency -- 3.2 Consistency: Some Major Results -- 3.3 Accuracy and Asymptotic Comparisons -- 3.4 Fixed Sample Performance -- 3.5 Smoothed Bootstrap -- 3.6 Nonregular Cases -- 3.7 Conclusions and Discussions -- 4. Bootstrap Confidence Sets and Hypothesis Tests -- 4.1 Bootstrap Confidence Sets -- 4.2 Asymptotic Theory -- 4.3 The Iterative Bootstrap and Other Methods -- 4.4 Empirical Comparisons -- 4.5 Bootstrap Hypothesis Tests -- 4.6 Conclusions and Discussions -- 5. Computational Methods -- 5.1 The Delete-1 Jackknife -- 5.2 The Delete-d Jackknife -- 5.3 Analytic Approaches for the Bootstrap -- 5.4 Simulation Approaches for the Bootstrap -- 5.5 Conclusions and Discussions -- 6. Applications to Sample Surveys -- 6.1 Sampling Designs and Estimates -- 6.2 Resampling Methods -- 6.3 Comparisons by Simulation -- 6.4 Asymptotic Results -- 6.5 Resampling Under Imputation -- 6.6 Conclusions and Discussions -- 7. Applications to Linear Models -- 7.1 Linear Models and Regression Estimates -- 7.2 Variance and Bias Estimation -- 7.3 Inference and Prediction Using the Bootstrap -- 7.4 Model Selection -- 7.5 Asymptotic Theory -- 7.6 Conclusions and Discussions -- 8. Applications to Nonlinear, Nonparametric, and Multivariate Models -- 8.1 Nonlinear Regression -- 8.2 Generalized Linear Models -- 8.3 Cox’s Regression Models -- 8.4 Kernel Density Estimation -- 8.5 Nonparametric Regression -- 8.6 Multivariate Analysis -- 8.7 Conclusions and Discussions -- 9. Applications to Time Series and Other Dependent Data -- 9.1 m-Dependent Data -- 9.2 Markov Chains -- 9.3 Autoregressive Time Series -- 9.4 Other Time Series -- 9.5 Stationary Processes -- 9.6 Conclusions and Discussions -- 10. Bayesian Bootstrap and Random Weighting -- 10.1 Bayesian Bootstrap -- 10.2 Random Weighting -- 10.3 Random Weighting for Functional and Linear Models -- 10.4 Empirical Results for Random Weighting -- 10.5 Conclusions and Discussions -- Appendix A. Asymptotic Results -- A.1 Modes of Convergence -- A.2 Convergence of Transformations -- A.4 The Borel-Cantelli Lemma -- A.5 The Law of Large Numbers -- A.6 The Law of the Iterated Logarithm -- A.7 Uniform Integrability -- A.8 The Central Limit Theorem -- A.9 The Berry-Esséen Theorem -- A.10 Edgeworth Expansions -- A.11 Cornish-Fisher Expansions -- Appendix B. Notation -- References -- Author Index.
Record Nr. UNINA-9910480734103321
Shao Jun  
New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Jackknife and Bootstrap [[electronic resource] /] / by Jun Shao, Dongsheng Tu
The Jackknife and Bootstrap [[electronic resource] /] / by Jun Shao, Dongsheng Tu
Autore Shao Jun
Edizione [1st ed. 1995.]
Pubbl/distr/stampa New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Descrizione fisica 1 online resource (XVII, 517 p.)
Disciplina 519
Collana Springer Series in Statistics
Soggetto topico Applied mathematics
Engineering mathematics
Applications of Mathematics
ISBN 1-4612-0795-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 1.1 Statistics and Their Sampling Distributions -- 1.2 The Traditional Approach -- 1.3 The Jackknife -- 1.4 The Bootstrap -- 1.5 Extensions to Complex Problems -- 1.6 Scope of Our Studies -- 2. Theory for the Jackknife -- 2.1 Variance Estimation for Functions of Means -- 2.2 Variance Estimation for Functionals -- 2.3 The Delete-d Jackknife -- 2.4 Other Applications -- 2.5 Conclusions and Discussions -- 3. Theory for the Bootstrap -- 3.1 Techniques in Proving Consistency -- 3.2 Consistency: Some Major Results -- 3.3 Accuracy and Asymptotic Comparisons -- 3.4 Fixed Sample Performance -- 3.5 Smoothed Bootstrap -- 3.6 Nonregular Cases -- 3.7 Conclusions and Discussions -- 4. Bootstrap Confidence Sets and Hypothesis Tests -- 4.1 Bootstrap Confidence Sets -- 4.2 Asymptotic Theory -- 4.3 The Iterative Bootstrap and Other Methods -- 4.4 Empirical Comparisons -- 4.5 Bootstrap Hypothesis Tests -- 4.6 Conclusions and Discussions -- 5. Computational Methods -- 5.1 The Delete-1 Jackknife -- 5.2 The Delete-d Jackknife -- 5.3 Analytic Approaches for the Bootstrap -- 5.4 Simulation Approaches for the Bootstrap -- 5.5 Conclusions and Discussions -- 6. Applications to Sample Surveys -- 6.1 Sampling Designs and Estimates -- 6.2 Resampling Methods -- 6.3 Comparisons by Simulation -- 6.4 Asymptotic Results -- 6.5 Resampling Under Imputation -- 6.6 Conclusions and Discussions -- 7. Applications to Linear Models -- 7.1 Linear Models and Regression Estimates -- 7.2 Variance and Bias Estimation -- 7.3 Inference and Prediction Using the Bootstrap -- 7.4 Model Selection -- 7.5 Asymptotic Theory -- 7.6 Conclusions and Discussions -- 8. Applications to Nonlinear, Nonparametric, and Multivariate Models -- 8.1 Nonlinear Regression -- 8.2 Generalized Linear Models -- 8.3 Cox’s Regression Models -- 8.4 Kernel Density Estimation -- 8.5 Nonparametric Regression -- 8.6 Multivariate Analysis -- 8.7 Conclusions and Discussions -- 9. Applications to Time Series and Other Dependent Data -- 9.1 m-Dependent Data -- 9.2 Markov Chains -- 9.3 Autoregressive Time Series -- 9.4 Other Time Series -- 9.5 Stationary Processes -- 9.6 Conclusions and Discussions -- 10. Bayesian Bootstrap and Random Weighting -- 10.1 Bayesian Bootstrap -- 10.2 Random Weighting -- 10.3 Random Weighting for Functional and Linear Models -- 10.4 Empirical Results for Random Weighting -- 10.5 Conclusions and Discussions -- Appendix A. Asymptotic Results -- A.1 Modes of Convergence -- A.2 Convergence of Transformations -- A.4 The Borel-Cantelli Lemma -- A.5 The Law of Large Numbers -- A.6 The Law of the Iterated Logarithm -- A.7 Uniform Integrability -- A.8 The Central Limit Theorem -- A.9 The Berry-Esséen Theorem -- A.10 Edgeworth Expansions -- A.11 Cornish-Fisher Expansions -- Appendix B. Notation -- References -- Author Index.
Record Nr. UNINA-9910789347303321
Shao Jun  
New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Jackknife and Bootstrap / / by Jun Shao, Dongsheng Tu
The Jackknife and Bootstrap / / by Jun Shao, Dongsheng Tu
Autore Shao Jun
Edizione [1st ed. 1995.]
Pubbl/distr/stampa New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Descrizione fisica 1 online resource (XVII, 517 p.)
Disciplina 519
Collana Springer Series in Statistics
Soggetto topico Mathematics
Applications of Mathematics
ISBN 1-4612-0795-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 1.1 Statistics and Their Sampling Distributions -- 1.2 The Traditional Approach -- 1.3 The Jackknife -- 1.4 The Bootstrap -- 1.5 Extensions to Complex Problems -- 1.6 Scope of Our Studies -- 2. Theory for the Jackknife -- 2.1 Variance Estimation for Functions of Means -- 2.2 Variance Estimation for Functionals -- 2.3 The Delete-d Jackknife -- 2.4 Other Applications -- 2.5 Conclusions and Discussions -- 3. Theory for the Bootstrap -- 3.1 Techniques in Proving Consistency -- 3.2 Consistency: Some Major Results -- 3.3 Accuracy and Asymptotic Comparisons -- 3.4 Fixed Sample Performance -- 3.5 Smoothed Bootstrap -- 3.6 Nonregular Cases -- 3.7 Conclusions and Discussions -- 4. Bootstrap Confidence Sets and Hypothesis Tests -- 4.1 Bootstrap Confidence Sets -- 4.2 Asymptotic Theory -- 4.3 The Iterative Bootstrap and Other Methods -- 4.4 Empirical Comparisons -- 4.5 Bootstrap Hypothesis Tests -- 4.6 Conclusions and Discussions -- 5. Computational Methods -- 5.1 The Delete-1 Jackknife -- 5.2 The Delete-d Jackknife -- 5.3 Analytic Approaches for the Bootstrap -- 5.4 Simulation Approaches for the Bootstrap -- 5.5 Conclusions and Discussions -- 6. Applications to Sample Surveys -- 6.1 Sampling Designs and Estimates -- 6.2 Resampling Methods -- 6.3 Comparisons by Simulation -- 6.4 Asymptotic Results -- 6.5 Resampling Under Imputation -- 6.6 Conclusions and Discussions -- 7. Applications to Linear Models -- 7.1 Linear Models and Regression Estimates -- 7.2 Variance and Bias Estimation -- 7.3 Inference and Prediction Using the Bootstrap -- 7.4 Model Selection -- 7.5 Asymptotic Theory -- 7.6 Conclusions and Discussions -- 8. Applications to Nonlinear, Nonparametric, and Multivariate Models -- 8.1 Nonlinear Regression -- 8.2 Generalized Linear Models -- 8.3 Cox’s Regression Models -- 8.4 Kernel Density Estimation.-8.5 Nonparametric Regression -- 8.6 Multivariate Analysis -- 8.7 Conclusions and Discussions -- 9. Applications to Time Series and Other Dependent Data -- 9.1 m-Dependent Data -- 9.2 Markov Chains -- 9.3 Autoregressive Time Series -- 9.4 Other Time Series -- 9.5 Stationary Processes -- 9.6 Conclusions and Discussions -- 10. Bayesian Bootstrap and Random Weighting -- 10.1 Bayesian Bootstrap -- 10.2 Random Weighting -- 10.3 Random Weighting for Functional and Linear Models -- 10.4 Empirical Results for Random Weighting -- 10.5 Conclusions and Discussions -- Appendix A. Asymptotic Results -- A.1 Modes of Convergence -- A.2 Convergence of Transformations -- A.4 The Borel-Cantelli Lemma -- A.5 The Law of Large Numbers -- A.6 The Law of the Iterated Logarithm -- A.7 Uniform Integrability -- A.8 The Central Limit Theorem -- A.9 The Berry-Esséen Theorem -- A.10 Edgeworth Expansions -- A.11 Cornish-Fisher Expansions -- Appendix B. Notation -- References -- Author Index.
Record Nr. UNINA-9910961075203321
Shao Jun  
New York, NY : , : Springer New York : , : Imprint : Springer, , 1995
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui