UID:
almafu_9959243630602883
Format:
1 online resource (479 p.)
Edition:
1st ed.
ISBN:
981-238-545-2
Content:
This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of informati
Note:
Description based upon print version of record.
,
Contents; Preface; List of Tables; Chapter 1 Introduction; 1.1. Background; 1.1.1. Historical Review; 1.1.2. Eficient Criteria; 1.1.3. Consistent Criteria; 1.2. Overview; 1.2.1. Distributions; 1.2.2. Model Notation; 1.2.3. Discrepancy and Distance Measures; 1.2.4. Eficiency under Kullback-Leibler and L2; 1.2.5. Overfitting and Underfitting; 1.3. Layout; 1.4. Topics Not Covered; Chapter 2 The Univariate Regression Model; 2.1. Model Description; 2.1.1. Model Structure and Notation; 2.1.2. Distance Measures; 2.2. Derivations of the Foundation Model Selection Criteria
,
2.3. Moments of Model Selection Criteria2.3.1. AIC and AICc; 2.3.2. FPE and Cp; 2.3.3. SIC and HQ; 2.3.4. Adjusted R2, R2adj; 2.4. Signal-to-noise Corrected Variants; 2.4.1. AICu; 2.4.2. FPEu; 2.4.3. HQu; 2.5. Overfitting; 2.5.1. Small-sample Probabilities of Overfitting; 2.5.2. Asymptotic Probabilities of Overfitting; 2.5.3. Small-sample Signal-to-noise Ratios; 2.5.4. Asymptotic Signal-to-noise Ratios; 2.6. Small-sample Underfitting; 2.6.1. Distributional Review; 2.6.2. Expectations of L2 and Kullback-Leibler Distance; 2.6.3. Expected Values for Two Special Case Models
,
2.6.4. Signal-to-noise Ratios for Two Special Case Models2.6.5. Small-sample Probabilities for Two Special Case Models; 2.7. Random X Regression and Monte Carlo Study; 2.8. Summary; Appendix 2A. Distributional Results in the Central Case; Appendix 2B. Proofs of Theorems 2.1 to 2.6; Appendix 2C. Small-sample and Asymptotic Properties; Appendix 2D. Moments of the Noncentral X2; Chapter 3 The Univariate Autoregressive Model; 3.1. Model Description; 3.1.1. Autoregressive Models; 3.1.2. Distance Measures; 3.2. Selected Derivations of Model Selection Criteria; 3.2.1. AIC; 3.2.2. AICc; 3.2.3. AICu
,
3.2.4. FPE3.2.5. FPEu; 3.2.6. Cp; 3.2.7. SIC; 3.2.8. HQ; 3.2.9. HQc; 3.3. Small-sample Signal-to-noise Ratios; 3.4. Overfitting; 3.4.1. Small-sample Probabilities of Overfitting; 3.4.2. Asymptotic Probabilities of Overfitting; 3.4.3. Small-sample Signal-to-noise Ratios; 3.4.4. Asymptotic Signal-to-noise Ratios; 3.5. Underfitting for Two Special Case Models; 3.5.1. Expected Values for Two Special Case Models; 3.5.2. Signal-to-noise Ratios for Two Special Case Models; 3.5.3. Probabilities for Two Special Case Models; 3.6. Autoregressive Monte Carlo Study
,
3.7. Moving Average MA(1) Misspecified as Autoregressive Models3.7.1. Two Special Case MA(1) Models; 3.7.2. Model and Distance Measure Definitions; 3.7.3. Expected Values for Two Special Case Models; 3.7.4. Masspecified MA(1) Monte Carlo study; 3.8. Multistep Forecasting Models; 9.8.1. Kullback-Leibler Discrepancy for Multistep; 3.8.2. AICcm, AICm, and FPEm; 3.8.3. Multistep Monte Carlo Study; 3.9. Summary; Appendix 3A. Distributional Results in the Central Case; Appendix 3B. Small-sample Probabilities of Overfitting; Appendix 3C. Asymptotic Results
,
Chapter 4 The Multivariate Regression Model
,
English
Additional Edition:
ISBN 981-02-3242-X
Language:
English
Bookmarklink
