Muutke küpsiste eelistusi

E-raamat: Statistical Methods for Handling Incomplete Data 2nd edition [Taylor & Francis e-raamat]

(Department of Statistics, University of Wisconsin, USA),
  • Formaat: 380 pages, 28 Tables, black and white; 6 Line drawings, black and white; 6 Illustrations, black and white
  • Ilmumisaeg: 19-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429321740
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 244,00 €
  • Säästad 30%
Statistical Methods for Handling Incomplete Data 2nd editionSuurem pilt
  • Formaat: 380 pages, 28 Tables, black and white; 6 Line drawings, black and white; 6 Illustrations, black and white
  • Ilmumisaeg: 19-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429321740
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429321740

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. This book covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.



Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Features

  • Uses the mean score equation as a building block for developing the theory for missing data analysis

  • Provides comprehensive coverage of computational techniques for missing data analysis

  • Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation

  • Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data

  • Describes a survey sampling application

  • Updated with a new chapter on Data Integration

    • Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation

    • The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

    List of Figures
    		xi
    List of Tables
    		xiii
    Preface xv
    1 Introduction
    		1(4)
    1.1 Introduction
    		1(1)
    1.2 Outline
    		2(1)
    1.3 How to Use This Book
    		3(2)
    2 Likelihood-Based Approach
    		5(26)
    2.1 Introduction
    		5(6)
    2.2 Observed Likelihood
    		11(6)
    2.3 Mean Score Function
    		17(3)
    2.4 Observed Information
    		20(11)
    3 Computation
    		31(46)
    3.1 Introduction
    		31(8)
    3.2 Factoring Likelihood Approach
    		39(8)
    3.3 EM Algorithm
    		47(11)
    3.4 Monte Carlo Computation
    		58(5)
    3.5 Monte Carlo EM
    		63(4)
    3.6 Data Augmentation
    		67(10)
    4 Imputation
    		77(28)
    4.1 Introduction
    		77(2)
    4.2 Basic Theory
    		79(9)
    4.3 Variance Estimation after Imputation
    		88(7)
    4.4 Replication Variance Estimation
    		95(10)
    5 Multiple Imputation
    		105(26)
    5.1 Review of Bayesian Inference
    		105(5)
    5.2 MI: Bayesian Justification
    		110(2)
    5.3 MI: Frequentist Justification
    		112(9)
    5.4 MI Using Mixture Models
    		121(4)
    5.5 MI for General Purpose Estimation
    		125(6)
    6 Fractional Imputation
    		131(30)
    6.1 Parametric Fractional Imputation
    		132(12)
    6.2 Nonparametric Approach
    		144(5)
    6.3 Semiparametric Fractional Imputation
    		149(2)
    6.4 FI Using Mixture Models
    		151(3)
    6.5 FI for Multivariate Categorical Data
    		154(4)
    6.6 Model Selection
    		158(3)
    7 Propensity Scoring Approach
    		161(38)
    7.1 Introduction
    		161(6)
    7.2 Regression Weighting Method
    		167(3)
    7.3 Propensity Score Method
    		170(8)
    7.4 Optimal PSEstimation
    		178(5)
    7.5 Maximum Entropy Method
    		183(4)
    7.6 Doubly Robust Estimation
    		187(4)
    7.7 Empirical Likelihood Method
    		191(3)
    7.8 Nonparametric Method
    		194(5)
    8 Nonignorable Missing Data
    		199(32)
    8.1 Model Identification
    		199(4)
    8.2 Conditional Likelihood Approach
    		203(4)
    8.3 Pseudo Likelihood Approach
    		207(2)
    8.4 GMM Approach
    		209(6)
    8.5 Exponential Tilting Model
    		215(4)
    8.6 Latent Variable Approach
    		219(2)
    8.7 Callbacks
    		221(4)
    8.8 Capture-Recapture Experiment
    		225(6)
    9 Longitudinal and Clustered Data
    		231(34)
    9.1 Ignorable Missing Data
    		231(2)
    9.2 Nonignorable Monotone Missing Data
    		233(9)
    9.2.1 Parametric Models
    		233(1)
    9.2.2 Nonparametric p(y | x)
    		234(4)
    9.2.3 Nonparametric Propensity
    		238(4)
    9.3 Past-Value-Dependent Missing Data
    		242(13)
    9.3.1 Three Different Approaches
    		242(1)
    9.3.2 Imputation Models under Past-Value-Dependent Nonmonotone Missing
    		243(3)
    9.3.3 Nonparametric Regression Imputation
    		246(1)
    9.3.4 Dimension Reduction
    		247(2)
    9.3.5 Simulation Study
    		249(2)
    9.3.6 Wisconsin Diabetes Registry Study
    		251(4)
    9.4 Random-Effect-Dependent Missing Data
    		255(10)
    9.4.1 Three Existing Approaches
    		255(3)
    9.4.2 Summary Statistics
    		258(2)
    9.4.3 Simulation Study
    		260(2)
    9.4.4 Modification of Diet in Renal Disease
    		262(3)
    10 Application to Survey Sampling
    		265(34)
    10.1 Introduction
    		265(3)
    10.2 Calibration Estimation
    		268(4)
    10.3 Propensity Score Weighting Method
    		272(5)
    10.4 Multiple Imputation
    		277(2)
    10.5 Fractional Imputation
    		279(5)
    10.6 Fractional Hot Deck Imputation
    		284(3)
    10.7 Imputation for Two-Phase Sampling
    		287(3)
    10.8 Synthetic Data Imputation
    		290(9)
    11 Data Integration
    		299(24)
    11.1 Mass Imputation
    		300(3)
    11.2 Propensity Score Method
    		303(5)
    11.3 Nonparametric Propensity Score Approach
    		308(3)
    11.4 Doubly Robust Method
    		311(2)
    11.5 Statistical Matching
    		313(2)
    11.6 Mass Imputation Using Multilevel Models
    		315(3)
    11.7 Data Integration for Regression Analysis
    		318(2)
    11.8 Record Linkage
    		320(3)
    12 Advanced Topics
    		323(20)
    12.1 Smoothing Spline Imputation
    		323(2)
    12.2 Kernel Ridge Regression Imputation
    		325(3)
    12.3 KRR-Based Propensity Score Estimation
    		328(4)
    12.4 Soft Calibration
    		332(1)
    12.5 Penalized Regression Imputation
    		333(3)
    12.6 Sufficient Dimension Reduction
    		336(3)
    12.7 Neural Network Model
    		339(4)
    Bibliography 343(18)
    Index 361
    Jae Kwang Kim is a LAS deans professor in the Department of Statistics at Iowa State University. He is a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He is the recipient of 2015 Gertude M. Cox award, sponsored by Washington Statistical Society and RTI international.

    Jun Shao is a professor in the Department of Statistics at University of Wisconsin Madison. He is a fellow of ASA and IMS, a former president of International Chinese Statistical Association and currently the founding editor of Statistical Theory and Related Fields.
    Püsilink: https://www.kriso.ee/db/9780429321740_pe.html
    Märksõnad: