The Resource Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin
Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin
Resource Information
The item Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
 Edition
 Second edition.
 Extent
 xv, 381 pages
 Contents

 Bartlett's ANCOVA Method
 Least Squares Estimates of Missing Values by ANCOVA Using Only CompleteData Methods
 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares
 Correct Least Squares Sums of Squares with More Than One Degree of Freedom
 CompleteCase and AvailableCase Analysis, Including Weighting Methods
 CompleteCase Analysis
 Weighted CompleteCase Analysis
 AvailableCase Analysis
 Single Imputation Methods
 Imputing Means from a Predictive Distribution
 The Problem of Missing Data
 Imputing Draws from a Predictive Distribution
 Estimation of Imputation Uncertainty
 Imputation Methods that Provide Valid Standard Errors from a Single Filledin Data Set
 Standard Errors for Imputed Data by Resampling
 Introduction to Multiple Imputation
 Comparison of Resampling Methods and Multiple Imputation
 LikelihoodBased Approaches to the Analysis of Missing Data
 Theory of Inference Based on the Likelihood Function
 Review of LikelihoodBased Estimation for Complete Data
 LikelihoodBased Inference with Incomplete Data
 MissingData Patterns
 A Generally Flawed Alternative to Maximum Likelihood: Maximizing Over the Parameters and the Missing Data
 Likelihood Theory for Coarsened Data
 Mechanisms That Lead to Missing Data
 A Taxonomy of MissingData Methods
 Missing Data in Experiments
 The Exact Least Squares Solution with Complete Data
 The Correct Least Squares Analysis with Missing Data
 Filling in Least Squares Estimates
 Theory of the EM algorithm
 extensions of EM
 Hybrid maximization methods
 Largesample inference based on maximum likelihood estimates
 Standard errors based on the information matrix
 Standard errors via methods that do not require computing and inverting an estimate of the observed information matrix
 Bayes and multiple imputation
 Bayesian iterative simulation methods
 Multiple imputation
 Multivariate normal examples, ignoring the missingdata mechanism
 Factored likelihood methods, ignoring the missingdata mechanism
 Inference for a mean vector and covariance matrix with missing data under normality
 Estimation with a restricted covariance matrix
 Multiple linear regression
 A general repeatedmeasures model with missing data
 Time series models
 Robust estimation
 Robust estimation for a univariate sample
 Robust estimation of the mean and covariance matrix
 Further extensions of the t model 
 Bivariate normal data with one variable subject to nonresponse : ML estimation
 Bivariate normal monotone data : smallsample inference
 monotone data with more than two variables
 Factorizations for special nonmonotone patterns
 Maximum likelihood for general patterns of missing data : introduction and theory with ignorable nonresponse
 Alternative computational strategies  Introduction to the EM algorithm
 The E and M steps of EM
 Futher extensions of the general location model
 Nonignorable missingdata models
 Likelihood theory for nonignorable models
 Models with known nonignorable missingdata mechanisms : grouped and rounded data
 Normal selection models
 Normal patternmixture models
 Nonignorable models for normal repeatedmeasures data
 Nonignorable models for categorical data
 Models for partially classified contingency tables, ignoring the missingdata mechanism
 Factored likelihoods for monotone multinomial data
 ML and Bayes estimation for multinomial samples with general patterns of missing data
 Loglinear models for partially classified contingency tables
 mixed normal and nonnormal data with missing values, ignoring the missingdata mechanism
 The general location model
 The general location model with parameter constraints
 Regression problems involving mixtures of continuous and categorical variables
 Isbn
 9780471183860
 Label
 Statistical analysis with missing data
 Title
 Statistical analysis with missing data
 Statement of responsibility
 Roderick J.A. Little, Donald B. Rubin
 Language
 eng
 Cataloging source
 ITFiEUI
 http://library.link/vocab/creatorName
 Little, Roderick J. A
 Illustrations
 illustrations
 Index
 index present
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName
 Rubin, Donald B.
 http://library.link/vocab/subjectName

 Mathematical statistics
 Missing observations (Statistics)
 Label
 Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin
 Bibliography note
 Includes bibliography and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents

 Bartlett's ANCOVA Method
 Least Squares Estimates of Missing Values by ANCOVA Using Only CompleteData Methods
 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares
 Correct Least Squares Sums of Squares with More Than One Degree of Freedom
 CompleteCase and AvailableCase Analysis, Including Weighting Methods
 CompleteCase Analysis
 Weighted CompleteCase Analysis
 AvailableCase Analysis
 Single Imputation Methods
 Imputing Means from a Predictive Distribution
 The Problem of Missing Data
 Imputing Draws from a Predictive Distribution
 Estimation of Imputation Uncertainty
 Imputation Methods that Provide Valid Standard Errors from a Single Filledin Data Set
 Standard Errors for Imputed Data by Resampling
 Introduction to Multiple Imputation
 Comparison of Resampling Methods and Multiple Imputation
 LikelihoodBased Approaches to the Analysis of Missing Data
 Theory of Inference Based on the Likelihood Function
 Review of LikelihoodBased Estimation for Complete Data
 LikelihoodBased Inference with Incomplete Data
 MissingData Patterns
 A Generally Flawed Alternative to Maximum Likelihood: Maximizing Over the Parameters and the Missing Data
 Likelihood Theory for Coarsened Data
 Mechanisms That Lead to Missing Data
 A Taxonomy of MissingData Methods
 Missing Data in Experiments
 The Exact Least Squares Solution with Complete Data
 The Correct Least Squares Analysis with Missing Data
 Filling in Least Squares Estimates
 Theory of the EM algorithm
 extensions of EM
 Hybrid maximization methods
 Largesample inference based on maximum likelihood estimates
 Standard errors based on the information matrix
 Standard errors via methods that do not require computing and inverting an estimate of the observed information matrix
 Bayes and multiple imputation
 Bayesian iterative simulation methods
 Multiple imputation
 Multivariate normal examples, ignoring the missingdata mechanism
 Factored likelihood methods, ignoring the missingdata mechanism
 Inference for a mean vector and covariance matrix with missing data under normality
 Estimation with a restricted covariance matrix
 Multiple linear regression
 A general repeatedmeasures model with missing data
 Time series models
 Robust estimation
 Robust estimation for a univariate sample
 Robust estimation of the mean and covariance matrix
 Further extensions of the t model 
 Bivariate normal data with one variable subject to nonresponse : ML estimation
 Bivariate normal monotone data : smallsample inference
 monotone data with more than two variables
 Factorizations for special nonmonotone patterns
 Maximum likelihood for general patterns of missing data : introduction and theory with ignorable nonresponse
 Alternative computational strategies  Introduction to the EM algorithm
 The E and M steps of EM
 Futher extensions of the general location model
 Nonignorable missingdata models
 Likelihood theory for nonignorable models
 Models with known nonignorable missingdata mechanisms : grouped and rounded data
 Normal selection models
 Normal patternmixture models
 Nonignorable models for normal repeatedmeasures data
 Nonignorable models for categorical data
 Models for partially classified contingency tables, ignoring the missingdata mechanism
 Factored likelihoods for monotone multinomial data
 ML and Bayes estimation for multinomial samples with general patterns of missing data
 Loglinear models for partially classified contingency tables
 mixed normal and nonnormal data with missing values, ignoring the missingdata mechanism
 The general location model
 The general location model with parameter constraints
 Regression problems involving mixtures of continuous and categorical variables
 Control code
 FIEb17163092
 Dimensions
 24 cm.
 Edition
 Second edition.
 Extent
 xv, 381 pages
 Isbn
 9780471183860
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code

 n
 Other physical details
 illustrations
 System control number
 (OCoLC)48466746
 Label
 Statistical analysis with missing data, Roderick J.A. Little, Donald B. Rubin
 Bibliography note
 Includes bibliography and index
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents

 Bartlett's ANCOVA Method
 Least Squares Estimates of Missing Values by ANCOVA Using Only CompleteData Methods
 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares
 Correct Least Squares Sums of Squares with More Than One Degree of Freedom
 CompleteCase and AvailableCase Analysis, Including Weighting Methods
 CompleteCase Analysis
 Weighted CompleteCase Analysis
 AvailableCase Analysis
 Single Imputation Methods
 Imputing Means from a Predictive Distribution
 The Problem of Missing Data
 Imputing Draws from a Predictive Distribution
 Estimation of Imputation Uncertainty
 Imputation Methods that Provide Valid Standard Errors from a Single Filledin Data Set
 Standard Errors for Imputed Data by Resampling
 Introduction to Multiple Imputation
 Comparison of Resampling Methods and Multiple Imputation
 LikelihoodBased Approaches to the Analysis of Missing Data
 Theory of Inference Based on the Likelihood Function
 Review of LikelihoodBased Estimation for Complete Data
 LikelihoodBased Inference with Incomplete Data
 MissingData Patterns
 A Generally Flawed Alternative to Maximum Likelihood: Maximizing Over the Parameters and the Missing Data
 Likelihood Theory for Coarsened Data
 Mechanisms That Lead to Missing Data
 A Taxonomy of MissingData Methods
 Missing Data in Experiments
 The Exact Least Squares Solution with Complete Data
 The Correct Least Squares Analysis with Missing Data
 Filling in Least Squares Estimates
 Theory of the EM algorithm
 extensions of EM
 Hybrid maximization methods
 Largesample inference based on maximum likelihood estimates
 Standard errors based on the information matrix
 Standard errors via methods that do not require computing and inverting an estimate of the observed information matrix
 Bayes and multiple imputation
 Bayesian iterative simulation methods
 Multiple imputation
 Multivariate normal examples, ignoring the missingdata mechanism
 Factored likelihood methods, ignoring the missingdata mechanism
 Inference for a mean vector and covariance matrix with missing data under normality
 Estimation with a restricted covariance matrix
 Multiple linear regression
 A general repeatedmeasures model with missing data
 Time series models
 Robust estimation
 Robust estimation for a univariate sample
 Robust estimation of the mean and covariance matrix
 Further extensions of the t model 
 Bivariate normal data with one variable subject to nonresponse : ML estimation
 Bivariate normal monotone data : smallsample inference
 monotone data with more than two variables
 Factorizations for special nonmonotone patterns
 Maximum likelihood for general patterns of missing data : introduction and theory with ignorable nonresponse
 Alternative computational strategies  Introduction to the EM algorithm
 The E and M steps of EM
 Futher extensions of the general location model
 Nonignorable missingdata models
 Likelihood theory for nonignorable models
 Models with known nonignorable missingdata mechanisms : grouped and rounded data
 Normal selection models
 Normal patternmixture models
 Nonignorable models for normal repeatedmeasures data
 Nonignorable models for categorical data
 Models for partially classified contingency tables, ignoring the missingdata mechanism
 Factored likelihoods for monotone multinomial data
 ML and Bayes estimation for multinomial samples with general patterns of missing data
 Loglinear models for partially classified contingency tables
 mixed normal and nonnormal data with missing values, ignoring the missingdata mechanism
 The general location model
 The general location model with parameter constraints
 Regression problems involving mixtures of continuous and categorical variables
 Control code
 FIEb17163092
 Dimensions
 24 cm.
 Edition
 Second edition.
 Extent
 xv, 381 pages
 Isbn
 9780471183860
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code

 n
 Other physical details
 illustrations
 System control number
 (OCoLC)48466746
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