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
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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 Library.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 Library.
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 Complete-Data 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
- Complete-Case and Available-Case Analysis, Including Weighting Methods
- Complete-Case Analysis
- Weighted Complete-Case Analysis
- Available-Case 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 Filled-in Data Set
- Standard Errors for Imputed Data by Resampling
- Introduction to Multiple Imputation
- Comparison of Resampling Methods and Multiple Imputation
- Likelihood-Based Approaches to the Analysis of Missing Data
- Theory of Inference Based on the Likelihood Function
- Review of Likelihood-Based Estimation for Complete Data
- Likelihood-Based Inference with Incomplete Data
- Missing-Data 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 Missing-Data 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
- Large-sample 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 missing-data mechanism
- Factored likelihood methods, ignoring the missing-data 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 repeated-measures 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 : small-sample 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 missing-data models
- Likelihood theory for nonignorable models
- Models with known nonignorable missing-data mechanisms : grouped and rounded data
- Normal selection models
- Normal pattern-mixture models
- Nonignorable models for normal repeated-measures data
- Nonignorable models for categorical data
- Models for partially classified contingency tables, ignoring the missing-data 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 non-normal data with missing values, ignoring the missing-data 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
- IT-FiEUI
- 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 Complete-Data 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
- Complete-Case and Available-Case Analysis, Including Weighting Methods
- Complete-Case Analysis
- Weighted Complete-Case Analysis
- Available-Case 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 Filled-in Data Set
- Standard Errors for Imputed Data by Resampling
- Introduction to Multiple Imputation
- Comparison of Resampling Methods and Multiple Imputation
- Likelihood-Based Approaches to the Analysis of Missing Data
- Theory of Inference Based on the Likelihood Function
- Review of Likelihood-Based Estimation for Complete Data
- Likelihood-Based Inference with Incomplete Data
- Missing-Data 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 Missing-Data 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
- Large-sample 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 missing-data mechanism
- Factored likelihood methods, ignoring the missing-data 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 repeated-measures 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 : small-sample 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 missing-data models
- Likelihood theory for nonignorable models
- Models with known nonignorable missing-data mechanisms : grouped and rounded data
- Normal selection models
- Normal pattern-mixture models
- Nonignorable models for normal repeated-measures data
- Nonignorable models for categorical data
- Models for partially classified contingency tables, ignoring the missing-data 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 non-normal data with missing values, ignoring the missing-data 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 Complete-Data 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
- Complete-Case and Available-Case Analysis, Including Weighting Methods
- Complete-Case Analysis
- Weighted Complete-Case Analysis
- Available-Case 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 Filled-in Data Set
- Standard Errors for Imputed Data by Resampling
- Introduction to Multiple Imputation
- Comparison of Resampling Methods and Multiple Imputation
- Likelihood-Based Approaches to the Analysis of Missing Data
- Theory of Inference Based on the Likelihood Function
- Review of Likelihood-Based Estimation for Complete Data
- Likelihood-Based Inference with Incomplete Data
- Missing-Data 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 Missing-Data 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
- Large-sample 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 missing-data mechanism
- Factored likelihood methods, ignoring the missing-data 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 repeated-measures 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 : small-sample 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 missing-data models
- Likelihood theory for nonignorable models
- Models with known nonignorable missing-data mechanisms : grouped and rounded data
- Normal selection models
- Normal pattern-mixture models
- Nonignorable models for normal repeated-measures data
- Nonignorable models for categorical data
- Models for partially classified contingency tables, ignoring the missing-data 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 non-normal data with missing values, ignoring the missing-data 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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