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Handbook of Missing Data Methodology

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have;

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Missing-Data Methods

This Book Set consists of: *9781780525242 - Missing Data Methods: Cross-sectional Methods and Applications (Part A) *9781780525266;

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Missing Data

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables;

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Missing Data Methods

Volume 27 of Advances in Econometrics , entitled Missing Data Methods , contains 16 chapters authored by specialists in the field;

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Missing Data Methods

Volume 27 of Advances in Econometrics , entitled Missing Data Methods , contains 16 chapters authored by specialists in the field;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Preventing and Treating Missing Data in Longitudinal Clinical Trials

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have;

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Preventing and Treating Missing Data in Longitudinal Clinical Trials

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have;

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Multiple Imputation of Missing Data in Practice

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple;

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Semiparametric Theory and Missing Data

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and;

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Deep Learning and Missing Data in Engineering Systems

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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Statistical Analysis with Missing Data

Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current;

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Applying Quantitative Bias Analysis to Epidemiologic Data

improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new;

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Applied Missing Data Analysis in the Health Sciences

This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field;

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Statistical Methods for Handling Incomplete Data

in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and;

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Mixed Effects Models for Complex Data

approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews;

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Mixed Effects Models for Complex Data

approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews;

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Strategies for Data Analysis with Two Types of Missing Values

Missing data, an issue frequently encountered in data analysis, causes difficulties with estimation, precision and inference. Methods for;

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Data Analysis & Statistics for Nursing Research

is given to quantitative methods. Other features include management of data, how to "clean" data, and how to work around missing data.

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Bayesian Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Bayesian Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Flexible Imputation of Missing Data, Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly;

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Flexible Imputation of Missing Data, Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly;

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Missing Data In Clinical Studies

Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are;

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Growth Curves

experiments, covering time-moving covariates, exchangable errors, bioassay results, missing data procedures and nonparametric and Bayesian methods.;

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Analysis of Incomplete Multivariate Data

, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data;

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