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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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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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Multiple Imputation of Missing Data Using SAS

Find guidance on using SAS for multiple imputation and solving common missing data issues.Multiple Imputation of Missing Data Using SAS;

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Flexible Imputation of Missing Data

"""Preface We are surrounded by missing data. Problems created by missing data in statistical analysis have long been swept under the;

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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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Multiple Imputation in Practice

helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct;

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Multiple Imputation in Practice

book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with;

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Handbook of Statistical Data Editing and Imputation

A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data;

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Missing Data Methods

, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;

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Missing Data Methods

, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;

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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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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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Applied Multiple Imputation

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the;

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Applied Multiple Imputation: Advantages, Pitfalls, New Developments and Applications in R

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the;

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Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation;

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Applied Missing Data Analysis in the Health Sciences

traditional techniques for missing data inference including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods and applies the;

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Methodologies for Time Series Prediction and Missing Value Imputation

prediction and missing value imputation. Accurate prediction of future values is heavily dependent not only on a good model, which is well trained;

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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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Missing Data

missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation;

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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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Discovering Knowledge In Data

analytics, and statistical analysis. * Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing;

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Deep Learning and Missing Data in Engineering Systems

the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep;

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Survey Methodology and Missing Data

This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and;

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Missing Data

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data;

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Selection Models for Nonignorable Missing Data

An introduction to missing data in statistical applications is given in the beginning. The main part of the book deals with selection;

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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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