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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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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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Flexible Imputation of Missing Data, Second Edition

restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The;

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Flexible Imputation of Missing Data, Second Edition

restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The;

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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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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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Missing-Data Methods

- Missing Data Methods: Time-series Methods and Applications (Part B) The papers in this volume cover topics in the econometric approach to missing;

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Flexible Imputation of Missing Data

for multiple imputation to enter mainstream statistics. Computers and software are now potent enough to do the required calculations with;

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Time Series Analysis

Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the;

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Multiple Imputation in Practice

sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be;

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Multiple Imputation in Practice

imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware;

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Handbook of Statistical Data Editing and Imputation

, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values * More;

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Foundations of Time Series Analysis and Prediction Theory

This book provides a mathematical foundation for prediction theory and time series analysis using the idea of regression (prediction) and;

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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 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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Handbook of Missing Data Methodology

both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for;

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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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Estimation and Prediction for Certain Models of Spatial Time Series

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Estimation and Prediction for Certain Models of Spatial Time Series

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Microarray Bioinformatics

, data normalization, and missing value imputation, as well as the statistical analysis of the data and the most popular computer tools to;

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Theory and Applications of Time Series Analysis

forecasting, and time series analysis and prediction in other real-world problems. The book offers readers valuable insights into the different;

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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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Time Series Modelling with Unobserved Components

the software for implementing UCMs. The book's first part discusses introductory time series and prediction theory. Unlike most other;

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Time Series Modelling with Unobserved Components

the software for implementing UCMs. The book's first part discusses introductory time series and prediction theory. Unlike most other books;

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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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State-Space Methods for Time Series Analysis

offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible;

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