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Small Area Estimation

Praise for the First Edition This pioneering work, in which Rao provides a comprehensive and up-to-date treatment of small area estimation;

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A Course on Small Area Estimation and Mixed Models

of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets;

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Introduction to Small Area Estimation Techniques: A Practical Guide for National Statistics Offices

This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It;

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Spectral Analysis of Signals

-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from;

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Missing and Modified Data in Nonparametric Estimation

samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be;

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Missing and Modified Data in Nonparametric Estimation

explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing;

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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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Small Area Population Estimation

Small Area Population Estimation is een boek van Shuo-Sheng Wu;

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Missing Data and Small-Area Estimation

This book evolved from lectures, courses and workshops on missing data and small-area estimation that I presented during my tenure as the;

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Missing Data and Small-Area Estimation

This book evolved from lectures, courses and workshops on missing data and small-area estimation that I presented during my tenure as the;

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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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Small Area Estimation and Microsimulation Modeling

Small Area Estimation and Microsimulation Modeling is the first practical handbook that comprehensively presents modern statistical SAE;

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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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Statistical Data Fusion

contains several real data ... Overall, I found that the book covers an important topic and the DRM is a promising tool in this area. Researchers;

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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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Rainfall-Runoff Relationship in a Local Small Watershed Area

hydrologists. Small catchments where hydrological data is not available and also for ungauged catchments, its difficult to apply such models. For;

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Selection Models for Nonignorable Missing Data

models for nonignorable missing data. The theory of selection models is described and illustrated by examples. Maximum Likelihood as well as;

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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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Small Area Estimation and Microsimulation Modeling

Small Area Estimation and Microsimulation Modeling is the first practical handbook that comprehensively presents modern statistical SAE;

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Statistical and Mathematical Sciences and Their Applications

mathematicians. Forecasting of crop production using econometric methods small area estimation techniques, fractional factorial plans for multi level;

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Bayesian Demographic Estimation and Forecasting

, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems;

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Bayesian Demographic Estimation and Forecasting

, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems;

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Multisensor Attitude Estimation

association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated;

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Quantitative Genetics And Its Connections With Big Data And Sequenced Genomes

relatively small sets of data. In this book, new methods of direct estimation are introduced and analyzed that are appropriate for an era of big sets;

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Quantitative Genetics And Its Connections With Big Data And Sequenced Genomes

relatively small sets of data. In this book, new methods of direct estimation are introduced and analyzed that are appropriate for an era of big sets;

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Handbook of Missing Data Methodology

been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook;

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