Small area estimation and microsimulation modelling

著者

書誌事項

Small area estimation and microsimulation modelling

by Azizur Rahman, Ann Harding

CRC Press, c2017

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注記

"A Chapman & Hall book"

Includes bibliographical references (p. 295-320) and index

内容説明・目次

内容説明

Small Area Estimation and Microsimulation Modeling is the first practical handbook that comprehensively presents modern statistical SAE methods in the framework of ultramodern spatial microsimulation modeling while providing the novel approach of creating synthetic spatial microdata. Along with describing the necessary theories and their advantages and limitations, the authors illustrate the practical application of the techniques to a large number of substantive problems, including how to build up models, organize and link data, create synthetic microdata, conduct analyses, yield informative tables and graphs, and evaluate how the findings effectively support the decision making processes in government and non-government organizations. Features Covers both theoretical and applied aspects for real-world comparative research and regional statistics production Thoroughly explains how microsimulation modeling technology can be constructed using available datasets for reliable small area statistics Provides SAS codes that allow readers to utilize these latest technologies in their own work. This book is designed for advanced graduate students, academics, professionals and applied practitioners who are generally interested in small area estimation and/or microsimulation modeling and dealing with vital issues in social and behavioural sciences, applied economics and policy analysis, government and/or social statistics, health sciences, business, psychology, environmental and agriculture modeling, computational statistics and data simulation, spatial statistics, transport and urban planning, and geospatial modeling. Dr Azizur Rahman is a Senior Lecturer in Statistics and convenor of the Graduate Program in Applied Statistics at the Charles Sturt University, and an Adjunct Associate Professor of Public Health and Biostatistics at the University of Canberra. His research encompasses small area estimation, applied economics, microsimulation modeling, Bayesian inference and public health. He has more than 60 scholarly publications including two books. Dr. Rahman's research is funded by the Australian Federal and State Governments, and he serves on a range of editorial boards including the International Journal of Microsimulation (IJM). Professor Ann Harding, AO is an Emeritus Professor of Applied Economics and Social Policy at the National Centre for Social and Economic Modelling (NATSEM) of the University of Canberra. She was the founder and inaugural Director of this world class Research Centre for more than sixteen years, and also a co-founder of the International Microsimulation Association (IMA) and served as the inaugural elected president of IMA from 2004 to 2011. She is a fellow of the Academy of the Social Sciences in Australia. She has more than 300 publications including several books in microsimulation modeling.

目次

Table of Contents Preface Introduction Introduction Main Aims of the Book Guide for the Reader Concluding Remarks Small Area Estimation Introduction Small area estimation Advantages of small area estimation Why small area estimation techniques? Applications of small area estimation Approaches to small area estimation Direct estimation Horvitz-Thomposn (H-T) estimator Generalized regression (GREG) estimator Modified direct estimator Design-based model-assited estimators A comparison of direct estimators Concluding remarks Indirect Estimation: Statistical Approaches Introduction Implicit models approach Synthetic estimaton Composite estimation Demographic estimation Comparison of various implicit models based indirect estimation Explicit models approach Basic area level model Basic unit leve model General linear mixed model Comparison of various explicit models based indirect estimation Methods for estimating explicit models E-BLUP approach EB approach HB approach A comparison of three methods Concluding remarks Indirect Estimation: Geographic Approaches Introduction Microsimulation modeling Process of microsimulation Types of microsimulation models Advantages of microsimulation modeling Methodologies in microsimulation modeling technology Techniques for creating spatial microdata Statistical data matching or fusion Iterative proportional fitting Repeated weighting method Reweighting Combinatorial optimisation reweighing approach The simulated annealing method in CO An illustration of CO process for hypothetical data Reweighting: The GREGWT approach Theoretical setting How does GREGWT generate new weights? Explicit numerical solution for a hypothetical data A comparison between GREGWT and CO Concluding remarks Bayesian Prediction-Based Microdata Simulation Introduction The basic steps The Bayesian prediction theory The multivariate model The prior and posterior distributions The linkage model Prediction for moedling unobserved population units Concluding remarks Microsimulation Modelling Technology for Small Area Estimation Introduction Data sources and issues The Census Data Survey Datasets Survey Datasets MMT based Model Specification Model inputs Generating small area synthetic weights Model inputs Generating small area synthetic weights Model inputs Gnerating small area synthetic weights Model outputs Housing stress Definition Measures of housing stress A comparison of various measures Small area estimation of housing stress Inputs at the second stae model Final model outputs Concluding remarks Applications of the Methodologies Introduction Results of the model: A general view Model accuracy report Scenarios of housing stress under various measures Distribution of housing stress estimation Lorenz curve for housing stress estimates Proportional cumulative frequency graph and index of dissimilarity Scenarios of households and housing stress by tenures Estimation of households in housing stress by spatial scales Results for different states Results for various statistical divisions Results for various statistical subdivisions Small area estimates: Number of households in housing stress Estimated numbers of overall households in housing stress Estimated numbers of buyerhouseholds in housing stress Estimated numbers of public renter households in housing stress Estimated numbers of private renter households in housing stress Estimated numbers of total renter households in housing stress Small area estimates: Percentage of households in housing stress Percentage estimates of housing stress for overall households Percentage estimates of housing stress for buyer households Percentage estimates of housing stress for public renter households Percentage estimates of housing stress for private renter households Percentage estimates of housing stress for total renter households Concluding remarks Analysis of Small Area Estimates in Capital Cities Introduction Scenarios of the results for major capital cities Trends in housing stress for some major cities Mapping the estimates at SLA levels within major cities Sydney Housing stress estimates for overall households Small area estimation by household's tenure types Melbourne Housing stress estimates for overall households Small area estimation by household's tenure types Brisbane Housing stress estimates for overall households Small area estimation by household's tenure types Adelaide Housing stress estimates for overall households Small area estimation by household's tenure types Canberra Housing stress estimates for overall households Small area estimation by household's tenure types Hobart Housing stress estimates for overall households Small area estimation by household's tenure types Darwin Housing stress estimates for overall households Small area estimation by household's tenure types Concluding remarks Validation and Measure of Statistical Reliability Introduction Some validation methods in the literature New approaches to validating housing stress estimation Statistical significance test of the MMT estimates Results of the statistical significance test Absolute standardised residual estimate (ASRE) analysis Results from the ASRE analysis Measure of statistical reliability of the MMT estimates Confidence interval estimation Results from the estimates of confidence intervals Concluding remarks Conclusions and Computing Codes Introduction Summary of major findings Limitations Areas of further studies Computing codes and programming The general model file codes SAS programming for reweithing algorithms The second stage program file codes Concluding remarks Appendices.

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