Small Area Estimation and Microsimulation Modeling

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.

Introduction Introduction Main Aims of the Book Guide for the Reader Concluding remarks Small Area Estimation Introduction Small area estimation Approaches to small area estimation Direct estimation Concluding remarks Indirect Estimation: Statistical Approaches IntroductionImplicit models approach Explicit models approach Methods for estimating explicit models A comparison of three methods Concluding remarks Indirect Estimation: Geographic Approaches Introduction Microsimulation modelling Methodologies in microsimulation modelling technology Combinatorial optimisation reweighting approach Reweighting: The GREGWT approach A comparison between GREGWT and CO 87Concluding remarks Bayesian Prediction-Based Microdata SimulationIntroduction The basic steps The Bayesian prediction theory The multivariate model The prior and posterior distributions The linkage model Prediction for modelling unobserved population units Concluding remarks Microsimulation Modelling Technology for Small Area EstimationIntroduction Data sources and issuesMMT-Based Model SpecificationHousing stressSmall area estimation of housing stressConcluding remarks Applications of the Methodologies Introduction Results of the model: A general viewEstimation of households in housing stress by spatial scalesSmall area estimates: Number of households in housing stressSmall area estimates: Percentage of households in housing stressConcluding remarks Analysis of Small Area Estimates in Capital Cities IntroductionSydney Melbourne Brisbane Perth AdelaideCanberra Hobart Darwin Concluding remarks Validation and Measure of Statistical Reliability Introduction Some validation methods in the literatureNew approaches to validating housing stress estimationMeasure of statistical reliability of the MMT estimatesConcluding remarks Conclusions and Computing Codes Introduction Summary of major findings Limitations Areas offurther studies Computing codes and programming Concluding remarks References Appendices

Associate Professor Azizur Rahman, PhD, is a statistician and data scientist with expertise in both developing and applying novel methodologies, models and technologies. He is the Leader of Statistics and Data Mining Research Group at the Charles Sturt University (CSU), and able to assist in understanding multi-disciplinary research issues within various fields including how to understand the individual activities which occur within very complex scientific, behavioural, socio-economic and ecological systems. His research encompasses issues in simple to multi-facet analyses in various fields ranging from the statistical sciences to the law and legal studies. He has more than 100 scholarly publications including a few books. Prof. Rahmans research is funded by the Australian Federal and State Governments, and he serves on a range of editorial boards including theInternational Journal of Microsimulation (IJM) and Sustaining Regions. He obtained several awards including the SOCM Research Excellence Award 2018 and the CSU-RED Achievement Award 2019. 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.