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Small Area Estimation With Unmatched Sampling And Linking Models


Small Area Estimation With Unmatched Sampling And Linking Models
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Small Area Estimation With Unmatched Sampling And Linking Models


Small Area Estimation With Unmatched Sampling And Linking Models
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Author : Yong You
language : en
Publisher:
Release Date : 2000

Small Area Estimation With Unmatched Sampling And Linking Models written by Yong You and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Sampling (Statistics) categories.


In small area estimation, a sampling error model on transformed survey estimates (e.g., log-transformation) and a corresponding linking model on the transformed population means are often used. This leads to a linear mixed effects combined model on the transformed estimates, and standard results in empirical Bayes (EB) or hiererchical Bayes (HB) can be applied to get improved small area estimates. But the assumption of zero mean sampling errors may not be valid in small samples due to nonlinear transformation. To avoid this difficulty, we propose a sampling error model on the original survey estimates and then combine it with the linking model on the transformed population means using a complete HB approach. A numerical study is conducted to compare the posterior means and posterior variances under the complete HB approach with those obtained under the customary linear mixed model approach.



Small Area Estimation


Small Area Estimation
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Author : J. N. K. Rao
language : en
Publisher: John Wiley & Sons
Release Date : 2005-02-25

Small Area Estimation written by J. N. K. Rao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-25 with Mathematics categories.


An accessible introduction to indirect estimation methods, both traditional and model-based. Readers will also find the latest methods for measuring the variability of the estimates as well as the techniques for model validation. Uses a basic area-level linear model to illustrate the methods Presents the various extensions including binary response data through generalized linear models and time series data through linear models that combine cross-sectional and time series features Provides recent applications of SAE including several in U.S. Federal programs Offers a comprehensive discussion of the design issues that impact SAE



Small Area Estimation


Small Area Estimation
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Author : J. N. K. Rao
language : en
Publisher: John Wiley & Sons
Release Date : 2015-08-10

Small Area Estimation written by J. N. K. Rao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-10 with Mathematics categories.


Praise for the First Edition "This pioneering work, in which Rao provides a comprehensive and up-to-date treatment of small area estimation, will become a classic...I believe that it has the potential to turn small area estimation...into a larger area of importance to both researchers and practitioners." —Journal of the American Statistical Association Written by two experts in the field, Small Area Estimation, Second Edition provides a comprehensive and up-to-date account of the methods and theory of small area estimation (SAE), particularly indirect estimation based on explicit small area linking models. The model-based approach to small area estimation offers several advantages including increased precision, the derivation of "optimal" estimates and associated measures of variability under an assumed model, and the validation of models from the sample data. Emphasizing real data throughout, the Second Edition maintains a self-contained account of crucial theoretical and methodological developments in the field of SAE. The new edition provides extensive accounts of new and updated research, which often involves complex theory to handle model misspecifications and other complexities. Including information on survey design issues and traditional methods employing indirect estimates based on implicit linking models, Small Area Estimation, Second Edition also features: Additional sections describing the use of R code data sets for readers to use when replicating applications Numerous examples of SAE applications throughout each chapter, including recent applications in U.S. Federal programs New topical coverage on extended design issues, synthetic estimation, further refinements and solutions to the Fay-Herriot area level model, basic unit level models, and spatial and time series models A discussion of the advantages and limitations of various SAE methods for model selection from data as well as comparisons of estimates derived from models to reliable values obtained from external sources, such as previous census or administrative data Small Area Estimation, Second Edition is an excellent reference for practicing statisticians and survey methodologists as well as practitioners interested in learning SAE methods. The Second Edition is also an ideal textbook for graduate-level courses in SAE and reliable small area statistics.



Mixed Effects Models And Small Area Estimation


Mixed Effects Models And Small Area Estimation
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Author : Shonosuke Sugasawa
language : en
Publisher: Springer Nature
Release Date : 2023-02-02

Mixed Effects Models And Small Area Estimation written by Shonosuke Sugasawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Mathematics categories.


This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.



Introduction To Small Area Estimation Techniques


Introduction To Small Area Estimation Techniques
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Author : Asian Development Bank
language : en
Publisher: Asian Development Bank
Release Date : 2020-05-01

Introduction To Small Area Estimation Techniques written by Asian Development Bank and has been published by Asian Development Bank this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-01 with Business & Economics categories.


This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.



Small Area Estimation And Microsimulation Modeling


Small Area Estimation And Microsimulation Modeling
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Author : Azizur Rahman
language : en
Publisher: CRC Press
Release Date : 2016-11-30

Small Area Estimation And Microsimulation Modeling written by Azizur Rahman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-30 with Mathematics categories.


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.



Small Area Estimation


Small Area Estimation
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Author : David Alan Marker
language : en
Publisher:
Release Date : 1995

Small Area Estimation written by David Alan Marker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.




Analysis Of Poverty Data By Small Area Estimation


Analysis Of Poverty Data By Small Area Estimation
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Author : Monica Pratesi
language : en
Publisher: John Wiley & Sons
Release Date : 2016-02-23

Analysis Of Poverty Data By Small Area Estimation written by Monica Pratesi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-23 with Mathematics categories.


A comprehensive guide to implementing SAE methods for poverty studies and poverty mapping There is an increasingly urgent demand for poverty and living conditions data, in relation to local areas and/or subpopulations. Policy makers and stakeholders need indicators and maps of poverty and living conditions in order to formulate and implement policies, (re)distribute resources, and measure the effect of local policy actions. Small Area Estimation (SAE) plays a crucial role in producing statistically sound estimates for poverty mapping. This book offers a comprehensive source of information regarding the use of SAE methods adapted to these distinctive features of poverty data derived from surveys and administrative archives. The book covers the definition of poverty indicators, data collection and integration methods, the impact of sampling design, weighting and variance estimation, the issue of SAE modelling and robustness, the spatio-temporal modelling of poverty, and the SAE of the distribution function of income and inequalities. Examples of data analyses and applications are provided, and the book is supported by a website describing scripts written in SAS or R software, which accompany the majority of the presented methods. Key features: Presents a comprehensive review of SAE methods for poverty mapping Demonstrates the applications of SAE methods using real-life case studies Offers guidance on the use of routines and choice of websites from which to download them Analysis of Poverty Data by Small Area Estimation offers an introduction to advanced techniques from both a practical and a methodological perspective, and will prove an invaluable resource for researchers actively engaged in organizing, managing and conducting studies on poverty.



National Patterns Of R D Resources


National Patterns Of R D Resources
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2013-08-09

National Patterns Of R D Resources written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-09 with Social Science categories.


National Patterns of R&D Resources is an annual report issued by the National Center for Science and Engineering Statistics (NCSES) of the National Science Foundation, which provides a national view of current 'patterns' in funding of R&D activities in government, industry, academia, federally funded research and development centers, and non-profits. Total R&D funds are broken out at the national level by type of provider, type of recipient, and whether the R&D is basic, applied, or developmental. These patterns are compared both longitudinally versus historical R&D amounts, and internationally. This report series, which is based on input from several censuses and surveys, is used to formulate policies that, e.g., might increase incentives to support different types, sources, or recipients of R&D than is currently the case. To communicate these R&D patterns, each report is composed of a set of tabulations of national R&D disaggregated by type of donor, type of recipient, and type of R&D. While this satisfies many key user groups, the question was whether some modifications of the report could attract a wider user community and at the same time provide more useful information for current users. National Patterns of R&D Resources: Future Directions for Content and Methods addresses the following questions: (1) what additional topics and tabulations could be presented without modifying the current portfolio of R&D censuses and surveys, (2) what additional topics and tabulations might be presented by expanding these current data collections, (3) what could be done to enhance international comparability of the tabulations, (4) since much of the information on non-profit R&D providers and recipients is estimated from 15 year-old data, what impact might this be having on the quality of the associated National Patterns tabulations, (5) what statistical models could be used to support the issuance R&D estimates at state-level and geographic regions below the national level, (6) what use could be made from the recent development of administrative sources of R&D information, and finally, (7) what graphical tools could be added to the current tabulations to enhance the communication of R&D patterns to the users of this series of publications.



Big Data Meets Survey Science


Big Data Meets Survey Science
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Author : Craig A. Hill
language : en
Publisher: John Wiley & Sons
Release Date : 2020-09-29

Big Data Meets Survey Science written by Craig A. Hill and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-29 with Social Science categories.


Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.