A Pseudo Empirical Likelihood Approach to Nonignorable Nonresponse PDF Download
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Author: Quan Hong
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Book Description
Author: Quan Hong
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Book Description
Author: Fang Fang
Publisher:
ISBN:
Category :
Languages : en
Pages : 130
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Book Description
Author: Ewa Joanna Kabzińska
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: David E. McNabb
Publisher: SAGE Publications
ISBN: 1483323757
Category : Social Science
Languages : en
Pages : 272
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Book Description
A welcome and much-needed addition to the literature on survey data quality in social research, McNabb’s book examines the most common sources of nonsampling error: frame error; measurement error; response error, nonresponse error, and interviewer error. Offering the only comprehensive and non-technical treatment available, the book’s focus on controlling error shows readers how to eliminate the opportunity for error to occur, and features revealing examples of past and current efforts to control the incidence and effects of nonsampling error. Most importantly, it gives readers the tools they need to understand, identify, address, and prevent the most prevalent and difficult-to-control types of survey errors.
Author: Ewa Joanna Kabzinska
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Changbao Wu
Publisher: Springer Nature
ISBN: 3030442462
Category : Social Science
Languages : en
Pages : 371
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Book Description
The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sampling. Basic knowledge of probability theory and statistical inference is assumed, but no prior exposure to survey sampling is required. The first part focuses on the design-based approach to finite population sampling. It contains a rigorous coverage of basic sampling designs, related estimation theory, model-based prediction approach, and model-assisted estimation methods. The second part stems from original research conducted by the authors as well as important methodological advances in the field during the past three decades. Topics include calibration weighting methods, regression analysis and survey weighted estimating equation (EE) theory, longitudinal surveys and generalized estimating equations (GEE) analysis, variance estimation and resampling techniques, empirical likelihood methods for complex surveys, handling missing data and non-response, and Bayesian inference for survey data. The third part provides guidance and tools on practical aspects of large-scale surveys, such as training and quality control, frame construction, choices of survey designs, strategies for reducing non-response, and weight calculation. These procedures are illustrated through real-world surveys. Several specialized topics are also discussed in detail, including household surveys, telephone and web surveys, natural resource inventory surveys, adaptive and network surveys, dual-frame and multiple frame surveys, and analysis of non-probability survey samples. This book is a self-contained introduction to survey sampling that provides a strong theoretical base with coverage of current research trends and pragmatic guidance and tools for conducting surveys.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 804
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Book Description
Author: Geraldo da Silva e Souza
Publisher:
ISBN:
Category :
Languages : en
Pages : 63
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Book Description
Estimation and hypothesis testing are considered for a system of simultaneous, monlinear, implicit equations. These problems are studied in a general setting. A given objective function, the pseudo likelihood, defines an estimator. Conditions are set forth such that this estimator is consistent and asymptotically normaly distributed. The Wald's test and analogs of the lagrange multiplier test and the likelihood ratio test are derived from this estimator and their null and non-null distributions are given. To illustrate the theory, results are applied in three instances: maximum likelihood estimation in simultaneous nonlinear systems, single equation nonlinear explicit models, and seemingly unrelated nonlinear regression models.
Author: Raghunath Arnab
Publisher: Academic Press
ISBN: 0128118970
Category : Mathematics
Languages : en
Pages : 932
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Book Description
Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students. Covers a wide spectrum of topics on survey sampling and statistics Serves as an ideal text for graduate students and researchers in survey sampling theory and applications Contains material on recent developments in survey sampling not covered in other books Illustrates theories using numerical examples and exercises
Author: Jae Kwang Kim
Publisher: CRC Press
ISBN: 1000466299
Category : Mathematics
Languages : en
Pages : 380
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Book Description
Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.
