Fence Methods in Model and Moment Condition Selection
Author: Yanhua Zhang
Publisher:
Published: 2009
Total Pages: 174
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Yanhua Zhang
Publisher:
Published: 2009
Total Pages: 174
ISBN-13:
DOWNLOAD EBOOKAuthor: Jiming Jiang
Publisher: World Scientific
Published: 2015-09-22
Total Pages: 248
ISBN-13: 9814596086
DOWNLOAD EBOOKThis book is about a recently developed class of strategies, known as the fence methods, which fits particularly well in non-conventional and complex model selection problems with practical considerations. The idea involves a procedure to isolate a subgroup of what are known as correct models, of which the optimal model is a member. This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. In particular, the criterion of optimality can incorporate consideration of practical interest, thus making model selection a real life practice.Furthermore, this book introduces a data-driven approach, called adaptive fence, which can be used in a wide range of problems involving determination of tuning parameters, or constants. Instead of relying on asymptotic theory, the fence focuses on finite-sample performance, and computation. Such features are particularly suitable to statistics in the new era.
Author: Thuan Nguyen
Publisher:
Published: 2008
Total Pages: 308
ISBN-13:
DOWNLOAD EBOOKAuthor: Jiming Jiang
Publisher: Springer Nature
Published: 2022-04-04
Total Pages: 689
ISBN-13: 3030916952
DOWNLOAD EBOOKThis book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites..
Author: Kenneth W. McAlister
Publisher:
Published: 1971
Total Pages: 60
ISBN-13:
DOWNLOAD EBOOKAuthor: United States. National Advisory Committee for Aeronautics
Publisher:
Published: 1954
Total Pages: 48
ISBN-13:
DOWNLOAD EBOOKAuthor: United States. National Aeronautics and Space Administration. Scientific and Technical Information Division
Publisher:
Published: 1970
Total Pages: 956
ISBN-13:
DOWNLOAD EBOOKAuthor: United States. National Aeronautics and Space Administration. Scientific and Technical Information Division
Publisher:
Published: 1970
Total Pages: 962
ISBN-13:
DOWNLOAD EBOOKAuthor: Lang Wu
Publisher: CRC Press
Published: 2009-11-11
Total Pages: 431
ISBN-13: 9781420074086
DOWNLOAD EBOOKAlthough standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.