Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope...
Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a ...
A. K. MD Ehsanes Saleh Mohammad Arashi S. M. M. Tabatabaey
This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors
Includes a wide array of applications for the analysis of multivariate observations
Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics
Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic
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This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models w...
This book discusses current methods of estimation in linear models. In particular, the authors address the methodology of linear multiple regression models that plays an important role in almost every scientific investigations in various fields, including economics, engineering, and biostatistics. The standard estimation method for regression parameters is the ordinary least square (OLS) principal where residual squared errors are minimized. Applied statisticians are often not satisfied with OLS estimators when the design matrix is ill-conditioned, leading to a multicollinearity problem...
This book discusses current methods of estimation in linear models. In particular, the authors address the methodology of linear multiple regressio...