System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notations, learning just how the Kalman filter works can be a daunting task.

With its mathematically rigorous, no frills approach to the basic discrete-time Kalman filter, A Kalman Filter Primer builds a thorough understanding of the inner...

Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts, such as grid computing and massively parallel supercomputers. The Handbook of Parallel Computing and Statistics systematically applies the principles of parallel computing for solving increasingly complex problems in statistics research....

"Data Analysis of Asymmetric Structures" provides a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications and provides a wealth of new approaches in every section. It meets both the practical and theoretical needs of research professionals across a wide range of disciplines andA considers data analysis in fields such as psychology, sociology, social science, ecology, and marketing. In seven comprehensive chapters this guide details theories, methods, and models for the analysis of asymmetric structures in a variety of disciplines and...

Since publication of the first edition in 1992, the field of survey sampling has grown considerably. This new edition of Survey Sampling: Theory and Methods has been updated to include the latest research and the newest methods. The authors have undertaken the daunting task of surveying the sampling literature of the past few decades to provide an outstanding research reference. Starting with the unified theory, the authors explain in the clearest of terms the subsequent developments. In fact, even the most modern innovations of survey sampling, both methodological and theoretical, have found...

Review "Professor Mukhopadhyay has done an excellent job of collecting a broad statistical knowledge base on the various topics covered and presented them in a straightforward manner Not many books on statistical inference with such features are available currently." -- Sunil K. Dhar, New Jersey Institute of Technology " This book is designed for a one-semester course in Mathematical Statistics. In many universities, graduate students from Economics, Actuarial Sciences, Finance, and several other departments do no t have enough time to take a two-semester sequenceFor them, this book is ideal....

The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.
Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form...