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Estimation theory

Most popular at the top

  • Bootstrap Methods and their Applicationby A. C. Davison; D. V. Hinkley

    Cambridge University Press 1997; US$ 82.00

    This book on statistical methods was first published in 1997 and has code on a supporting website. more...

  • An Introduction to Bootstrap Methods with Applications to Rby Michael R. Chernick; Robert A. LaBudde

    Wiley 2014; US$ 118.00 US$ 102.27

    A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully... more...

  • Theory of Preliminary Test and Stein-Type Estimation with Applicationsby A. K. Md. Ehsanes Saleh

    Wiley 2006; US$ 186.00 US$ 161.20

    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... more...

  • Multivariate Density Estimationby David W. Scott

    Wiley 2015; US$ 115.00 US$ 99.67

    Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory... more...

  • Quasi-Likelihood And Its Applicationby Christopher C. Heyde

    Springer New York 2008; US$ 127.37

    This book is concerned with the general theory of optimal estimation of - rameters in systems subject to random e?ects and with the application of this theory. The focus is on choice of families of estimating functions, rather than the estimators derived therefrom, and on optimization within these families. Only assumptions about means and covariances... more...

  • Empirical Likelihoodby Art B. Owen

    CRC Press 2001; US$ 125.95

    Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also... more...

  • Introduction to Robust Estimation and Hypothesis Testingby Rand R. Wilcox

    Elsevier Science 2005; US$ 124.95

    This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background... more...

  • Restricted Parameter Space Estimation Problemsby Constance van Eeden

    Springer New York 2006; US$ 124.07

    This monograph is addressed to anyone interested in the subject of restrict- parameter-space estimation, and in particular to those who want to learn, or bring their knowledge up to date, about (in)admissibility and minimaxity problems for such parameter spaces. The coverage starts in the early 1950s when the subject of inference for - stricted parameter... more...

  • Semiparametric Theory and Missing Databy Anastasios Tsiatis

    Springer New York 2007; US$ 148.61

    This book summarizes current knowledge of the theory of estimation for semiparametric models with missing data, applying modern methods to missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible. more...

  • Stochastic Processes and Filtering Theoryby Andrew H. Jazwinski

    Elsevier Science 1970; US$ 99.95

    This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been collected... more...