Advances in Statistical Modeling and Inference_ Essays in Honor of Kjell a Doksum (2007)

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    This volume covers a broad range of contemporary topics in statistical modeling and inference. There have been many exciting developments in the field of Statistics over the last quarter century, stimulated by the rapid advances in computing and data-measurement technologies. The increased computing power and availability of large datasets have led to considerable new research in flexible modeling, semiparametric and nonparametric methods, and computationally-intensive techniques. These developments have allowed us to move away from parametric techniques that rely on restrictive assumptions to much more flexible methods for modeling and analysis of data. There is also extensive use of simulation and Monte Carlo techniques for doing statistical inference. This book provides an overview of some of these advances as well as description of new research in methodology and theory.
    There are 32 chapters written by leading international researchers on a broad range of topics: semiparametric methods, transformation models, nonparametric regression, rank-based inference, mixture models, survival and reliability analysis, Bayesian inference, resampling methods, and inference under constraints. Researchers, graduate students, as well as practitioners will find the volume to be useful.

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