There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.Essays in Honor of Kjell A. Doksum Vijay Nair. 6. Cheng, R. C. H. (1985). Generation of multivariate normal samples with given sample mean and covariance matrix. J. Statist. Comput. ... Michael, J. R., Schucany, W. R. and Haas , R. W. (1976).
|Title||:||Advances in Statistical Modeling and Inference|
|Publisher||:||World Scientific - 2007|