Download e-book for kindle: Advances in Statistical Modeling and Inference: Essays in by Vijay Nair

By Vijay Nair

ISBN-10: 9812703691

ISBN-13: 9789812703699

ISBN-10: 9812708294

ISBN-13: 9789812708298

There were significant advancements within the box of facts during the last area century, spurred by way of the fast advances in computing and data-measurement applied sciences. those advancements have revolutionized the sector and feature tremendously stimulated learn instructions in idea and technique. elevated computing strength has spawned totally new parts of study in computationally-intensive equipment, permitting us to maneuver clear of narrowly acceptable parametric recommendations according to restrictive assumptions to even more versatile and lifelike versions and strategies. those computational advances have additionally resulted in the huge use of simulation and Monte Carlo suggestions in statistical inference. All of those advancements have, in flip, prompted new examine in theoretical data. This quantity offers an updated evaluation of contemporary advances in statistical modeling and inference. Written through well known researchers from the world over, it discusses versatile versions, semi-parametric equipment and transformation types, nonparametric regression and mix types, survival and reliability research, and re-sampling options. With its assurance of method and concept in addition to functions, the ebook is an important reference for researchers, graduate scholars, and practitioners.

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Additional info for Advances in Statistical Modeling and Inference: Essays in Honor of Kjell a Doksum (Series in Biostatistics) (Series in Biostatistics)

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It is reason to December 14, 2006 14:14 World Scientific Review Volume - 9in x 6in main-test Stochastic Processes in Survival Analysis 39 believe that the standard frailty models, with all frailty placed at time zero and perfectly remembered, exaggerates the effects of frailty. We shall give examples illustrating this issue. 1 Special case 1: Standard frailty model We assume a(t, v) ≡ 1. Let r(t) be equal to ρ up to time T and 0 after this time, and assume that λ(t) is equal to 0 up to time T .

1983). Maximum likelihood estimation in a latent variable problem. Pp. 31-65 in Studies in Econometrics, Time Series and Multivariate Statistics. (Eds. S. Karlin, T. A. Goodman). Academic Press, New York. 7. , Grosse, E. M. (1992). Local regression models. Pp. 309-376 in Statistical Models in S. Eds. J. M. Chambers and T. J. Hastie. Wadsworth, Pacific Grove. 8. Doksum, K. A. (1966). Distribution-free statistics based on normal deviates in analysis of variance. Rev. Inter. Statist. Inst. 34, 376-388.

Dyte, D. and Clarke, S. R. (2000) A ratings based Poisson model for World Cup soccer simulation. J. Operational Research Soc. 51, 993-998. 12. Fienberg, S. E. (1980). The Analysis of Cross-classified Data. MIT Press, Cambridge. 13. Harville, D. (2003). The selection or seeding of college basketball or football teams for postseason competition. J. American Statistical Assoc. 98, 17-27. 14. Karlis D. and Ntzoufras J. (2000). On modelling soccer data. Student 3, 229-245. 15. Karlis, D. and Ntzoufras, J.

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Advances in Statistical Modeling and Inference: Essays in Honor of Kjell a Doksum (Series in Biostatistics) (Series in Biostatistics) by Vijay Nair


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