Advances in Statistical Modeling and Inference: Essays in by Vijay Nair PDF

By Vijay Nair

ISBN-10: 9812703691

ISBN-13: 9789812703699

ISBN-10: 9812708294

ISBN-13: 9789812708298

There were significant advancements within the box of information over the past sector century, spurred via the swift advances in computing and data-measurement applied sciences. those advancements have revolutionized the sector and feature significantly stimulated study instructions in thought and technique. elevated computing strength has spawned totally new parts of analysis in computationally-intensive equipment, permitting us to maneuver clear of narrowly appropriate parametric innovations in accordance with restrictive assumptions to even more versatile and practical versions and techniques. those computational advances have additionally ended in the broad use of simulation and Monte Carlo options in statistical inference. All of those advancements have, in flip, influenced new examine in theoretical information. This quantity offers an updated evaluate of contemporary advances in statistical modeling and inference. Written by means of popular researchers from the world over, it discusses versatile versions, semi-parametric equipment and transformation types, nonparametric regression and blend versions, survival and reliability research, and re-sampling thoughts. With its insurance of technique and idea in addition to functions, the booklet is a necessary reference for researchers, graduate scholars, and practitioners.

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

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For instance, one may not actually believe in the lack of memory property (conditional on the present state). It is important to note, however, that the Markov property may be much less of a restriction than one thinks. This point has been made by Datta and Satten (2001) and Glidden (2002). In fact, the basic Markov tool of multiplying transition matrices often has a validity beyond the Markov framework. Basically, the multiplication of transition matrices is simply a description of the movements of individuals on the chain and does not necessarily depend on probabilistic assumptions.

The major tradition, like in most of statistics, is very pragmatic. One computes Kaplan-Meier survival curves and runs regression analyses, which are straightforward analyses of the actual observed data. However, there are also attempts at looking below the surface. One example is frailty theory, based on the recognition that some individuals have higher risk than others. Such models will rarely be identifiable for univariate survival data, but nevertheless it may yield considerable insight to speculate about the frailty effects that may be present.

From a medical viewpoint there is considerable variation in the risk of developing various diseases, and in the prognosis for patients. This variation may be due to genetics, lifestyle or other factors. Some of these factors may be controlled in a statistical analysis, while others are unknown. g. Aalen, 1994). In particular, the shape of hazard functions may be strongly influenced, so that, for instance, the observed hazard may be pulled down to increase much more slowly, or even decrease, compared to what would have been observed in a homogenous group.

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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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