Titlebook: On Model Uncertainty and its Statistical Implications; Proceedings of a Wor Theo K. Dijkstra Conference proceedings 1988 Springer-Verlag Be

Cession

书目名称On Model Uncertainty and its Statistical Implications影响因子(影响力)




书目名称On Model Uncertainty and its Statistical Implications影响因子(影响力)学科排名




书目名称On Model Uncertainty and its Statistical Implications网络公开度




书目名称On Model Uncertainty and its Statistical Implications网络公开度学科排名




书目名称On Model Uncertainty and its Statistical Implications被引频次




书目名称On Model Uncertainty and its Statistical Implications被引频次学科排名




书目名称On Model Uncertainty and its Statistical Implications年度引用




书目名称On Model Uncertainty and its Statistical Implications年度引用学科排名




书目名称On Model Uncertainty and its Statistical Implications读者反馈




书目名称On Model Uncertainty and its Statistical Implications读者反馈学科排名

敲诈

Conference proceedings 1988 experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. O

Lineage

Data-Driven Selection of Regressors and the Bootstrap,he usual unbiased estimate of the residual variance obtained by fitting all columns; and a is a positive number, often 2 but α = 1 or α = log n have also surfaced in the literature. The value of (1) is calculated for those sets of columns one is willing to consider and that model is chosen for which the criterion is minimal.

懒惰人民

原告

Conference proceedings 1988thers, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

antidepressant

意见一致

acolyte

真繁荣

assent

Model Selection in Multinomial Experiments,he . p, with p. = n./N, and the . prop(n.,...,n.). Another way of expressing this is that for each N we have defined a random vector .., taking values in .., the unit simplex in ... We use the convention of underlining random variables in this paper. Thus .. is a sequence of random vectors.