this article we propose three criteria that can be used to address model selection. These emphasize observables rather than parameters and are based on a certain Bayesian predictive density. They have a unifying basis that is simple and interpretable,are free of asymptotic de#nitions,and allow the incorporation of prior information. Moreover,two of these criteria are readily calibrated.

This paper describes an approach to Bayesian sensitivity analysis that uses an influence statistic and an outlier statistic to assess the sensitivity of a model to perturbations. The basic outlier statistic is a Bayes factor, while the influence statistic depends strongly on the purpose of the analysis. The task of influence analysis is aided by having an interpretable influence statistic. Two alternative divergences, an L1 distance and a Ø 2 divergence are proposed and shown to be interpretable. The Bayes factor and the proposed influence measures are shown to be summaries of the posterior of a perturbation function. Keywords: Bayes Factor; Censoring; Conditional Predictive Ordinate; Diagnostics, Influence Analysis. 1 Introduction This paper describes an approach to Bayesian sensitivity analysis that uses an influence statistic and an outlier statistic to assess the sensitivity of a model to perturbations. Let the likelihood and prior from an initial model M 0 combine to give the ...

this paper, we propose two variable and transformation selection procedures on the predictor variables in the linear model. The first procedure is a simultaneous variable and transformation selection procedure. For data sets with many predictors, a stepwise variable selection procedure is also presented. The procedures are based on Bayesian model selection criteria introduced by Ibrahim and Laud (1994) and Laud and Ibrahim (1995). Several examples are given to illustrate the methodology.

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Power estimation and optimization is an increasingly important issue in IC design. The memory subsystem is a significant aspect, since memory power can dominate total system power. Estimation and optimization hence rely heavily on models for embedded memories. We present an effective black box modelling methology for generating nonlinear memory models automatically. The resulting models are accuracte, computationally modest, and in analytical form. They outperform linear models by far. Average absolute relative errors are below 6%.

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Abstract not found

Abstract not found

GOBANCHO HIKARI BLDG. 4 TH Floor

Summary. We propose a new type of residual and an easily computed functional form test for the Cox proportional hazards model. The proposed test is a modification of the omnibus test for testing the overall fit of a parametric regression model, developed by Stute, González Manteiga and Presedo Quindimil (1998), and is based on what we call censoring consistent residuals. In addition, we develop residual plots that can be used to identify the correct functional forms of covariates. In a simulation study we find our test statistic compares favorably with the functional form test of Lin, Wei and Ying (1993) when covariates are mildly correlated. The application of the proposed methods is illustrated using both a simulated data set and a real data set.