Abstract #301766

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JSM 2003 Abstract #301766
Activity Number: 396
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301766
Title: Principles for Good Statistical Analyses
Author(s): Bruce G. Lindsay*+
Companies: Pennsylvania State University
Address: 422 J. Thomas Bldg., University Park, PA, 16802-2112,
Keywords: mixture models ; quadratic distance ; component labels
Abstract:

Our thesis is that the foremost statistical principle is for the user of statistics to ask two basic questions. The first question is, Is the model I use a reasonable description of the data generating mechanism? Related to this point, I will discuss a collection of work by myself and others on assessing the fit of mixture models in high dimensions using quadratic distance kernels. The second basic question is, Is the methodology I use well-behaved within the model as well as in reasonable approximations to it? Within the model, Bayes and likelihood methods are both generally well behaved within models provided adequate averaging is done. Related to this point, I will discuss the labeling problem in K-component mixture models. Although the labels are, per se, not identifiable except in asymptopia, we will demonstrate a device for creating labels in the K-component mixture model that has uses in both Bayes and bootstrap simulation based inference, thereby making possible reasonable assessments of uncertainty in the component parameters.

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