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Activity Number: 111
Type: Contributed
Date/Time: Monday, July 30, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #310052
Title: Impact of Censoring on Inference for the Regression Coefficient in a Bivariate Normal Model
Author(s): Richard Linder*+
Companies: Ohio Wesleyan University
Address: , Delaware, OH, 43015,
Keywords: Censoring ; Correlation ; Order Statistics ; Concomitants of Order Statistics ; Bivariate Normal Model ; Regression
Abstract:

Suppose a random sample of bivariate normal data is subjected to Type II censoring on one of the variates, so that only a set of order statistics and their concomitants are observed. Inference about the correlation parameter and the regression coefficient is described here in this context. First, properties of the maximum likelihood estimator of the regression coefficient are described and compared with those of a range-based estimator from the literature. Two simple methods for approximating power of the test for independence in this model are proposed. Finally, based on these, a method is illustrated for determining sample sizes necessary to test for independence at specified power.

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Revised September, 2007