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作者：BEDRICK, EJ; HILL, JR
摘要：We consider exact conditional methods for identifying outliers in logistic regression data. Tests for a single outlier and multiple outliers are developed assuming a logistic slippage model. The p-values for these tests are determined using an explicit enumeration of all possible responses consistent with the observed value of the sufficient statistic. Justifications are given for preferring this computationally intensive approach to standard methods based on asymptotic approximations. The tec...
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作者：WEI, LJ; YING, Z; LIN, DY
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Washington; University of Washington Seattle
摘要：Recently linear rank statistics with censored data have been used as the estimating functions for the regression parameters in the linear model with an unspecified error distribution. The resulting rank estimators are consistent and asymptotically normal. However, the asymptotic variances of these estimators are complicated and are difficult to estimate well with censored data. In this paper, we propose some simple methods for making inference about a subset of the regression coefficients whil...
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作者：POLSON, N; WASSERMAN, L
摘要：We derive prior distributions for the bivariate binomial model using Bernardo''s (1979) method. These priors are compared to the Jeffreys to a prior and prior proposed by Crowder and Sweeting (1989). The priors possess desirable symmetry properties since we allow them to depend on the parameter of interest.
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作者：LEE, SY
摘要：This paper considers the multilevel analysis of structural equation models with unbalanced sampling designs. The analysis is based on the maximum likelihood and the generalized least squares approaches. Basic statistical results for inference such as the asymptotic distribution of the estimators, goodness-of-fit test statistics for the validity of the model and functional constraints are developed. Computationally, the application of the scoring algorithm and the Gauss-Newton algorithm are dis...
