The authors study the problem of checking the adequacy of a parametric model for a
distribution using several possibly censored weight biased samples. They discuss …

We propose two versions of asymptotically distribution free empirical processes. When a
composite null hypothesis contains a family of distributions indexed by a one dimensional …

This paper introduces a conditional Kolmogorov test, in the spirit of Andrews (1997), that
allows for comparison of multiple misspecifed conditional distribution models, for the case of …

This paper proposes a new goodness-of-fit test for parametric conditional probability
distributions using the nonparametric smoothing methodology. An asymptotic normal …

This paper surveys selectively some of the problems that arise in checking models for
ungrouped binary data. Attention is paid to the deviance, to residuals, measures of influence …

The chi-square test of fit provides a possible and widely used method to justify what is the
underlying distribution. However real data frequently reveal at least small deviations from …

We study the problem of testing a wide range of statistical hypotheses under the assumption
of the sample being randomly right-censored. As an alternative to the classical approach …

A basic problem in any statistical modeling of a scientific dataset is to provide the 'best'fit.
Such inference is generally based on the empirical distribution function when the underlying …

In many cases, data are drawn from a population which is distributed approximately normal,
but with bounded, rather than infinite, domain. The traditional approach is a truncated …

Abstract The Khmaladze Transformation provides a general method for transforming a
parametric empirical process into an asymptotically distribution free process. Distribution …