C. B. Bell

New Lilliefors and Srinivasan tables with applications

Two approaches to the problem of goodness-of-fit with nuisance parameters are presented in this paper, both based on modifications of the Kolmogorov-Smirnov statistics. Improved tables of critical values originally computed by Lilliefors and Srinivasan are presented in the normal and exponential cases. Also given are tables for the uniform case, normal with known mean and normal with known variance. All tables were computed using Monte Carlo simulation with sample size n = 20000.

Inference for goodness-of-fit problems with nuisance parameters: (Applications to signal detection)

Abstract Signal detection problems are treated in the context of several Gaussian and non-Gaussian stochastic processes with one or more nuisance parameters. The techniques all entail a basic data transformation and the use of a Kolmogorov-type statistic on oneor more components of the transformed data. Extraneous statistical noise and randomized rank statistics are employed.

Algunos métodos generales para la construcción de tests paramétricos y no paramétricos.

ResumenSe construyen algunos tests para varias hipótesis paramétricas y no paramétricas. La metodología utiliza un nuevo sistema de coordinados, cuyos componentes son el estadístico suficiente mínimo y un estadístico independiente y complementario a tal estadístico.AbstractTests are constructed for various parametric and non-parametric hypotheses. The methodology is based on a new coordinate system, whose components are the relevant minimal sufficient statistic, and a statistic independent of and complementary to that statistic.

Discrimination, signal detection, and estimation for Weibull-type Poisson processes

Discrimination, signal detection, and estimation in the context of Weibull-type nonhomogeneous Poisson processes are considered for four different sampling plans: Type I and Type II censoring and same-shape and equal-distance sampling. Procedures based on likelihood functions and on extensions of the Kolmogorov-Smirnov statistic are developed. The results are pertinent to signal detection problems in biomedicine (e.g., EKG and EEG monitoring) and engineering. >

A characterization of exponential distributions based on order statistics

This note contains a characterization of exponential distributions based on the properties of linear transformations of order statistics. This is a certain converse of a well known theorem of Rényi about the distribution of linear combinations of order statistics from exponential distributions. Some statistical applications of the result are indicated.