A comparison of three optimality criteria for observation channels
A model discrimination method for processes with different memory structure.
A Modification of the Chi-Square Test to Guard Against Special Alternatives
A Modified Maximum Likelihood Test.
A sharp analysis on the asymptotic behavior of the Durbin–Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin–Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise....
ABSTRACT - Procedures for Epidemic Alternatives.
ABSTRACT - Some Tests on Exponential Populations.
Additions and correction to “The bootstrap of the mean with arbitrary bootstrap sample”
An Approximation to the Limit Distribution of the Epps-Pulley Test Statistic for Normality.
An asymptotic representation of the likelihood ratio for irregular families of distributions in the multivariate case.
An asymptotic test for quantitative gene detection
An occupancy problem with group drawings of different sizes
Approximations and Time-Discretization in Testing One-Sided Hypotheses for the Mean of a Gaussian Process.
Asymptotic behaviour of the likelihood ratio test under presence of deviations of the model
Asymptotic behaviour of the robust test in the Rieder's model of contamination
Asymptotic distribution of the likelihood ratio test statistic in the multisample case
Asymptotic Expansion of the Power Function of the Two-Sample Binomial Test with and without Randomization.
Asymptotic stability of tests based on order statistics for the scale parameter
Asymptotics for robust MOSUM
Bayes and empirical bayes tests for the life parameter
We study the one-sided testing problem for the exponential distribution via the empirical Bayes (EB) approach. Under a weighted linear loss function, a Bayes test is established. Using the past samples, we construct an EB test and exhibit its optimal rate of convergence. When the past samples are not directly observable, we work out an EB test by using the deconvolution kernel method and obtain its asymptotic optimality.