Comparing Mean Vectors Via Generalized Inference in Multivariate Log-Normal Distributions

Inference of Several Log-normal Distributions

This research considers several log-normal distributions when variances are heteroscedastic and group sizes are unequal. We proposed fiducial generalized pivotal quantities (FGPQ)-based simultaneous confidence intervals for ratios of the means. We also proved that the constructed confidence intervals have correct asymptotic coverage. Simulation results show that the proposed methods work well. ...

Multivariate extremes of generalized skew-normal distributions

We explore extremal properties of a family of skewed distributions extended from the multivariate normal distribution by introducing a skewing function π . We give sufficient conditions on the skewing function for the pairwise asymptotic independence to hold. We apply our results to a special case of the bivariate skew-normal distribution and finally support our conclusions by a simulation stud...

Inference on Difference of Means of two Log-Normal Distributions A Generalized Approach

Over the past decades, various methods for comparing the means of two log-normal have been proposed. Some of them are differing in terms of how the statistic test adjust to accept or to reject the null hypothesis. In this study, a new method of test for comparing the means of two lognormal populations is given through the generalized measure of evidence to have against the null hypothesis. Howe...

Comparing the Shape Parameters of Two Weibull Distributions Using Records: A Generalized Inference

The Weibull distribution is a very applicable model for the lifetime data. For inference about two Weibull distributions using records, the shape parameters of the distributions are usually considered equal. However, there is not an appropriate method for comparing the shape parameters in the literature. Therefore, comparing the shape parameters of two Weibull distributions is very important. I...

Equivalence Testing for Mean Vectors of Multivariate Normal Populations

Learning Multivariate Log-concave Distributions

We study the problem of estimating multivariate log-concave probability density functions. We prove the first sample complexity upper bound for learning log-concave densities on Rd, for all d ≥ 1. Prior to our work, no upper bound on the sample complexity of this learning problem was known for the case of d > 3. In more detail, we give an estimator that, for any d ≥ 1 and ǫ > 0, draws Õd ( (1/ǫ...