We introduce estimation and test procedures through divergence optimization for discrete or continuous parametric models. This approach is based on a new dual representation for divergences. We treat point estimation and tests for simple and composite hypotheses, extending maximum likelihood technique. An other view at the maximum likelihood approach, for estimation and test, is given. We prove...

Privatization which refers to the transition of ownership and management of state-owned enterprises to the private sector, is one of the main drivers of economic prosperity. In this paper, the performance of the state, private, and privatized banks have been compared through examining the effects of privatization in the banking system on its performance. The data from 11 banks during the 13-yea...

BACKGROUND Gene, protein, or metabolite expression levels are often non-normally distributed, heavy tailed and contain outliers. Standard statistical approaches may fail as location tests in this situation. OBJECTIVES In three Monte-Carlo simulation studies, we aimed at comparing the type I error levels and empirical power of standard location tests and three adaptive tests [O'Gorman, Can J S...

This paper compares empirical type I error and power of different tests that have been proposed to assess the homogeneity of within-group variances, prior to anova. The tests of homogeneity of variance (THV) compared in this study are: Bartlett's test, the Scheffé-Box log-anova test, Cochran’s C test and Box’s M test, in their parametric and permutational forms. The main questions addressed in ...

In this paper we introduce some tests for the comparison of k samples based on kernel density estimators (KDE), and we develope the Double Minimum method as a new and useful procedure for the crucial problem of bandwidth selection. We study, via Monte Carlo simulations, the statistical power of the proposed tests, as well as the impact of the smoothing degree and the performance of the Double M...

We introduce estimation and test procedures through divergence optimization for discrete or continuous parametric models. This approach is based on a new dual representation for divergences. We treat point estimation and tests for simple and composite hypotheses, extending maximum likelihood technique. An other view at the maximum likelihood approach, for estimation and test, is given. We prove...

We introduce two novel non-parametric statistical hypothesis tests. The first test, called the relative test of dependency, enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbert-Schmidt Independence Criterion (HSIC). The second test, called the relative test of similarity, is use to dete...

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric Ž ts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates...

Loss functions play an important role in analyzing insurance portfolios. A fundamental issue in the study of loss functions involves the selection of probability models for claim frequencies. In this article, we propose a semiparametric approach based on the generalized method of moments (GMM) to solve the specification problems concerning claim frequency distributions. The GMM-based testing pr...