Model close fit is one key issue in the mean and covariance structure analysis. In this article, we utilize the latest results on the general distribution of likelihood ratio statistic in this methodology and propose several distribution free root mean square error of approximation (RMSEA) tests for evaluating the hypothesis of close fit of misspecified models. Simulation studies show that thre...

We provide a tight sample complexity bound for learning bounded-norm linear predictors with respect to the squared loss. Our focus is on an agnostic PAC-style setting, where no assumptions are made on the data distribution beyond boundedness. This contrasts with existing results in the literature, which rely on other distributional assumptions, refer to specific parameter settings, or use other...

INTRODUCTION In countries with high burden of HIV, major programmatic challenges have been identified to preventing new infections among children and scaling up of treatment for pregnant mothers. We initiated this study to examine operational approaches that were used to enhance implementation of PMTCT interventions in Muhima health Centre (Kigali/Rwanda) from 2007 to 2010. METHODS The prospe...

many commonly used statistical methods require that the population distribution be nearly normal. unfortunately, in some papers the one-sample kolmogorov-smirnov test has been used for testing normality while the assumptions of applying this test are not satisfied. to conduct this test, it is assumed that the population distribution is fully specified. in practical situation where the mean and ...