Information–Theoretic Aspects of Location Parameter Estimation under Skew–Normal Settings

Abstract

In several applications, the assumption of normality is often violated in data with some level of skewness, so skewness affects the mean’s estimation. The class of skew–normal distributions is considered, given their flexibility for modeling data with asymmetry parameter. In this paper, we considered two location parameter (μ) estimation methods in the skew–normal setting, where the coefficient of variation and the skewness parameter are known. Specifically, the least square estimator (LSE) and the best unbiased estimator (BUE) for μ are considered. The properties for BUE (which dominates LSE) using classic theorems of information theory are explored, which provides a way to measure the uncertainty of location parameter estimations. Specifically, inequalities based on convexity property enable obtaining lower and upper bounds for differential entropy and Fisher information. Some simulations illustrate the behavior of differential entropy and Fisher information bounds.

Distance in Information and Statistical Physics III

“…Contreras-Reyes [3] considers the two methods for the location parameter estimation of skew-normal distribution: the least square estimator and the best unbiased estimator. In this process, the author presents two lower bounds for differential entropy and obtains both the lower and upper bounds for the Fisher information of the location parameter.…”