EDA on the asymptotic normality of the standardized sequential stopping times, Part-I: Parametric models. Issue 3 (3rd July 2018) - Mukhopadhyay, Nitis; Zhang, Chen
ABSTRACT: In sequential analysis, an experimenter may gather information regarding an unknown parameter by observing random samples in successive steps. We emphasize a number of specific parametric models under a variety of loss functions. The total number of observations collected at termination is a positive integer-valued random variable, customarily denoted byN ≡ N ν . The exact probability distribution of N is often hard to obtain. However, under a set of regulatory conditions, our standardized versionQ ≡ Q ν of the stopping variableN ν from Definition 2.1 in Section 2.2 would follow a normal distribution in the asymptotic sense. In this article, we first show how these regulatory conditions build upon one another in order to conclude the asymptotic normality of such standardized stopping variable, Q ν . We provide exploratory data analysis (EDA) via a number of interesting illustrations obtained through large-scale simulation studies. We demonstrate the broad applicability of purely sequential methodologies included in this article along with the appropriateness of our conclusions regarding the asymptotic normality of the standardized stopping variableQ ν as a practical guideline.