New approaches to parameter estimation with statistical censoring by means of the CEV algorithm: Characterization of its properties for high-performance normal processes

Abstract

The process of parameter estimation in order to characterize a population using algorithms is in constant development and perfection. Recent years show that data-based decision-making is complex when there is uncertainty generated by statistical censoring. The purpose of this article is to evaluate the effect of statistical censoring on the normal distribution, which is common in many processes. Parameter estimation properties will be characterized with the conditional expected value algorithm, using different censoring percentages and sample sizes. The estimation properties chosen for the study will focus on the monitoring and decision-making related to industrial processes with the presence of censoring.