Showing papers by "Instituto Tecnológico Autónomo de México published in 2023"
12 Apr 2023
09 May 2023
TL;DR: In this article , the authors derived analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and they used these expressions to illustrate the validity of their theoretical results by performing simulation experiments using this forecast model.
Abstract: When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm (n) observations are generated for the parameters, and in the inner level (m) observations of the simulation model are generated with the value of parameters fixed at the value generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent, showing how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments using this forecast model.
01 Jan 2023
TL;DR: In this article , the authors introduce a framework to theoretically and empirically examine electoral maldistricting, the intentional drawing of electoral districts to advance partisan objectives, compromising voter welfare.
Abstract: We introduce a framework to theoretically and empirically examine electoral maldistricting—the intentional drawing of electoral districts to advance partisan objectives, compromising voter welfare. We identify the legislatures that maximize voter welfare and those that maximize partisan goals, and incorporate them into a maldistricting index. This index measures the intent to maldistrict by comparing distances from the actual legislature to the nearest partisan and welfare-maximizing legislatures. Using 2008 presidential election data and 2010 census-based district maps, we find a Republican-leaning bias in district maps. Our index tracks court rulings in intuitive ways. This article is protected by copyright. All rights reserved