Title: Bayesian quantile regression and unsupervised learning methods to the US Army and Navy data

Authors: Jong-Min Kim; Chuwen Li; Il Do Ha

Addresses: Division of Science and Mathematics, University of Minnesota-Morris, Morris, USA ' Department of Statistics, University of Michigan, 1085 South University, Ann Arbor, MI 48109-1107, USA ' Department of Statistics, Pukyong National University, Busan, South Korea

Abstract: We apply the Bayesian quantile regression (BayesQR) model for binary response variables and the unsupervised learning methods to synthetic data (Stevens and Anderson-Cook, 2017a, 2017b), which is univariate data with a binary response of passing or failing for complex munitions generated to match age and usage rate found in US Department of Defense complex systems (Army and Navy). Instead of the generalised linear model (GLM) used in Stevens and Anderson-Cook (2017a), we propose to apply the BayesQR to predict a binary response of passing or failing for the Army and Navy data as well as the unsupervised learning methods. First, we want to find the best models for the Army and Navy through comparing statistical inference of BayesQR and GLMs and calculating their percentage correctly classified (PCC) which tests the accuracy of a prediction. The second method focuses on clustering using the k-means clustering and random forests based on the results of BayesQR. We compare models with different covariates to find the one that can best divide data into two groups: Army and Navy.

Keywords: generalised linear model; GLM; BayesQR; k-means; random forests.

DOI: 10.1504/IJPQM.2021.112016

International Journal of Productivity and Quality Management, 2021 Vol.32 No.1, pp.92 - 108

Received: 14 May 2019
Accepted: 24 Jul 2019
Published online: 22 Dec 2020 *

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