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

Jong Min Kim, Chuwen Li, Il Do Ha

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)92-108
Number of pages17
JournalInternational Journal of Productivity and Quality Management
Volume32
Issue number1
DOIs
StatePublished - Jan 1 2021

Bibliographical note

Publisher Copyright:
Copyright © 2021 Inderscience Enterprises Ltd.

Keywords

  • BayesQR
  • GLM
  • Generalised linear model
  • K-means
  • Random forests

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