A control chart based on Pearson residuals for a negative binomial regression: application to infant mortality data

Abstract

PurposeThe study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.Design/methodology/approachIn order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.FindingsWhen the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.Originality/valueThe proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

Hotelling T2 Control Chart for Detecting Changes in Mortality Models Based on Machine-Learning Decision Tree

“…Meanwhile, other papers utilize control charts for micro mortality data only. Research by [9] uses the EWMA chart on infant mortality, which monitors the residual deviance to see substantial errors, considered as outliers. On the other hand, Ref.…”