Sequential change-point detection in a multinomial logistic regression model

Abstract

Change-point detection in categorical time series has recently gained attention as statistical models incorporating change-points are common in practice, especially in the area of biomedicine. In this article, we propose a sequential change-point detection procedure based on the partial likelihood score process for the detection of changes in the coefficients of multinomial logistic regression model. The asymptotic results are presented under both the null of no change and the alternative of changes in coefficients. We carry out a Monte Carlo experiment to evaluate the empirical size of the proposed procedure as well as its average run length. We illustrate the method by using data on a DNA sequence. Monte Carlo experiments and real data analysis demonstrate the effectiveness of the proposed procedure.

Structural change detection in ordinal time series

“…As generalized linear regression models for categorical time series allow for parsimonious modeling and incorporation of random time-dependent covariates, Fokianos and Kedem [ 15 ] suggested the generalized linear model for categorical time series modeling. For change-point detection in the generalized linear model, Xia et al [ 16 ] introduced two procedures to sequentially detect the structural change in generalized linear models with assuming independence; Hudecová [ 17 ] investigated the detection of change in autoregressive models for binary time series; Fokianos et al [ 18 ] provided a statistical procedure based on the partial likelihood score process to detect a structural change in binary logistic regression model; Gombay et al [ 19 ] and Li et al [ 20 ] discussed retrospective change detection and sequential change detection in multinominal logistic regression model.…”