Sequential change-point detection in a multinomial logistic regression model
DOI10.1515/math-2020-0037zbMath1479.62074OpenAlexW3078890438MaRDI QIDQ2053415
Fuxiao Li, Zhanshou Chen, Yanting Xiao
Publication date: 29 November 2021
Published in: Open Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1515/math-2020-0037
categorical time seriesmultinomial logistic regression modelsequential change-point detectionpartial likelihood score process
Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Sequential statistical analysis (62L10)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Retrospective change detection for binary time series models
- Structural changes in autoregressive models for binary time series
- Modified procedures for change point monitoring in linear models
- Monitoring parameter change in time series models
- Monitoring parameter change in AR\((p)\) time series models
- Regression theory for categorical time series
- Detection of structural changes in generalized linear models
- Change-point detection in multinomial data with a large number of categories
- Monitoring changes in linear models
- Monitoring changes in the error distribution of autoregressive models based on Fourier methods
- Sequential change-point detection based on nearest neighbors
- Change detection in autoregressive time series
- Nonparametric maximum likelihood approach to multiple change-point problems
- Monitoring distributional changes of squared residuals in GARCH models
- Monitoring Structural Changes in Generalized Linear Models
- Monitoring Structural Change
- Retrospective change detection in categorical time series
This page was built for publication: Sequential change-point detection in a multinomial logistic regression model
