Change-Point Estimation and Detection for Mixture of Linear Regression Models
This paper studies the estimation and detection problems in the mixture of linear regression models with change point. An improved Expectation–Maximization (EM) algorithm is devised specifically for multi-classified mixture data with change points. Under appropriate conditions, the large-sample prop...
Saved in:
| Main Authors: | Wenzhi Zhao, Tian Cheng, Zhiming Xia |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Axioms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1680/14/6/402 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The mixture of multiple regression equations: open problems
by: T. Ya. Yeleyko, et al.
Published: (2025-06-01) -
A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points
by: Junxia Ma, et al.
Published: (2025-06-01) -
Kernel Density Estimated Linear Regression
by: Roshan Kalpavruksha, et al.
Published: (2025-05-01) -
Semiparametric Transformation Models with a Change Point for Interval-Censored Failure Time Data
by: Junyao Ren, et al.
Published: (2025-08-01) -
A simulation-based evidence on the improved performance of a new modified leverage adjusted heteroskedastic consistent covariance matrix estimator in the linear regression model
by: Nuzhat Aftab, et al.
Published: (2018-08-01)