Guaranteed maximum likelihood splitting tests of a linear regression model

DOI10.1080/02331880601013874zbMath1120.62050OpenAlexW2040802333MaRDI QIDQ5423155

Albert Vexler, Gregory Gurevich

Publication date: 22 October 2007

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331880601013874

zbMATH Keywords

maximum likelihoodthresholdChange pointmartingale structuretwo-phase linear model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03) Point estimation (62F10)

Related Items

Retrospective Change Point Detection: From Parametric to Distribution Free Policies

Cites Work

• Unnamed Item
• Sequential detection of a change in a normal mean when the initial value is unknown
• An application of the maximum likelihood test to the change-point problem
• Limit theorems for a class of tests of gradual changes
• Detecting a change in regression: First-order optimality
• Change point problems in the model of logistic regression
• On the average run length to false alarm in surveillance problems which possess an invariance structure
• Asymptotics of maximum likelihood estimator in a two-phase linear regression model
• Nonanticipating estimation applied to sequential analysis and changepoint detection
• Asymptotics of M-estimators in two-phase linear regression models.
• Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes
• The likelihood ratio test for a change-point in simple linear regression
• Sample Splitting and Threshold Estimation
• Asymptotically Optimal Solutions in the Change-Point Problem
• Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time
• The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes