 Adaptive and unbiased predictors in a change point regression modelArthur Cohen and Debashis Kushary Statistics & Probability Letters, 1994, vol. 20, issue 2, 131-138 Abstract: Consider the problem of prediction in a change point regression model. That is, assume a simple linear regression model holds for all x (independent variable) less than [gamma]k, and a different simple linear regression model holds for x #62; [gamma]k. However, [gamma]k, the change point, is unknown but can be one of m possible values ([gamma]1 [less-than-or-equals, slant] [gamma]2 [less-than-or-equals, slant] ... [less-than-or-equals, slant] [gamma]m). For x = x0 #62; [gamma]m we want to predict a future value of the dependent variable. Adaptive predictors (estimators) and unbiased predictors (estimators) are studied. The adaptive estimator is one which estimates by least squares for the kth model, provided the residual sum of squares for the kth model is smallest. Some theoretical justification for the adaptive estimator is given through a decision theory formulation. A small simulation study comparing the adaptive estimator and unbiased estimator is also offered. Keywords: Generalized; Bayes; estimators; Uninformative; prior; distributions; Penalty; factor; Bias; Mean; squared; error (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:20:y:1994:i:2:p:131-138 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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