Ridge estimation in generalized linear models and proportional hazards regressions

Guanghan Liu and Steven Piantadosi

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 23, 11466-11479

Abstract: Conventionally, a ridge parameter is estimated as a function of regression parameters based on ordinary least squares. In this article, we proposed an iterative procedure instead of the one-step or conventional ridge method. Additionally, we construct an indicator that measures the potential degree of improvement in mean squared error when ridge estimates are employed. Simulations show that our methods are appropriate for a wide class of non linear models including generalized linear models and proportional hazards (PHs) regressions. The method is applied to a PH regression with highly collinear covariates in a cancer recurrence study.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:23:p:11466-11479

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DOI: 10.1080/03610926.2016.1267767

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