On assessing binary regression models based on ungrouped data

Biometrics
Chunling Lu, Yuhong Yang

Abstract

Assessing a binary regression model based on ungrouped data is a commonly encountered but very challenging problem. Although tests, such as Hosmer-Lemeshow test and le Cessie-van Houwelingen test, have been devised and widely used in applications, they often have low power in detecting lack of fit and not much theoretical justification has been made on when they can work well. In this article, we propose a new approach based on a cross-validation voting system to address the problem. In addition to a theoretical guarantee that the probabilities of type I and II errors both converge to zero as the sample size increases for the new method under proper conditions, our simulation results demonstrate that it performs very well.

References

Sep 1, 1973·The Proceedings of the Nutrition Society·H Eyssen
May 15, 1997·Statistics in Medicine·D W HosmerS Lemeshow
May 2, 2006·Statistical Applications in Genetics and Molecular Biology·Mark J van der LaanSunduz Keles
Aug 26, 2006·Nature Clinical Practice. Rheumatology·Ernest Choy
May 16, 2012·Intensive Care Medicine·Nicolás Serrano
May 31, 2013·Electronic Journal of Statistics·Guosheng Yin, Yanyuan Ma

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