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On assessing binary regression models based on ungrouped data

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  • 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.

Suggested Citation

  • Chunling Lu & Yuhong Yang, 2019. "On assessing binary regression models based on ungrouped data," Biometrics, The International Biometric Society, vol. 75(1), pages 5-12, March.
  • Handle: RePEc:bla:biomet:v:75:y:2019:i:1:p:5-12
    DOI: 10.1111/biom.12969
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    References listed on IDEAS

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    1. Bernard Veldkamp & Wim Linden, 2002. "Multidimensional adaptive testing with constraints on test content," Psychometrika, Springer;The Psychometric Society, vol. 67(4), pages 575-588, December.
    2. J. Fan & M. Farmen & I. Gijbels, 1998. "Local maximum likelihood estimation and inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 591-608.
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