A new goodness-of-fit test for the logistic regression model is proposed. It exploits the property of this model that when it is correct, i.e. not misspecified, the parameter estimates are (asymptotically) invariant under reweighting the observations by weights w(i) that are a function of the binary (0/1) outcomes y(i). Misspecification of the model can thus be concluded when parameter estimates change under reweighting. A local test, considering weights of the form w(i)=(1 + epsilony(i)) is explored. The test is especially suitable for case-control studies but may be used in other contexts as well. Copyright (C) 2005 John Wiley Sons, Ltd.
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N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USAN Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
Lu, Wenbin
Liu, Mengling
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NYU, Sch Med, Dept Populat Hlth, New York, NY USA
NYU, Sch Med, Dept Environm Med, New York, NY USAN Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
Liu, Mengling
Chen, Yi-Hau
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Acad Sinica, Inst Stat Sci, Taipei 11529, TaiwanN Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
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East China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R ChinaEast China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R China
Sheng, Zhen
Liu, Yukun
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East China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R China
East China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai 200062, Peoples R ChinaEast China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R China
Liu, Yukun
Qin, Jing
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Natl Inst Allergy & Infect Dis, NIH, Bethesda, MD USAEast China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R China