Dealing with the Phenomenon of Quasi-complete Separation and a Goodness of Fit Test in Logistic Regression Models in the Case of Long Data Sets

V. G. Vassiliadis (), I. I. Spyroglou (), A. G. Rigas (), J. R. Rosenberg () and K. A. Lindsay ()
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V. G. Vassiliadis: Democritus University of Thrace
I. I. Spyroglou: Democritus University of Thrace
A. G. Rigas: Democritus University of Thrace
J. R. Rosenberg: University of Glasgow
K. A. Lindsay: University of Glasgow

Statistics in Biosciences, 2019, vol. 11, issue 3, No 5, 567-596

Abstract: Abstract The phenomenon of quasi-complete separation that appears in the identification of the neuromuscular system called muscle spindle by a logistic regression model is considered. The system responds when it is affected by a number of stimuli. Both the response and the stimuli are very long binary sequences of events. In the logistic model, three functions are of special interest: the threshold, the recovery and the summation functions. The maximum likelihood estimates are obtained efficiently and very fast by using the penalized likelihood function. A validity test for the fitted model based on the randomized quantile residuals is proposed. The validity test is transformed to a goodness of fit test and the use of Q–Q plot is also discussed.

Keywords: Penalized likelihood function; Randomized quantile residuals; Q–Q plot; Binary data; Muscle spindle (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-019-09249-z

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