Extending conditional likelihood in models for stratified binary data

Ruggero Bellio () and Nicola Sartori ()
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Ruggero Bellio: Universitá degli Studi di Udine
Nicola Sartori: Statistiche Universitá degli Studi di Padova

Statistical Methods & Applications, 2003, vol. 12, issue 2, No 1, 132 pages

Abstract: Abstract. The conditional likelihood is widely used in logistic regression models with stratified binary data. In particular, it leads to accurate inference for the parameters of interest, which are common to all strata, eliminating stratum-specific nuisance parameters. The modified profile likelihood is an accurate approximation to the conditional likelihood, but has the advantage of being available for general parametric models. Here, we propose the modified profile likelihood as an ideal extension of the conditional likelihood in generalized linear models for binary data, with generic link function. An important feature is that for the implementation we only need standard outputs of routines for generalized linear models. The accuracy of the method is supported by theoretical properties and is confirmed by simulation results.

Keywords: Conditional likelihood; modified profile likelihood; nuisance parameter; profile likelihood; stratified data (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/s10260-003-0055-1

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