Inference in the presence of likelihood monotonicity for proportional hazards regression

John E. Kolassa and Juan Zhang

Statistica Neerlandica, 2023, vol. 77, issue 3, 322-339

Abstract: Proportional hazards are often used to model event time data subject to censoring. Samples involving discrete covariates with strong effects can lead to infinite maximum partial likelihood estimates. A methodology is presented for eliminating nuisance parameters estimated at infinity using approximate conditional inference. Of primary interest is testing in cases in which the parameter of primary interest has a finite estimate, but in which other parameters are estimated at infinity.

Date: 2023
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