Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistica Neerlandica and proposes a methodology for eliminating nuisance parameters.

The article’s abstract is given below, with the full article available to read here.

Kolassa, J. E., & Zhang, J. (2023). Inference in the presence of likelihood monotonicity for proportional hazards regression. Statistica Neerlandica, 1– 18. https://doi.org/10.1111/stan.12287

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.