Computing estimates in the proportional odds model

David R. Hunter, Kenneth Lange

Research output: Contribution to journalArticle

12 Scopus citations


The semiparametric proportional odds model for survival data is useful when mortality rates of different groups converge over time. However, fitting the model by maximum likelihood proves computationally cumbersome for large datasets because the number of parameters exceeds the number of uncensored observations. We present here an alternative to the standard Newton-Raphson method of maximum likelihood estimation. Our algorithm, an example of a minorization-maximization (MM) algorithm, is guaranteed to converge to the maximum likelihood estimate whenever it exists. For large problems, both the algorithm and its quasi-Newton accelerated counterpart outperform Newton-Raphson by more than two orders of magnitude.

Original languageEnglish (US)
Pages (from-to)155-168
Number of pages14
JournalAnnals of the Institute of Statistical Mathematics
Issue number1
StatePublished - Jul 11 2002


All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this