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An active set algorithm to estimate parameters in generalized linear models with ordered predictors. (English) Zbl 1284.62466

Summary: In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. An active set algorithm that can efficiently compute such estimators is proposed, and a characterization of the solution is provided. Having an efficient algorithm at hand is especially relevant when applying likelihood ratio tests in restricted generalized linear models, where one needs the value of the likelihood at the restricted maximizer. The algorithm is illustrated on a real life data set from oncology.

MSC:

62J12 Generalized linear models (logistic models)
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

R; logcondens; OrdFacReg
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Full Text: DOI arXiv

References:

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