 Concave likelihood‐based regression with finite‐support response variablesK.O. Ekvall and M. Bottai Biometrics, 2023, vol. 79, issue 3, 2286-2297 Abstract: We propose a unified framework for likelihood‐based regression modeling when the response variable has finite support. Our work is motivated by the fact that, in practice, observed data are discrete and bounded. The proposed methods assume a model which includes models previously considered for interval‐censored variables with log‐concave distributions as special cases. The resulting log‐likelihood is concave, which we use to establish asymptotic normality of its maximizer as the number of observations n tends to infinity with the number of parameters d fixed, and rates of convergence of L1‐regularized estimators when the true parameter vector is sparse and d and n both tend to infinity with log(d)/n→0$\log (d) / n \rightarrow 0$. We consider an inexact proximal Newton algorithm for computing estimates and give theoretical guarantees for its convergence. The range of possible applications is wide, including but not limited to survival analysis in discrete time, the modeling of outcomes on scored surveys and questionnaires, and, more generally, interval‐censored regression. The applicability and usefulness of the proposed methods are illustrated in simulations and data examples. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:79:y:2023:i:3:p:2286-2297 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
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