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This paper determines maximum likelihood estimates under general univariate linear models with a priori information related to maximum effects in the models. The negative log-likelihood functions and the constraints are convex functions, so convex optimization theory can be utilized to obtain relevant estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction.

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Maximum likelihood estimation under inequality constraints in univariate linear models

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  • 发表日期：

    2026年

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  • Katarzyna Filipiak,
  • Dietrich von Rosen,
  • Martin Singull

摘要

This paper determines maximum likelihood estimates under general univariate linear models with a priori information related to maximum effects in the models. The negative log-likelihood functions and the constraints are convex functions, so convex optimization theory can be utilized to obtain relevant estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction.