Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints

Abstract:We study the problem of estimating multiple discrete unimodal distributions, motivated by search behavior analysis on a real-world platform. To incorporate prior knowledge of precedence relations among distributions, we impose stochastic order constraints and formulate the estimation task as a mixed-integer convex quadratic optimization problem. Experiments on both synthetic and real datasets show that the proposed method reduces the Jensen-Shannon divergence by 2.2% on average (up to 6.3%) when the sample size is small, while performing comparably to existing methods when sufficient data are available.

Optimization and Control (math.OC); Machine Learning (cs.LG); Methodology (stat.ME)

arXiv:2603.11532 [math.OC]

(or arXiv:2603.11532v2 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2603.11532 Focus to learn more arXiv-issued DOI via DataCite

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