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Bivariate Isotonic Regression by Dynamic Programming

Pedro Afonso Fernandes

arXiv 14 Jul 2026 · Econometrics

arXiv:2607.12629 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This article extends the dynamic programming framework introduced by (Rote, 2019) from the univariate to the bivariate isotonic problem, using an anti-diagonal traversal procedure. The proposed algorithm is applied to the well-known baseball data set that describes the association of salary with a collection of player properties, including the number of runs batted and hits. The new algorithm is relevant in the sense that dynamic programming has a wide range of applications in economics, such as the savings problem, economic growth, job search, business cycles, oligopoly equilibrium, recursive contracts, and forecasting.

Citation extraction

12
references
22
in-text mentions
12
distinct cited
0
self-citations
2,068
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1G. Rote (2019) Isotonic Regression by Dynamic Programming1.00063100%
2E. Lim (2025) Isotonic and Convex Regression: A Review of Theory, Algorithms, and Applications0.73732100%
3G. Bril, R. Dykstra, C. Pillers, and T. Robertson (1984) Algorithm AS 206: Isotonic Regression in Two Independent Variables0.64422100%
4R. L. Dykstra and T. Robertson (1982) An Algorithm for Isotonic Regression for Two or More Independent Variables0.64422100%
5R. Luss, S. Rosset, and M. Shahar (2024) Isotonic Recursive Partitioning0.51121100%
6E. Barlow, R, D. J. Bartholomew, J. M. Bremner, and H. D. Brunk (1972) Statistical Inference under Order Restrictions0.40511100%
7D. P. Bertsekas and J. N. Tsitsiklis (1996) Neuro-Dynamic Programming0.40511100%
8L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone (1984) Classification And Regression Trees0.40511100%
9H. D. Brunk (1955) Maximum Likelihood Estimates of Monotone Parameters0.40511100%
10Y. K. Cheung and K. M. Diaz (2023) Monotone response surface of multi-factor condition: estimation and Bayes classifiers0.40511100%

Showing the top 10 of 12 scored citations.