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Adaptive joint distribution learning

Author

Listed:
  • Damir Filipović

    (École Polytechnique Fédérale de Lausanne; Swiss Finance Institute)

  • Michael D. Multerer

    (Swiss Finance Institute - USI Lugano)

  • Paul Schneider

    (University of Lugano - Institute of Finance; Swiss Finance Institute)

Abstract

We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensional, normalized and positive model of a Radon-Nikodym derivative, which we estimate from sample sizes of up to several millions, alleviating the inherent limitations of RKHS modeling. Well-defined normalized and positive conditional distributions are natural by-products to our approach. Our proposal is fast to compute and accommodates learning problems ranging from prediction to classification. Our theoretical findings are supplemented by favorable numerical results.

Suggested Citation

  • Damir Filipović & Michael D. Multerer & Paul Schneider, 2024. "Adaptive joint distribution learning," Swiss Finance Institute Research Paper Series 24-50, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2450
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4965963
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    More about this item

    Keywords

    distribution estimation; tensor product RKHS; low-rank approximation;
    All these keywords.

    JEL classification:

    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving

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