Regression with stagewise minimization on risk function

Takuma Yoshida and Kanta Naito

Computational Statistics & Data Analysis, 2019, vol. 134, issue C, 123-143

Abstract: This paper studies a curve estimation based on empirical risk minimization. The estimator is composed as a convex combination of words (learners) in a dictionary. A word is selected in each step of the proposed stagewise algorithm, which minimizes a certain divergence measure. A non-asymptotic error bound of the estimator is developed, and it is shown that the error bound becomes sharp as the number of iterations of the algorithm increases. A simulation study and real data example confirm the performance of the estimator.

Keywords: Non-asymptotic theory; Regression; Risk minimization; Stagewise estimation (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:134:y:2019:i:c:p:123-143

DOI: 10.1016/j.csda.2018.12.011

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