Local binary regression with spherical predictors

Marco Di Marzio, Stefania Fensore, Agnese Panzera and Charles C. Taylor

Statistics & Probability Letters, 2019, vol. 144, issue C, 30-36

Abstract: We discuss local regression estimators when the predictor lies on the d-dimensional sphere and the response is binary. Despite Di Marzio et al. (2018b), who introduce spherical kernel density classification, we build on the theory of local polynomial regression and local likelihood. Simulations and a real-data application illustrate the effectiveness of the proposals.

Keywords: Local likelihood; Spherical kernels; Tangent-normal decomposition (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2018.07.019

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