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arXiv · 2204.02508

A robust scalar-on-function logistic regression for classification

Muge Mutis·Ufuk Beyaztas·Gulhayat Golbasi Simsek·Han Lin Shang
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Abstract

Scalar-on-function logistic regression, where the response is a binary outcome and the predictor consists of random curves, has become a general framework to explore a linear relationship between the binary outcome and functional predictor. Most of the methods used to estimate this model are based on the least-squares type estimators. However, the least-squares estimator is seriously hindered by outliers, leading to biased parameter estimates and an increased probability of misclassification. This paper proposes a robust partial least squares method to estimate the regression coefficient function in the scalar-on-function logistic regression. The regression coefficient function represented by functional partial least squares decomposition is estimated by a weighted likelihood method, which downweighs the effect of outliers in the response and predictor. The estimation and classification performance of the proposed method is evaluated via a series of Monte Carlo experiments and a strawberry puree data set. The results obtained from the proposed method are compared favorably with existing methods.

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Muge Mutis, Ufuk Beyaztas, Gulhayat Golbasi Simsek, Han Lin Shang. 2022-04-05. A robust scalar-on-function logistic regression for classification. https://arxiv.org/abs/2204.02508

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