Copy For Citation
Mutiş M., Beyaztaş U., Gölbaşı Şimşek G., Shang H. L.
COMMUNICATIONS IN STATISTICS -THEORY AND METHODS, vol.52, no.23, pp.8538-8554, 2022 (SCI-Expanded)
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Publication Type:
Article / Article
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Volume:
52
Issue:
23
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Publication Date:
2022
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Doi Number:
10.1080/03610926.2022.2065018
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Journal Name:
COMMUNICATIONS IN STATISTICS -THEORY AND METHODS
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Journal Indexes:
Science Citation Index Expanded (SCI-EXPANDED)
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Page Numbers:
pp.8538-8554
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Keywords:
Basis function expansion, functional partial least squares, robust estimation, strawberry purees, weighted likelihood, GENERALIZED LINEAR-MODELS, GENE
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Yıldız Technical University Affiliated:
Yes
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.