Atıf İçin Kopyala
Mutiş M., Beyaztaş U., Gölbaşı Şimşek G., Shang H. L.
COMMUNICATIONS IN STATISTICS -THEORY AND METHODS, cilt.52, sa.23, ss.8538-8554, 2023 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
52
Sayı:
23
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Basım Tarihi:
2023
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Doi Numarası:
10.1080/03610926.2022.2065018
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Dergi Adı:
COMMUNICATIONS IN STATISTICS -THEORY AND METHODS
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED)
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Sayfa Sayıları:
ss.8538-8554
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Anahtar Kelimeler:
Basis function expansion, functional partial least squares, robust estimation, strawberry purees, weighted likelihood, GENERALIZED LINEAR-MODELS, GENE
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Yıldız Teknik Üniversitesi Adresli:
Evet
Özet
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.