A robust scalar-on-function logistic regression for classification

Mutis M., Beyaztaş U., Simsek G. G., Shang H. L.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.52, sa.23, ss.8538-8554, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 23
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/03610926.2022.2065018
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.8538-8554
  • Anahtar Kelimeler: Basis function expansion, functional partial least squares, robust estimation, strawberry purees, weighted likelihood, GENERALIZED LINEAR-MODELS, GENE
  • Marmara 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.