A robust scalar-on-function logistic regression for classification


Mutis M., Beyaztas U., Gölbaşı Şimşek G. , Shang H. L.

Communications In Statistics-Theory And Methods, vol.51, pp.1-17, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51
  • Publication Date: 2022
  • Doi Number: 10.1080/03610926.2022.2065018
  • Journal Name: Communications In Statistics-Theory And Methods
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1-17
  • Keywords: Basis function expansion, functional partial least squares, robust estimation, strawberry purees, weighted likelihood, GENERALIZED LINEAR-MODELS, GENE

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.