A robust scalar-on-function logistic regression for classification - BEYAZTAŞ, UFUK (My University, 2022)
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.
Keywords: İSTATİSTİK & OLASILIK; Matematik; Temel Bilimler (SCI); STATISTICS & PROBABILITY; MATHEMATICS; Natural Sciences (SCI); İstatistik, Olasılık ve Belirsizlik; Analiz; İstatistik ve Olasılık; Cebir ve Sayı Teorisi; Matematik (çeşitli); Genel Matematik; Sosyal Bilimler ve Beşeri Bilimler; Fizik Bilimleri; Statistics, Probability and Uncertainty; Analysis; Statistics and Probability; Algebra and Number Theory; Mathematics (miscellaneous); General Mathematics; Social Sciences & Humanities; Physical Sciences; Basis function expansion; functional partial least squares; robust estimation; strawberry purees; weighted likelihood; GENERALIZED LINEAR-MODELS; GENE