Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

Mirosław Krzyśko; Łukasz Smaga

doi:10.18778/0208-6018.334.04

Authors

Mirosław Krzyśko The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Interfaculty Institute of Mathematics and Statistics
Łukasz Smaga Adam Mickiewicz University in Poznań, Faculty of Mathematics and Computer Science

DOI:

https://doi.org/10.18778/0208-6018.334.04

Keywords:

basis functions representation, classification problem, functional regression analysis, logistic regression model, multi‑dimensional functional data, robust estimation

Abstract

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

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