Semi-functional partial linear model with missing at random responses

S. Novo Díaz, G. Aneiros Pérez

This work considers a semi-functional partial linear model in the presence of responses that are missing at random. Three estimation approaches are examined: imputation, semiparametric regression surrogate, and inverse marginal probability weighting. For each method, asymptotic properties of the estimators of the model components are derived. A simulation study and an application to real data are used to assess and compare the finite-sample performance of the proposed estimators.

Keywords: missing-at-random responses, functional data, semi-functional regression, partially linear models

Scheduled

GT Análisis de Datos Funcionales I
September 4, 2026  9:00 AM
Aula 30

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