Quadratic inference with dense functional responses.

ABSTRACT: We address the challenge of estimation in the context of constant linear effect models with dense functional responses. In this framework, the conditional expectation of the response curve is represented by a linear combination of functional covariates with constant regression parameters. In this paper, we present an alternative solution by employing the quadratic inference approach, a well-established method for analyzing correlated data, to estimate the regression coefficients. Our approach leverages non-parametrically estimated basis functions, eliminating the need for choosing working correlation structures. Furthermore, we demonstrate that our method achieves a parametric n -convergence rate, contingent on an appropriate choice of bandwidth. This convergence is observed when the numbe

SUBMITTER: Niyogi PG 

PROVIDER: S-EPMC12320752 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature