Single-index partially functional linear quantile regression

Jiang, Zhiqiang; Huang, Zhensheng

doi:10.1080/03610926.2022.2116282

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The spline method, which is frequently employed to investigate functional data, were used in many papers that studied functional data. Based on B-spline, Yuan and Zhang [11] used the residual sum of squares to construct the test statistic of the β in the model (1); Hu and Liang [12] considered the empirical likelihood in the single-index partially functional linear model when the observations are missing at random; Jiang and Huang [13]) discussed single-index partially functional linear quantile regression and used B-spline to approximate the link function and the slope function and further establish the convergence rates and asymptotic normality of the estimators. Bouka et al [14] employed a smoothing spline to study the estimators with a spatial functional linear regression.…”

Section: Introductionmentioning

confidence: 99%

Partially Functional Linear Models with Linear Process Errors

Hu,

Pang

2023

Mathematics

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In this paper, we focus on the partial functional linear model with linear process errors deduced by not necessarily independent random variables. Based on Mercer’s theorem and Karhunen–Loève expansion, we give the estimators of the slope parameter and coefficient function in the model, establish the asymptotic normality of the estimator for the parameter and discuss the weak convergence with rates of the proposed estimators. Meanwhile, the penalized estimator of the parameter is defined by the SCAD penalty and its oracle property is investigated. Finite sample behavior of the proposed estimators is also analysed via simulations.

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