Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data

2008 ◽  
Vol 27 (6) ◽  
pp. 845-863 ◽  
Author(s):  
Xiaowei Yang ◽  
Kun Nie
Keyword(s):  
Linear Regression ◽  
Longitudinal Data ◽  
Hypothesis Testing ◽  
Regression Models ◽  
Wavelet Thresholding ◽  
Linear Regression Models ◽  
Functional Linear Regression

Bootstrap Calibration in Functional Linear Regression Models with Applications

2010 ◽  
pp. 199-207
Author(s):  
Wenceslao González-Manteiga ◽  
Adela Martínez-Calvo
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Bootstrap Calibration ◽  
Functional Linear Regression

Estimation and inference for covariate adjusted partially functional linear regression models

2021 ◽  
Vol 14 (4) ◽  
pp. 359-371
Author(s):  
Zhiqiang Jiang ◽  
Zhensheng Huang ◽  
Hanbing Zhu
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Functional Linear Regression

Polynomial spline estimation for partial functional linear regression models

2016 ◽  
Vol 31 (3) ◽  
pp. 1107-1129 ◽  
Author(s):  
Jianjun Zhou ◽  
Zhao Chen ◽  
Qingyan Peng
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Polynomial Spline ◽  
Linear Regression Models ◽  
Spline Estimation ◽  
Functional Linear Regression

scholarly journals Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline

2018 ◽  
Vol 8 (1) ◽  
pp. 135
Author(s):  
Mingao Yuan ◽  
Yue Zhang
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
B Spline ◽  
Empirical Likelihood Method ◽  
Regression Parameters ◽  
Functional Linear Regression

In this paper, we apply empirical likelihood method to infer for the regression parameters in the partial functional linear regression models based on B-spline. We prove that the empirical log-likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi-square distributions. Our simulation shows that the proposed empirical likelihood method produces more accurate confidence regions in terms of coverage probability than the asymptotic normality method.

Model checks for functional linear regression models based on projected empirical processes

2020 ◽  
Vol 144 ◽  
pp. 106897
Author(s):  
Feifei Chen ◽  
Qing Jiang ◽  
Zhenghui Feng ◽  
Lixing Zhu
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Empirical Processes ◽  
Linear Regression Models ◽  
Functional Linear Regression
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