Tests for error correlation in the functional linear model

2012 ◽  
pp. 191-224
Author(s):  
Lajos Horváth ◽  
Piotr Kokoszka
Keyword(s):  
Linear Model ◽  
Functional Linear Model ◽  
Error Correlation

Tests for Error Correlation in the Functional Linear Model

2010 ◽  
Vol 105 (491) ◽  
pp. 1113-1125 ◽  
Author(s):  
Robertas Gabrys ◽  
Lajos Horváth ◽  
Piotr Kokoszka
Keyword(s):  
Linear Model ◽  
Functional Linear Model ◽  
Error Correlation

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Measures of influence for the functional linear model with scalar response

2010 ◽  
Vol 101 (2) ◽  
pp. 327-339 ◽  
Author(s):  
Manuel Febrero-Bande ◽  
Pedro Galeano ◽  
Wenceslao González-Manteiga
Keyword(s):  
Linear Model ◽  
Functional Linear Model

Empirical likelihood for high-dimensional partially functional linear model

2019 ◽  
Vol 09 (04) ◽  
pp. 2050017
Author(s):  
Zhiqiang Jiang ◽  
Zhensheng Huang ◽  
Guoliang Fan
Keyword(s):  
Linear Model ◽  
Empirical Likelihood ◽  
Likelihood Ratio Statistic ◽  
Real Data ◽  
Regression Coefficients ◽  
High Dimensional ◽  
Regularity Conditions ◽  
Functional Linear Model ◽  
Data Set ◽  
Functional Predictors

This paper considers empirical likelihood inference for a high-dimensional partially functional linear model. An empirical log-likelihood ratio statistic is constructed for the regression coefficients of non-functional predictors and proved to be asymptotically normally distributed under some regularity conditions. Moreover, maximum empirical likelihood estimators of the regression coefficients of non-functional predictors are proposed and their asymptotic properties are obtained. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real data set is analyzed for illustration.

scholarly journals Cross-Validation Model Averaging for Generalized Functional Linear Model

Econometrics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 7
Author(s):  
Haili Zhang ◽  
Guohua Zou
Keyword(s):  
Linear Model ◽  
Functional Data ◽  
Cross Validation ◽  
House Price ◽  
Model Averaging ◽  
Estimation Accuracy ◽  
Functional Linear Model ◽  
Model Set ◽  
Function Predictor ◽  
Better Than

Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method when the true model is not in the candidate model set. Our simulations show that the proposed method often performs better than the commonly used model selection and averaging methods. We also apply the proposed method to Beijing second-hand house price data.

Sieve M-estimator for a semi-functional linear model

2015 ◽  
Vol 58 (11) ◽  
pp. 2421-2434 ◽  
Author(s):  
LeLe Huang ◽  
HuiWen Wang ◽  
HengJian Cui ◽  
SiYang Wang
Keyword(s):  
Linear Model ◽  
Functional Linear Model ◽  
M Estimator

Prediction of Stock Price Index Using Functional Linear Model

2020 ◽  
Vol 34 (3) ◽  
pp. 309-326
Author(s):  
Miyoung Lee ◽  
◽  
Sung-Jae Kwon
Keyword(s):  
Linear Model ◽  
Stock Price ◽  
Price Index ◽  
Functional Linear Model ◽  
Stock Price Index
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