scholarly journals VARIABLE SELECTION FOR HISTORICAL FUNCTIONAL LINEAR MODEL

2021 ◽  
Vol 53 (1) ◽  
pp. 1-19
Author(s):  
Hidetoshi Matsui
Keyword(s):  
Variable Selection ◽  
Linear Model ◽  
Functional Linear Model ◽  
Selection For

scholarly journals Quantile regression and variable selection for partially linear model with randomly truncated data

2017 ◽  
Vol 60 (4) ◽  
pp. 1137-1160 ◽  
Author(s):  
Hong-Xia Xu ◽  
Zhen-Long Chen ◽  
Jiang-Feng Wang ◽  
Guo-Liang Fan
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Linear Model ◽  
Partially Linear Model ◽  
Truncated Data ◽  
Partially Linear ◽  
Selection For

Predictive Variable Selection for the Multivariate Linear Model

Biometrics ◽  
1997 ◽  
Vol 53 (2) ◽  
pp. 465 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen
Keyword(s):  
Variable Selection ◽  
Linear Model ◽  
Multivariate Linear Model ◽  
Predictive Variable ◽  
Selection For

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 Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document