scholarly journals A note on variable selection in functional regression via random subspace method

2018 ◽  
Vol 27 (3) ◽  
pp. 455-477 ◽  
Author(s):  
Łukasz Smaga ◽  
Hidetoshi Matsui
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Functional Model ◽  
Multiple Linear Regression Model ◽  
Real Data ◽  
Information Criteria ◽  
Functional Regression ◽  
Random Subspace ◽  
Subspace Method ◽  
Random Subspace Method

Abstract Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors. The functional model can be represented by certain multiple linear regression model via basis expansions of functional variables. Based on this model and random subspace method of Mielniczuk and Teisseyre (Comput Stat Data Anal 71:725–742, 2014), two simple variable selection procedures for scalar response functional regression model are proposed. The final functional model is selected by using generalized information criteria. Monte Carlo simulation studies conducted and a real data example show very satisfactory performance of new variable selection methods under finite samples. Moreover, they suggest that considered procedures outperform solutions found in the literature in terms of correctly selected model, false discovery rate control and prediction error.

A Unifying Classification for Functional Regression Modeling

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Functional Model ◽  
Additive Functional ◽  
Regression Modeling ◽  
Functional Regression ◽  
Regression Problem ◽  
Nonparametric Models ◽  
Index Model ◽  
Functional Linear Regression

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

Directed Random Subspace Method for Face Recognition

2010 ◽  
Author(s):  
Mehrtash T. Harandi ◽  
Majid Nili Ahmadabadi ◽  
Babak N. Araabi ◽  
Abbas Bigdeli ◽  
Brian C. Lovell
Keyword(s):  
Face Recognition ◽  
Random Subspace ◽  
Subspace Method ◽  
Random Subspace Method

POS-RS: A Random Subspace method for sentiment classification based on part-of-speech analysis

2015 ◽  
Vol 51 (4) ◽  
pp. 458-479 ◽  
Author(s):  
Gang Wang ◽  
Zhu Zhang ◽  
Jianshan Sun ◽  
Shanlin Yang ◽  
Catherine A. Larson
Keyword(s):  
Sentiment Classification ◽  
Speech Analysis ◽  
Random Subspace ◽  
Subspace Method ◽  
Random Subspace Method ◽  
Part Of Speech

A Novel Random Subspace Method for Online Writeprint Identification

2012 ◽  
Vol 7 (12) ◽  
Author(s):  
Zhi Liu ◽  
Zongkai Yang ◽  
Sanya Liu ◽  
Wenting Meng
Keyword(s):  
Random Subspace ◽  
Subspace Method ◽  
Random Subspace Method

Quantification of Concrete Waste Generated in Housing Construction Sites via Site Sampling: Development of a Multiple Linear Regression Model

2015 ◽  
Vol 802 ◽  
pp. 676-681
Author(s):  
Siti Hafizan Hassan ◽  
Hamidi Abdul Aziz ◽  
Izwan Johari ◽  
Mohd Nordin Adlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Multiple Linear Regression Model ◽  
Real Data ◽  
Housing Construction ◽  
Construction Sites ◽  
Average Percentage

Waste generated in construction sites has recently increased and has become an uncontrollable cause of environmental problems and profit loss to contractors. The lack of real data or research on such wastes is due to the lack of suitable policies regarding this issue. The actions of contractors are not controlled by rules on this issue. This situation leads to the lack of action or awareness on the side of the contractor. Concrete waste is also part of the waste generated in construction sites. We determine the concrete waste generated in construction stages and conduct multiple linear regression analysis of the amount of column waste generated. The methodology employed in this study involves site observations, interviews with site personnel, and sampling at housing construction sites. The estimation method is utilized for the sampling of concrete waste. Results show that the average percentage of column waste is 13.93% and that of slab waste is 0.34%. These percentage values are derived from the total order of the concrete. The difference is due to the sizes of structures and method of handling. The regression model obtained from the sample data on column waste resulted in an adjustedR2value of 0.895. Therefore, the model predicts approximately 89.5% of the factors involved in concrete waste generation.

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