scholarly journals Automatic Variable Selection for Partially Linear Functional Additive Model and Its Application to the Tecator Data Set

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yuping Hu ◽  
Sanying Feng ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Linear Functional ◽  
Additive Model ◽  
Selection Procedure ◽  
Real Data ◽  
Regularity Conditions ◽  
Data Set ◽  
Partially Linear ◽  
Functional Additive ◽  
Selection For

We introduce a new partially linear functional additive model, and we consider the problem of variable selection for this model. Based on the functional principal components method and the centered spline basis function approximation, a new variable selection procedure is proposed by using the smooth-threshold estimating equation (SEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero and simultaneously estimates the nonzero regression coefficients by solving the SEE. The approach avoids the convex optimization problem, and it is flexible and easy to implement. We establish the asymptotic properties of the resulting estimators under some regularity conditions. We apply the proposed procedure to analyze a real data set: the Tecator data set.

scholarly journals Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinghua Zhang ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Linear Models ◽  
Selection Procedure ◽  
Real Data ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Quadratic Inference Function ◽  
Quadratic Inference Functions

Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.

Optimal quantile level selection for disease classification and biomarker discovery with application to electrocardiogram data

2017 ◽  
Vol 27 (11) ◽  
pp. 3340-3349
Author(s):  
Yingchun Zhou ◽  
Rong Huang ◽  
Shanshan Yu ◽  
Yanyuan Ma
Keyword(s):  
Cardiovascular Diseases ◽  
Medical Research ◽  
Biomarker Discovery ◽  
Selection Procedure ◽  
Real Data ◽  
Disease Classification ◽  
Intensive Study ◽  
Data Set ◽  
Selection For

Classification with a large number of predictors and biomarker discovery become increasingly important in biological and medical research. This paper focuses on performing classification of cardiovascular diseases based on electrocardiogram analysis which deals with many variables and a lot of measurements within variables. We propose an optimal quantile level selection procedure to reduce dimension by characterizing distributions with quantiles and combine with classification tools to produce sensible classification and biomarker discovery results. Simulation and an intensive study of a real data set are performed to illustrate the performance of the proposed method.

VARIABLE SELECTION FOR PARTIALLY LINEAR VARYING COEFFICIENT QUANTILE REGRESSION MODEL

2013 ◽  
Vol 06 (03) ◽  
pp. 1350015 ◽  
Author(s):  
JIANG DU ◽  
ZHONGZHAN ZHANG ◽  
ZHIMENG SUN
Keyword(s):  
Variable Selection ◽  
Selection Procedure ◽  
Adaptive Lasso ◽  
Regularity Conditions ◽  
Finite Sample ◽  
Unknown Parameters ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Variable Selection Procedure ◽  
Quantile Loss

In this paper, we propose a variable selection procedure for partially linear varying coefficient model under quantile loss function with adaptive Lasso penalty. The functional coefficients are estimated by B-spline approximations. The proposed procedure simultaneously selects significant variables and estimates unknown parameters. The major advantage of the proposed procedures over the existing ones is easy to implement using existing software, and it requires no specification of the error distributions. Under the regularity conditions, we show that the proposed procedure can be as efficient as the Oracle estimator, and derive the optimal convergence rate of the functional coefficients. A simulation study and a real data application are undertaken to assess the finite sample performance of the proposed variable selection procedure.

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

scholarly journals Model Selection for Univariable Fractional Polynomials

2017 ◽  
Vol 17 (3) ◽  
pp. 619-629 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Test Procedure ◽  
Selection Procedure ◽  
Real Data ◽  
Parametric Modeling ◽  
Royal Statistical Society ◽  
Fractional Polynomials ◽  
Fractional Polynomial ◽  
Polynomial Models ◽  
Selection For ◽  
Function Selection

Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.

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