Oracle Properties, Bias Correction, and Bootstrap Inference for Adaptive Lasso for Time SeriesM-Estimators

2017 ◽  
Vol 39 (2) ◽  
pp. 111-128 ◽  
Author(s):  
Francesco Audrino ◽  
Lorenzo Camponovo
Keyword(s):  
Bias Correction ◽  
Adaptive Lasso ◽  
Oracle Properties ◽  
Bootstrap Inference

Variable selection in rank regression for analyzing longitudinal data

2016 ◽  
Vol 27 (8) ◽  
pp. 2447-2458 ◽  
Author(s):  
Liya Fu ◽  
You-Gan Wang
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Adaptive Lasso ◽  
New Method ◽  
Dispersion Function ◽  
Simulation Studies ◽  
Rank Regression ◽  
Oracle Properties ◽  
Effective Selection ◽  
Selection Of

In this paper, we consider variable selection in rank regression models for longitudinal data. To obtain both robustness and effective selection of important covariates, we propose incorporating shrinkage by adaptive lasso or SCAD in the Wilcoxon dispersion function and establishing the oracle properties of the new method. The new method can be conveniently implemented with the statistical software R. The performance of the proposed method is demonstrated via simulation studies. Finally, two datasets are analyzed for illustration. Some interesting findings are reported and discussed.

scholarly journals ADAPTIVE LASSO-TYPE ESTIMATION FOR MULTIVARIATE DIFFUSION PROCESSES

2012 ◽  
Vol 28 (4) ◽  
pp. 838-860 ◽  
Author(s):  
Alessandro De Gregorio ◽  
Stefano M. Iacus
Keyword(s):  
Diffusion Processes ◽  
Estimation Method ◽  
Real Data ◽  
Rates Of Convergence ◽  
American Statistical Association ◽  
Point Of View ◽  
Adaptive Lasso ◽  
Simultaneous Estimation ◽  
Oracle Properties ◽  
Selection Operator

The least absolute shrinkage and selection operator (LASSO) is a widely used statistical methodology for simultaneous estimation and variable selection. It is a shrinkage estimation method that allows one to select parsimonious models. In other words, this method estimates the redundant parameters as zero in the large samples and reduces variance of estimates. In recent years, many authors analyzed this technique from a theoretical and applied point of view. We introduce and study the adaptive LASSO problem for discretely observed multivariate diffusion processes. We prove oracle properties and also derive the asymptotic distribution of the LASSO estimator. This is a nontrivial extension of previous results by Wang and Leng (2007, Journal of the American Statistical Association, 102(479), 1039–1048) on LASSO estimation because of different rates of convergence of the estimators in the drift and diffusion coefficients. We perform simulations and real data analysis to provide some evidence on the applicability of this method.

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