scholarly journals The Adaptive Lasso and Its Oracle Properties

2006 ◽  
Vol 101 (476) ◽  
pp. 1418-1429 ◽  
Author(s):  
Hui Zou
2016 ◽  
Vol 27 (8) ◽  
pp. 2447-2458 ◽  
Author(s):  
Liya Fu ◽  
You-Gan Wang

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.


2012 ◽  
Vol 28 (4) ◽  
pp. 838-860 ◽  
Author(s):  
Alessandro De Gregorio ◽  
Stefano M. Iacus

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.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Schmidt ◽  
Karsten Schweikert

Abstract In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.


2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan G. De Gooijer ◽  
Dawit Zerom

Abstract We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor dimension and obtain quantile forecasts. Several recent empirical studies have considered a large set of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile context. The application offers three main findings. First, combining parametric and non-parametric approaches adds quantile forecast accuracy over and above the constituent methods. Second, a handful of macroeconomic predictors are found to have systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper quantiles of the equity premium distribution.


Author(s):  
Zeying Huang ◽  
Di Zeng

China has the highest mortality rate caused by diseases and conditions associated with its high-salt diet. Since 2016, China has initiated a national salt reduction campaign that aims at promoting the usage of salt information on food labels and salt-restriction spoons and reducing condiment and pickled food intake. However, factors affecting individuals’ decisions to adopt these salt reduction measures remain largely unknown. By comparing the performances of logistic regression, stepwise logistic regression, lasso logistic regression and adaptive lasso logistic regression, this study aims to fill this gap by analyzing the adoption behaviour of 1610 individuals from a nationally representative online survey. It was found that the practices were far from adopted and only 26.40%, 22.98%, 33.54% and 37.20% reported the adoption of labelled salt information, salt-restriction spoons, reduced condiment use in home cooking and reduced pickled food intake, respectively. Knowledge on salt, the perceived benefits of salt reduction, participation in nutrition education and training programs on sodium reduction were positively associated with using salt information labels. Adoption of the other measures was largely explained by people’s awareness of hypertension risks and taste preferences. It is therefore recommended that policy interventions should enhance Chinese individuals’ knowledge of salt, raise the awareness of the benefits associated with a low-salt diet and the risks associated with consuming excessive salt and reshape their taste choices.


2021 ◽  
pp. 096228022199750
Author(s):  
Zhaoxin Ye ◽  
Yeying Zhu ◽  
Donna L Coffman

Causal mediation effect estimates can be obtained from marginal structural models using inverse probability weighting with appropriate weights. In order to compute weights, treatment and mediator propensity score models need to be fitted first. If the covariates are high-dimensional, parsimonious propensity score models can be developed by regularization methods including LASSO and its variants. Furthermore, in a mediation setup, more efficient direct or indirect effect estimators can be obtained by using outcome-adaptive LASSO to select variables for propensity score models by incorporating the outcome information. A simulation study is conducted to assess how different regularization methods can affect the performance of estimated natural direct and indirect effect odds ratios. Our simulation results show that regularizing propensity score models by outcome-adaptive LASSO can improve the efficiency of the natural effect estimators and by optimizing balance in the covariates, bias can be reduced in most cases. The regularization methods are then applied to MIMIC-III database, an ICU database developed by MIT.


Sign in / Sign up

Export Citation Format

Share Document