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Composite Quantile Regression for the Single-Index Model

Fan, Yan; Härdle, Wolfgang Karl; Wang, Weining; Zhu, Lixing - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2013

Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a...

Persistent link: https://www.econbiz.de/10010609988

Single-index-based CoVaR with very high-dimensional covariates

Fan, Yan; Härdle, Wolfgang; Wang, Weining; Zhu, Lixing - In: Journal of business & economic statistics : JBES ; a … 36 (2018) 2, pp. 212-226

Persistent link: https://www.econbiz.de/10011894611

Reverse Engineering Galactose Regulation in Yeast through Model Selection

Thorsson, Vesteinn; Hörnquist, Michael; Siegel, Andrew; … - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 28-28

shrinkage via the Lasso. …

Persistent link: https://www.econbiz.de/10005459170

A LASSO-penalized BIC for mixture model selection

Bhattacharya, Sakyajit; McNicholas, Paul - In: Advances in Data Analysis and Classification 8 (2014) 1, pp. 45-61

this paper, a LASSO-penalized BIC (LPBIC) is introduced to overcome this problem. This approach is illustrated based on …

Persistent link: https://www.econbiz.de/10010846123

Variable and boundary selection for functional data via multiclass logistic regression modeling

Matsui, Hidetoshi - In: Computational Statistics & Data Analysis 78 (2014) C, pp. 176-185

Penalties with an ℓ1 norm provide solutions in which some coefficients are exactly zero and can be used for selecting variables in regression settings. When applied to the logistic regression model, they also can be used to select variables which affect classification. We focus on the form of...

Persistent link: https://www.econbiz.de/10011056462

Comparing variable selection techniques for linear regression: LASSO and Autometrics

Epprecht, Camila; Guegan, Dominique; Veiga, Álvaro - HAL - 2013

(automatic general-to-specific selection) and LASSO (ℓ1-norm regularization). In a simulation study, we show the performance of …

Persistent link: https://www.econbiz.de/10011025644

Lasso-type and Heuristic Strategies in Model Selection and Forecasting

Savin, Ivan; Winker, Peter - Wirtschaftswissenschaftliche Fakultät, … - 2012

popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information … criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this …

Persistent link: https://www.econbiz.de/10010580995

A comparative study of the Lasso-type and heuristic model selection methods

Savin, Ivan - COMISEF - 2010

This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic … subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular …, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model …

Persistent link: https://www.econbiz.de/10008483960

Comparing variable selection techniques for linear regression: LASSO and Autometrics.

Epprecht, Camila; Guegan, Dominique; Veiga, Álvaro - Centre d'Économie de la Sorbonne, Université Paris 1 … - 2013

(automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of …

Persistent link: https://www.econbiz.de/10010720623

Financial ratios and bankruptcy predictions : an international evidence

Tian, Shaonan; Yu, Yan - In: International review of economics & finance : IREF 51 (2017), pp. 510-526

Persistent link: https://www.econbiz.de/10011754572