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Variable selection and coefficient estimation via composite quantile regression with randomly censored data

Jiang, Rong; Qian, Weimin; Zhou, Zhangong - In: Statistics & Probability Letters 82 (2012) 2, pp. 308-317

Composite quantile regression with randomly censored data is studied. Moreover, adaptive LASSO methods for composite …

Persistent link: https://www.econbiz.de/10010576151

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

Generalization of ℓ1 constraints for high dimensional regression problems

Alquier, Pierre; Hebiri, Mohamed - In: Statistics & Probability Letters 81 (2011) 12, pp. 1760-1765

setting, several estimators such as the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candes and Tao, 2007) are known to … satisfy interesting properties whenever the vector β∗ is sparse. Interestingly, both the LASSO and the Dantzig Selector can be … for the LASSO. For a well chosen s>0, this set is actually a confidence region for β∗. In this paper, we investigate the …

Persistent link: https://www.econbiz.de/10011040111

Variable selection in general multinomial logit models

Tutz, Gerhard; Pößnecker, Wolfgang; Uhlmann, Lorenz - In: Computational Statistics & Data Analysis 82 (2015) C, pp. 207-222

The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. A sparsity-inducing penalty is proposed that accounts for the special structure of multinomial models by...

Persistent link: https://www.econbiz.de/10011117679

Variable selection for joint mean and dispersion models of the inverse Gaussian distribution

Wu, Liucang; Li, Huiqiong - In: Metrika 75 (2012) 6, pp. 795-808

The choice of distribution is often made on the basis of how well the data appear to be fitted by the distribution. The inverse Gaussian distribution is one of the basic models for describing positively skewed data which arise in a variety of applications. In this paper, the problem of interest...

Persistent link: https://www.econbiz.de/10010896499

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

Korrektur des Tätigkeitsschlüssels der Bundesagentur für Arbeit mithilfe maschineller Lernverfahren

Schmidt, Elena - In: WISTA – Wirtschaft und Statistik 72 (2020) 6, pp. 37-47

Ziel des vorgestellten Projekts war, das Merkmal Vollzeit/Teilzeit des Tätigkeitsschlüssels in den Integrierten Erwerbsbiografien der Bundesagentur für Arbeit zu korrigieren. Dies sollte mithilfe des auch in der Verdienststrukturerhebung vorhandenen, aber manuell korrigierten Schlüssels...

Persistent link: https://www.econbiz.de/10012392752

Simultaneous Confidence Bands for Penalized Spline Estimators

Krivobokova, Tatyana; Kneib, Thomas; Claeskens, Gerda - 2009

In this paper we construct simultaneous confidence bands for a smooth curve using penalized spline estimators. We consider three types of estimation methods: (i) as a standard (fixed effect) nonparametric model, (ii) using the mixed model framework with the spline coefficients as random effects...

Persistent link: https://www.econbiz.de/10010329885

Penalized small area models for the combination of unit- and area-level data

Burgard, Jan Pablo; Krause, Joscha; Münnich, Ralf T. - 2019

measurement errors. We propose a multi-level model with level-specific penalization to overcome these issues and use unit- and …

Persistent link: https://www.econbiz.de/10012140847

High dimensional semiparametric moment restriction models

Dong, Chaohua; Gao, Jiti; Linton, Oliver Bruce - 2018

We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved. We propose an estimation method based on the sieve generalized method of...

Persistent link: https://www.econbiz.de/10011941554