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Open AccessArticle

Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices

by Fei Jin 1,2 and Lung-fei Lee 3,*
1
School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China
2
Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai 200433, China
3
Department of Economics, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(1), 8; https://doi.org/10.3390/econometrics6010008
Received: 1 December 2017 / Revised: 13 February 2018 / Accepted: 13 February 2018 / Published: 22 February 2018
An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the n -rate in a regular case. We propose to estimate such models by the adaptive lasso maximum likelihood and propose an information criterion to select the involved tuning parameter. We show that the penalized maximum likelihood estimator has the oracle properties. The method can implement model selection and estimation simultaneously and the estimator always has the usual n -rate of convergence. View Full-Text
Keywords: penalized maximum likelihood; singular information matrix; lasso; oracle properties penalized maximum likelihood; singular information matrix; lasso; oracle properties
MDPI and ACS Style

Jin, F.; Lee, L.-F. Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices. Econometrics 2018, 6, 8.

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