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- W2379748222 abstract "How to do parameter estimation and variable selection simultaneously is a hot issue in the study of quantile regression for panel data models.On the base of the assumption that the fixed effect coefficients are subject to a novel conditional Laplace prior,the paper constructs a hierarchical Bayesian quantile regression model and gives the Gibbs sample algorithm for the unknown parameter estimation.In consideration of different explain variables should have different shrinkage degree,the proposed prior has the property of adaptivity,which could select the important explain variables in the model automatically.Furthermore,the slice Gibbs sample algorithm that the paper proposed is able to estimate the posteriori mean estimation of unknown parameter quickly and efficiently.Monte Carlo simulation study indicates that the proposed method is obviously superior to the existing methods in literatures on the accuracy of parameter estimation and variable selection.Finally,the paper gives a research of modeling the panel data including several macroeconomic indicators of our country and demonstrates the new method's capability of estimating parameters and doing variable selection." @default.
- W2379748222 created "2016-06-24" @default.
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- W2379748222 date "2014-01-01" @default.
- W2379748222 modified "2023-09-22" @default.
- W2379748222 title "Study on Adaptive Lasso Quantile Regression for Panel Data Models" @default.
- W2379748222 hasPublicationYear "2014" @default.
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