基于LASSO和SCAD的缺失数据分位数模型半参数估计

IF 0.3 Q4 ECONOMICS

Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences Pub Date : 2022-09-30 DOI:10.33095/jeas.v28i133.2351

Aws Adnan Al-Tai, Qutaiba N. Nayef Al-Kazaz

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引用次数: 0

摘要

在本研究中，我们对LASSO和SCAD两种处理部分分位数回归模型的特殊方法进行了比较。(Nadaraya & Watson Kernel)对非参数部分进行估计，并采用经验法则法对平滑带宽(h)进行估计。惩罚法在估计回归系数时是有效的，但在使用均值插值法估计缺失数据后，基于均方误差准则(MSE)的SCAD方法是最好的

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Semi parametric Estimators for Quantile Model via LASSO and SCAD with Missing Data

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

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Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences ECONOMICS-

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20.00%

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