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A New Scope of Penalized Empirical Likelihood with high-dimensional Estimating Equations
澳门新葡8455最新网站:2017年05月04日 09:15 点击数:

报告人:常晋源

报告地点:澳门新葡8455最新网站四楼学术报告厅

报告澳门新葡8455最新网站:2017年05月06日星期六14:00-14:30

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报告摘要:

Statistical methods with empirical likelihood (EL) are appealing and effective in conjunction with estimating equations through which useful data information can be adaptively and flexibly incorporated. To overcome the challenges, we begin our study with a careful investigation on high-dimensional EL from a new scope targeting at estimating a high-dimensional sparse model parameters. We show that the new scope provides an opportunity for relaxing the stringent requirement on the dimensionality of the model parameter. Motivated by the new scope, we then propose a new penalized EL by applying two penalty functions respectively regularizing the model parameters and the associated Lagrange multipliers in the optimizations of EL. By penalizing the Lagrange multiplier to encourage its sparsity, we show that drastic dimension reduction in the number of estimating equations can be effectively achieved without compromising the validity and consistency of the resulting estimators. Allowing both the dimensionalities of model parameters and estimating equations growing exponentially with the sample size, our theory demonstrates that the estimator from our new penalized EL is sparse and consistent with asymptotically normally distributed nonzero components.

主讲人概况:

常晋源现任教于西南财经大学,副教授。

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