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Semiparametric model averaging prediction for binary response

发布时间：2022-06-15 作者： 浏览次数：
Speaker: 夏小超 DateTime: 2022年6月20日（周一）上午10：00-11：00
Brief Introduction to Speaker:

夏小超，现为重庆大学数学与统计学院副教授，研究兴趣：海量数据分析、高维特征筛选、模型平均和非参半参模型。曾主持1项国家自然科学基金项目、1项湖北省自然科学基金项目，在Journal of Econometrics, Biometrics、Statistica Sinica、Scandinavian Journal of Statistics、Computational Statistics & Data Analysis等SCI期刊上发表论文十余篇，任美国《数学评论》评论员，中国现场统计研究会第十一届理事。

 Place: 腾讯会议：910495604
Abstract:Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a linear combination of one-dimensional marginal score functions. The weight parameters involved in the approximation are obtained by first smoothing the nonparametric marginal scores and then applying the parametric model averaging via a maximum likelihood estimation. The proposed SMAP provides greater flexibility than parametric models while being more stable than a fully nonparametric approach. Theoretical properties are investigated in two practical scenarios: (i) covariates are conditionally independent given the ...
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