我院刘史诗博士以第一作者在SCI统计学期刊《Statistica Sinica》发表论文《MODEL AVERAGING ESTIMATION FOR PARTIALLY LINEAR FUNCTIONAL SCORE MODELS》（全文链接：https://doi.org/10.5705/ss.202023.0067）。
《Statistica Sinica》创刊于1991 年，由中央研究院统计科学研究所（ISSAS）与国际泛华统计学会（ICSA） 联合主办，是亚洲首个被 SCI 收录的统计学期刊。期刊致力于发表统计学与数据科学领域高质量原创成果，在国际统计学研究领域具有较高的学术影响力。
01 论文题目
MODEL AVERAGING ESTIMATION FOR PARTIALLY LINEAR FUNCTIONAL SCORE MODELS
02 论文摘要
The scalar-on-function regression is quite useful for modelling mixed-data in the context of scalar and functional variables. Under this class of regression, the paper aims at proposing a compelling alternative to model selection methods to address model selection uncertainty. The considered models characterize a scalar response using parametric effect of the scalar predictors and nonparametric effect of a functional predictor, and a model averaging estimation is developed based on Mallows-type criterion to assign weights for averaging. Further, the asymptotic optimality of the resulting estimator, in terms of achieving the smallest possible squared error loss, is established. Besides, simulation studies demonstrate its superiority to or comparability with some information criterion score-based model selection and averaging estimators. The proposed procedure is also applied to a mid-infrared spectra dataset for illustration.
03 作者介绍
刘史诗，博士，杭州电子科技大学经济学院讲师，研究方向为函数型数据分析、高维统计等，研究成果发表于《Statistica Sinica》《Journal of Statistical Computation and Simulation》《Journal of Multivariate Analysis》《Statistics and Computing》等国际期刊。