報告人:馬海強
時 間: 2021年11月25日14:30-15:30
地 點:騰訊會議 840519082
題 目: Pseudo-Bayesian Classified Mixed Model Prediction
摘 要：We propose a new classified mixed model prediction (CMMP) procedure, called pseudo-Bayesian CMMP, that utilizes network information in matching the group index between the training data and new data, whose characteristics of interest one wishes to predict. The current CMMP procedures (Jiang et al. 2018; Sun et al. 2018) do not incorporate such information; as a result, the methods are not consistent in terms of matching the group index. Although, as the number of training data groups increases, the current CMMP method can predict the mixed effects of interest consistently, its accuracy is not guaranteed when the number of groups is moderate, as is the case in many potential applications. The proposed pseudo-Bayesian CMMP procedure assumes a flexible working probability model for the group index of the new observation to match the index of a training data group, which may be viewed as a pseudo prior. We show that, given any working model satisfying mild conditions, the pseudo Bayesian CMMP procedure is consistent and asymptotically optimal both in term of matching the group index and in terms of predicting the mixed effect of interest associated with the new observations. The theoretical results are fully supported by results of empirical studies, including Monte-Carlo simulations and real-data validation.
報告人簡介：
馬海強，博士，江西財經大學統計必一碩士生導師，2016年畢業於復旦大學管必一体育平台概率論與數理統計專業，師從朱仲義教授，主要的研究方向是函數型數據和分位數回歸. 目前以第一作者在國內外統計學術期刊發表SCI學術論文8篇；先後主持國家自然科學基金青年項目(已結題)👺🚡、國家自然科學基金地區項目(在研)、中國博士後面上項目各1項（在研）🙇🏽♂️，主持省部級項目4項。