報告題目：The Adaptive Projection Estimator with Enhanced Inference Efficiency
報 告 人：鄭澤敏教授 中國科學技術大學
報告時間：2020年6月11日 下午 13:30-14:30
報告地點：騰訊會議 ID：473 896 994
密碼: 200611
或點擊鍊接直接加入會議：
https://meeting.tencent.com/s/R0G9RWgYCnXD
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
As a popular class of methods, inference via the de-biased estimators typically requires a large sample size to guarantee the asymptotic normality and allows a relatively small number of nonzero coefficients above the identifiable level. To alleviate such constraints and enhance the inference efficiency, we develop a new inference procedure via an adaptive projection estimator, which is based on the adaptive or thogonalization vector. This or thogonalization vector is adaptive in that it is orthogonal to the other covariate vectors corresponding to the identifiable coefficients, and at the same time being a relaxed or thogonalization against the remaining unidentifiable covariates. In this way, it completely removes the impacts of identifiable coefficients and controls that of the unidentifiable ones at a neglectable level, yielding much weaker constraint on both the sample size and the number of nonzero coefficients.
報告人簡介：
鄭澤敏，男，現為中國科學技術大學管理學院教授、統計與金融系主任、博士生導師，其研究方向是高維統計推斷和大數據問題。研究成果發表在Journal of the Royal Statistical Society: Series B（JRSSB）、Operations Research（OR）、Annals of Statistics（AOS）、Journal of Machine Learning Research（JMLR）等國際統計學、機器學習及管理優化頂級期刊上，曾獲南加州大學授予的優秀科研獎和美國數理統計協會頒發的科研新人獎。
