報告時間： 2019年12月12日周四下午14:00~15:00

報告地點： X2511

報告題目:

Regression Analysis with individual-specific patterns of missing covariates *

Abstract:

It is increasingly common to collect data from heterogeneous sources in practice. Two major challenges complicate the statistical analysis of such data. First, only a small proportion of units have complete information across all sources. Second, the missing data patterns vary across individuals. Our motivating online-loan data have 93% missing covariates where the missing pattern is individual-specific. The existing regression analysis with missing covariates either are inefficient or require additional modeling assumptions on the covariates. We propose a simple yet efficient iterative least squares estimator of the regression coefficient for the data with individual-specific missing patterns. Our method has several desirable features. First, it does not require any modeling assumptions on the covariates. Second, the imputation of the missing covariates involves feasible one-dimensional nonparametric regressions, and can maximally use the information across units and the relationship among the covariates. Third, the iterative least squares estimate is both computationally and statistically efficient. We study the asymptotic properties of our estimator and apply it to the motivating online-loan data.

*Joint work with Wei Liu, Wei Lan.

報告人簡介：林華珍，西南財經(jīng)大學(xué)統(tǒng)計學(xué)院教授、博導(dǎo)，統(tǒng)計研究中心主任，美國華盛頓大學(xué)生物統(tǒng)計系博士后，四川大學(xué)博士。教育部長江學(xué)者特聘教授，國家百千萬人才工程入選者，國家杰出青年科學(xué)基金獲得者，教育部新世紀(jì)優(yōu)秀人才， 第十一批四川省學(xué)術(shù)和技術(shù)帶頭人，第十批成都市有突出貢獻(xiàn)的優(yōu)秀專家，2019年首批“四川省教書育人名師”。 有多篇學(xué)術(shù)論文發(fā)表在AoS、JASA、JoE、JRSSB、Biometrika及Biometrcs等國際統(tǒng)計學(xué)和計量經(jīng)濟(jì)學(xué)頂級期刊上， 先后是國際統(tǒng)計學(xué)期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》Associate Editor， 國內(nèi)核心學(xué)術(shù)期刊《應(yīng)用概率統(tǒng)計》、《系統(tǒng)科學(xué)與數(shù)學(xué)》、《數(shù)理統(tǒng)計與管理》編委。