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作者：Ren, Haojie; Chen, Nan; Zou, Changliang
作者单位：Nankai University; National University of Singapore
摘要：We propose a procedure based on a high-breakdown mean function estimator to detect outliers in functional data. The robust estimator is obtained from a clean subset of observations, excluding potential outliers, by minimizing the least-trimmed-squares projection coefficients after functional principal component analysis. A threshold rule based on the asymptotic distribution of the functional score-based distance robustly controls the false positive rate and detects outliers effectively. Furthe...
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作者：Cai, J. -J.; Chavez-Demoulin, V.; Guillou, A.
作者单位：Delft University of Technology; University of Lausanne; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg
摘要：We propose an estimator of the marginal expected shortfall by considering a log transformation of a variable which has an infinite expectation. We establish the asymptotic normality of our estimator under general assumptions. A simulation study suggests that the estimation procedure is robust with respect to the choice of tuning parameters. Our estimator has lower bias and mean squared error than the empirical estimator when the latter is applicable. We illustrate our method on a tsunami datas...
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作者：Hristache, M.; Patilea, V.
作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
摘要：We consider a general statistical model defined by moment restrictions when data are missing at random. Using inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. Our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. The equivalence result sheds ...
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作者：Luo, Wei; Zhu, Yeying; Ghosh, Debashis
作者单位：City University of New York (CUNY) System; Baruch College (CUNY); University of Waterloo; Colorado School of Public Health
摘要：In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this paper we discuss how sufficient dimension reduction can be used to aid causal inference, and we propose a new estimator of the regression causal effect inspired by minimum average variance estimation. The estimator requires a weaker common support condition than propensity score-based approaches, and can be u...
