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通过有效平衡得分估计响应均值AI 解读

We introduce effective balancing scores for estimation of the mean response under a missing at random mechanism. Unlike conventional balancing scores, the effective balancing scores are constructed via dimension reduction free of model specification. Three types of effective balancing scores are introduced: those that carry the covariate information about the missingness, the response, or both. They lead to consistent estimation with little or no loss in efficiency. Compared…

响应缺失时边际均值的半参数降维估计AI 解读

Model misspecification can be a concern for high-dimensional data. Nonparametric regression obviates model specification but is impeded by the curse of dimensionality. This paper focuses on the estimation of the marginal mean response when there is missingness in the response and multiple covariates are available. We propose estimating the mean response through nonparametric functional estimation, where the dimension is reduced by a parametric working index. The proposed…

We study the profile‐kernel and backfitting methods in partially linear models for clustered/longitudinal data. For independent data, despite the potential root‐n inconsistency of the backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance matrix, as shown by Opsomer & Ruppert (1999). In this paper, theoretical comparisons of the two estimators for multivariate responses are investigated. We show that, for correlated data, backfitting…