ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION

By Peter M. Robinson. 1988

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Local Linear Forests (2018, methodology): “Wager and Athey (2018) proposed an extension of random forests for estimating CATEs, and Athey, Tibshirani, and Wager (2019) improved on the method by making it locally robust to confounding using the transformation of Robinson (1988).”
Metalearners for estimating heterogeneous treatment effects using machine learning (2017, neutral): “[32] Robinson PM (1988) Root-n-consistent semiparametric regression.”
Generalized random forests (2016, methodology): “…which sets αi(x) = 1/k in (2) for the k nearest neighbors of x in Euclidean distance, series instrumental variables regression, plain generalized random forests as described above, and finally centered generalized random forests, using residualization via marginal regressions as in Robinson (1988).”
On asymptotically optimal confidence regions and tests for high-dimensional models (2013, background): “When observing {(Yi,Xi,1,Zi)}i=1 this is the partially linear regression model, where √ n-consistency for the parametric part β0 1 can be achieved [40].”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, background): “ly normal estimates for low-dimensional components with nuisance parameters estimated by conventional nonparametric estimators such as kernels or series. See, for example, the important work by [15], [52], [42], [58], [2], [43], [51], [40], [16], [21], and [1]. The major point of departure from the ”
Quasi-oracle estimation of heterogeneous treatment effects (2017, methodology): “Conceptually, we draw from the literature on semiparametric eﬃciency and constructions of orthogonal moments including Robinson (1988) and, more broadly, Belloni et al. (2017), Bickel et al. (1998), Newey (1994), Robins (2004), Robins and Rotnitzky (1995), Robins et al. (2017), Tsiatis (2007), van…”
Inequality and Development Across and Within Countries (2006, methodology): “Therefore, a semi-parametric estimation method, rather than a nonparametric estimation method, is required, and the method used here follows Robinson (1988). Taking the conditional expectation of Eqn.”
Investor Flows and Fragility in Corporate Bond Funds (2017, methodology): “To begin, we follow Chevalier and Ellison (1997) and Robinson (1988) to estimate a semi-parametric regression of fund flows on past fund performance, where the relation between fund flows and performance has a flexible functional form.”
Linear Hypothesis Testing in Dense High-Dimensional Linear Models (2016, background): “Notice that, for the low-dimensional components of β∗, β̄ achieves semiparametric efficiency; see Robinson (1988). Therefore, for sparse a, a β̄ is a semiparametrically efficient estimator for a β∗.”
A Survey on Causal Inference (2020, methodology): “Jing Gao, and Aidong Zhang Different from the regular loss function adopted in X-learner, R-learner, proposed in [87] designs the loss function for CATE estimator based on the Robinson transformation [104]. The character “R” in R-learner denotes the Robinson transformation. The Robinson transformati”
Do Peers Affect Student Achievement in China's Secondary Schools? (2007, methodology): “35Methods to estimate this equation are introduced in Robinson (1988).”