Semiparametric regression estimation in the presence of dependent censoring

By Andrea Rotnitzky, James M. Robins. 1995

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Citations in the corpus

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016, background): “Doubly robust estimators were introduced and remain popular in the statistics community (Rotnitzky & Robins, 1995; Hee-jung & Robins, 2005).”
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (2015, methodology): “The DR technique was ﬁrst studied in statistics (Rotnitzky & Robins, 1995) to improve the robustness of estimation against model misspeciﬁcation, and a DR estimator has been developed for dynamic treatment regime (Murphy et al., 2001).”
Doubly Robust Policy Evaluation and Optimization (2014, background): “A similar idea has been suggested earlier by a number of authors for diﬀerent estimation problems (Cassel, S¨arndal and Wretman, 1976; Rotnitzky and Robins, 1995; Robins and Rotnitzky, 1995; Murphy, van der Laan and Robins, 2001; Robins, 1998).”
Doubly Robust Off-policy Evaluation for Reinforcement Learning (2015, background): “DR was first studied in statistics (Rotnitzky & Robins, 1995) to improve robustness of estimations when there can be model misspecification, and a DR estimator is developed for dynamic treatment regime (Murphy et al., 2001), to estimate the value as a function of some static side information .”
A Distributional Approach for Causal Inference Using Propensity Scores (2006, unknown): “Rotnitzky and Robins (1995) proposed the estimator µ̂IPW,ext = 1 n n∑ i=1 Ti π(Xi; γ̃ , θ̃ ) Yi, where (γ̃ , θ̃ ) solves 0 = ∑ni=1(f(Xi),π−1(Xi; γ̂ ) (α̂ 1 × g(Xi))) (Ti − π(Xi;γ , θ)) for the extended model P(T = 1| X) = (γ f(X) + θ π−1(X; γ̂ ) (α̂ 1 g(X))).”
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score (2000, unknown): “Robins and Rotnitzky 1995 and Robins, Rotnitzky and Zhao 1995 and Rotnitzky and Robins 1995 study inference for parameters in a regression model with missing data, using essentially the missing at random MAR, Rubin, 1976; Little and Rubin, 1987 assumption.”
A paradox concerning nuisance parameters and projected estimating functions (2004, background): “for β; see Rotnitzky & Robins (1995) for detailed discussion on this type of estimating”
Mean-Squared-Error Calculations for Average Treatment Effects (2005, background): “Typically results are presented concerning the rates at which the smoothing parameters go to their limiting values, without specific recommendations regarding their values (Hahn, 1998; Hirano, Imbens and Ridder, 2003; Heckman, Ichimura and Todd, 1998; Rotnitzky and Robins, 1995; Robins, Rotnitzky an”
Semiparametric efficiency in GMM models with auxiliary data (2007, methodology): “Examples include inference in models with attrition or nonresponse [e.g., Little and Rubin (2002), Robins and Rotnitzky (1995), Rotnitzky and Robins (1995), Wooldridge (2002), Wooldridge (2003)], the estimation of treatment effects [see e.g., the references surveyed in Heckman, LaLonde and Smith…”
Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, background): “More discussions on this counter-intuitive phenomenon can be found in Rotnitzky & Robins (1995).”
Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects (2008, unknown): “Robins, Rotnitzky, and Zhao (1994), Robins and Rotnitzky (1995) and Rotnitzky and Robins (1995) presented a unified framework for calculating the efficient projection into tangent spaces of Newey (1990b) and Bickel, Klaassen, Ritov, and Wellner (1993) when the tangent spaces are defined by general…”
Bounded, efficient and doubly robust estimation with inverse weighting (2010, unknown): “Substitution of π̂ext(m̂OLS) for π̂ML in μ̂IPW yields the estimator of Rotnitzky & Robins (1995), μ̂IPW,ext = μ̂{π̂ext(m̂OLS), 0}.”
Inverse probability weighted estimation for general missing data problems (2004, methodology): “In previous work, I considered IPW M-estimation to account for variable probability sampling [Wooldridge (1999)] and for attrition and nonresponse [Wooldridge (2002a)].”
Efficient estimation of average treatment effects using the estimated propensity score (2003, unknown): “Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, Todd (1998), and Rotnitzky and Robins (1995).”