On Optimal Asymptotic Tests of Composite Statistical Hypotheses

By J. B. Bartoo, P. Puri. 1967

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “To avoid such bias, we use debiased/“double” machine learning (DML) based on Neyman orthogonal scores that have zero derivative with respect to each first step learner (e.g., Neyman (1959); Belloni et al. (2014, 2015); Chernozhukov et al. (2016, 2018a); Foster and Syrgkanis (2019)).”
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (2019, unknown): “Neyman orthogonality holds as h→ 0 (Neyman, 1959).”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “(Efficiency) Note that in this example, μ0 not only creates the necessary orthogonality but also creates the efficient score for inference on the target parameter θ when the quasi-log-likelihood function is the true (possibly conditional) log-likelihood, as demonstrated by Neyman (1959).”
Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems (2013, methodology): “S T ] 30 D ec 2 01 4 Keywords: Instrument; Post-selection inference; Sparsity; Neyman’s Orthogonal Score test; Uniformly valid inference; Z-estimation.”
SCORE TESTS FOR ZERO-INFLATION AND OVER-DISPERSION IN GENERALIZED LINEAR MODELS (2005, background): “It often maintains, at least approximately, a preassigned level of significance (Bartoo and Puri (1967)) and often produces a statistic which is simple to calculate.”
A Simulation Comparison of Several Procedures for Testing the Poisson Assumption (2000, background): “Bartoo and Puri (1967) examined this optimality and showed that a further restriction must be imposed on the mixing distribution. Moran (1973) extended the results of Bartoo and Puri (1967), examining the case when the mixing distribution is symmetric (zero third central moment).”