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作者：Chopin, Nicolas; Crucinio, Francesca R.; Singh, Sumeetpal S.
作者单位：Institut Polytechnique de Paris; ENSAE Paris; University of Turin; University of Wollongong
摘要：Given a smooth function $ f $, we develop a general approach to turn Monte Carlo samples with expectation $ m $ into an unbiased estimate of $ f(m) $. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $ f $ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy to set their tuning parameters (which depend on $ m $) automatically, with a view to making the whole approach simple to use. We ...
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作者：Rudolph, K. E.; Williams, N. T.; Stuart, E. A.; Diaz, I
作者单位：Columbia University; Johns Hopkins University; New York University
摘要：We develop flexible, semiparametric estimators of the average treatment effect transported to a new target population, which offer potential efficiency gains. Transport may be of value when the average treatment effect may differ across populations. We consider the setting where differences in the average treatment effect are due to differences in the distribution of effect modifiers, baseline covariates that modify the treatment effect. First, we propose a collaborative one-step semiparametri...
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作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
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作者：Martinussen, Torben; Vansteelandt, Stijn
作者单位：University of Copenhagen; Ghent University
摘要：Cox regression is the default approach to evaluating the (relative) effect of two treatments on a survival endpoint. This standard framework has nonetheless been criticized for its canonical effect measure, the hazard ratio, having a subtle interpretation, thereby hindering policy-making. This in turn has prompted interest in other effects measures, such as the difference in restricted mean survival time, the net benefit and the win ratio, which have become increasingly popular. Developments i...
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作者：Roycraft, B.; Rajaratnam, B.
作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis
摘要：Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high-dimensional applications. A part of their wide appeal stems from the significantly low sample sizes required for existence of the estimators, especially in comparison with the classical full covariance model. For undirected Gaussian graphical models, the minimum sample size required for the existence of maximum likelihood estimators had been an open question for almost half a century, and h...
