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作者：Claeskens, G.; Krivobokova, T.; Opsomer, J. D.
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作者：Tang, Yanlin; Wang, Huixia Judy; Barut, Emre
作者单位：Tongji University; George Washington University
摘要：Researchers sometimes have a priori information on the relative importance of predictors that can be used to screen out covariates. An important question is whether any of the discarded covariates have predictive power when the most relevant predictors are included in the model. We consider testing whether any discarded covariate is significant conditional on some pre-chosen covariates. We propose a maximum-type test statistic and show that it has a nonstandard asymptotic distribution, giving ...
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作者：Yiu, Sean; Su, Li
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimat...
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作者：Ding, Peng; Dasgupta, Tirthankar
作者单位：University of California System; University of California Berkeley; Rutgers University System; Rutgers University New Brunswick
摘要：Fisher randomization tests for Neyman's null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the F statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the F statistic in the Fisher randomization test can severely inflate the Type I error under Neyman's null hypothesis. We propose to use ...
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作者：Dette, H.; Guchenko, R.; Melas, V. B.; Wong, W. K.
作者单位：Ruhr University Bochum; Saint Petersburg State University; University of California System; University of California Los Angeles
摘要：Much work on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters. Recent work allows errors in the models to be nonnormally distributed but still requires the specification of the mean structures. Otsu (2008) proposed optimal discriminating designs for semiparametric models by generalizing the Kullback-Leibler optimality criterion proposed by Lpez-Fidalgo et al. (2007). This paper develops a relatively simple strategy for findin...
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作者：Stallard, Nigel; Kimani, Peter K.
作者单位：University of Warwick
摘要：Multi-arm multi-stage clinical trials compare several experimental treatments with a control treatment, with poorly performing treatments dropped at interim analyses. This leads to inferential challenges, including the construction of unbiased treatment effect estimators. A number of estimators which are unbiased conditional on treatment selection have been proposed, but are specific to certain selection rules, may ignore the comparison to the control and are not all minimum variance. We obtai...
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作者：Basse, Guillaume W.; Airoldi, Edoardo M.
作者单位：Harvard University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：In this paper we consider how to assign treatment in a randomized experiment in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. We use these models to develop restricted randomization strategies for allocating treatment optimally, by minimizing the mean squared error of the estimated average treatment effec...
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作者：Matias, C.; Rebafka, T.; Villers, F.
作者单位：Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
摘要：We propose an extension of the stochastic block model for recurrent interaction events in continuous time, where every individual belongs to a latent group and conditional interactions between two individuals follow an inhomogeneous Poisson process with intensity driven by the individuals' latent groups. We show that the model is identifiable and estimate it with a semiparametric variational expectation-maximization algorithm. We develop two versions of the method, one using a nonparametric hi...
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作者：Wong, Raymond K. W.; Chan, Kwun Chuen Gary
作者单位：Texas A&M University System; Texas A&M University College Station; University of Washington; University of Washington Seattle
摘要：Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numeri...
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作者：Wang, Lan; Van Keilegom, Ingrid; Maidman, Adam
作者单位：University of Minnesota System; University of Minnesota Twin Cities; KU Leuven
摘要：We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analysing such data. By allowing different covariates to be relevant for modelling conditional quantile functions at different quantile levels, it provides a more complete picture of the conditional distribution of a response variable than mean regression. Existing work on penalized quantile regression has be...
