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作者：Lee, Myoung-Jae
作者单位：Korea University
摘要：Propensity score matching is widely used to control covariates when analysing the effects of a nonrandomized binary treatment. However, it requires several arbitrary decisions, such as how many matched subjects to use and how to choose them. In this paper a simple least squares estimator is proposed, where the treatment, and possibly the response variable, is replaced by the propensity score residual. The proposed estimator controls covariates semiparametrically if the propensity score functio...
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作者：Wang, Z.; Kim, J. K.; Yang, S.
作者单位：Iowa State University; North Carolina State University
摘要：Statistical inference with complex survey data is challenging because the sampling design can be informative, and ignoring it can produce misleading results. Current methods of Bayesian inference under complex sampling assume that the sampling design is noninformative for the specified model. In this paper, we propose a Bayesian approach which uses the sampling distribution of a summary statistic to derive the posterior distribution of the parameters of interest. Asymptotic properties of the m...
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作者：Cao, Yuanpei; Lin, Wei; Li, Hongzhe
作者单位：University of Pennsylvania; Peking University
摘要：Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternative...
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作者：Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.
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作者：Green, Peter J.; Thomas, Alun
作者单位：University of Technology Sydney; Utah System of Higher Education; University of Utah
摘要：We present a new kind of structural Markov property for probabilistic laws on decomposable graphs, which allows the explicit control of interactions between cliques and so is capable of encoding some interesting structure. We prove the equivalence of this property to an exponential family assumption, and discuss identifiability, modelling, inferential and computational implications.
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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：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：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：Spady, R. H.; Stouli, S.
作者单位：University of Oxford; University of Bristol
摘要：We propose dual regression as an alternative to quantile regression for the global estimation of conditional distribution functions. Dual regression provides the interpretational power of quantile regression while avoiding the need to repair intersecting conditional quantile surfaces. We introduce a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models, and us...
