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作者：Lei, J.; Lin, K. Z.
作者单位：Carnegie Mellon University
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作者：Dehling, H.; Fried, R.; Wendler, M.
作者单位：Ruhr University Bochum; Dortmund University of Technology; Otto von Guericke University
摘要：We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges-Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and...
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作者：Kong, Xinbing
作者单位：Nanjing Audit University
摘要：We introduce a random-perturbation-based rank estimator of the number of factors of a large-dimensional approximate factor model. An expansion of the rank estimator demonstrates that the random perturbation reduces the biases due to the persistence of the factor series and the dependence between the factor and error series. A central limit theorem for the rank estimator with convergence rate higher than root n gives a new hypothesis-testing procedure for both one-sided and two-sided alternativ...
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作者：Lee, C. E.; Zhang, X.; Shao, X.
作者单位：University of Tennessee System; University of Tennessee Knoxville; Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We propose a new nonparametric conditional mean independence test for a response variable Y and a predictor variable X where either or both can be function-valued. Our test is built on a new metric, the so-called functional martingale difference divergence, which fully characterizes the conditional mean dependence of Y given X and extends the martingale difference divergence proposed by Shao & Zhang (2014). We define an unbiased estimator of functional martingale difference divergence by using...
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作者：Li, Wei; Gu, Yuwen; Liu, Lan
作者单位：Renmin University of China; University of Connecticut; University of Minnesota System; University of Minnesota Twin Cities
摘要：For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This ...
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作者：Cui, Xia; Li, Runze; Yang, Guangren; Zhou, Wang
作者单位：Guangzhou University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Jinan University; National University of Singapore
摘要：This paper is concerned with empirical likelihood inference on the population mean when the dimension p and the sample size n satisfy p/n -> c is an element of [1, infinity). As shown in Tsao (2004), the empirical likelihood method fails with high probability when p/n > 1/2 because the convex hull of the n observations in R-p becomes too small to cover the true mean value. Moreover, when p > n, the sample covariance matrix becomes singular, and this results in the breakdown of the first sandwi...
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作者：Cao, Yuanpei; Zhang, Anru; Li, Hongzhe
作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison
摘要：Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where bacterial composition is estimated based on the sequencing read counts. Due to limited sequencing depth and DNA dropouts, many rare bacterial taxa might not be captured in the final sequencing reads, which results in many zero counts. Naive composition estimation using count normalization leads to many zero proportions, which tend to result in inaccurate estimates of bacterial abundance a...
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作者：Lee, A.; Whiteley, N.
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作者：Yiu, A.; Goudie, R. J. B.; Tom, B. D. M.
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of freque...
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作者：Mukhopadhyay, Subhadeep; Wang, Kaijun
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Fred Hutchinson Cancer Center
摘要：High-dimensional k-sample comparison is a common task in applications. We construct a class of easy-to-implement distribution-free tests based on new nonparametric tools and unexplored connections with spectral graph theory. The test is shown to have various desirable properties and a characteristic exploratory flavour that has practical consequences for statistical modelling. Numerical examples show that the proposed method works surprisingly well across a broad range of realistic situations.
