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• Rerandomization and regression adjustment AI翻译
  作者：Li, Xinran; Ding, Peng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
  摘要：Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance in the analysis stage. We can embed blocking in a wider class of experimental design called rerandomization, and extend the classical analysis of covar...
• Robust inference on population indirect causal effects: the generalized front door criterion AI翻译
  作者：Fulcher, Isabel R.; Shpitser, Ilya; Marealle, Stella; Tchetgen, Eric J. Tchetgen
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Johns Hopkins University; University of Pennsylvania
  摘要：Standard methods for inference about direct and indirect effects require stringent no-unmeasured-confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of the paper is to introduce a new form of indirect effect, the population intervention indirect effect, that can be non-parametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the extent to which the effect ...
• The conditional permutation test for independence while controlling for confounders AI翻译
  作者：Berrett, Thomas B.; Wang, Yi; Barber, Rina Foygel; Samworth, Richard J.
  作者单位：University of Cambridge; University of Chicago
  摘要：We propose a general new method, the conditional permutation test, for testing the conditional independence of variables X and Y given a potentially high dimensional random vector Z that may contain confounding factors. The test permutes entries of X non-uniformly, to respect the existing dependence between X and Z and thus to account for the presence of these confounders. Like the conditional randomization test of Candes and co-workers in 2018, our test relies on the availability of an approx...
• Targeted sampling from massive block model graphs with personalized PageRank AI翻译
  作者：Chen, Fan; Zhang, Yini; Rohe, Karl
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：The paper provides statistical theory and intuition for personalized PageRank (called 'PPR'): a popular technique that samples a small community from a massive network. We study a setting where the entire network is expensive to obtain thoroughly or to maintain, but we can start from a seed node of interest and 'crawl' the network to find other nodes through their connections. By crawling the graph in a designed way, the PPR vector can be approximated without querying the entire massive graph,...
• A Bayesian hierarchical model for related densities by using Polya trees AI翻译
  作者：Christensen, Jonathan; Ma, Li
  作者单位：Duke University
  摘要：Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution...
• Making sense of sensitivity: extending omitted variable bias AI翻译
  作者：Cinelli, Carlos; Hazlett, Chad
  作者单位：University of California System; University of California Los Angeles
  摘要：We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounders, possibly acting non-linearly, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results. In particular, we introduce two nove...
• Correction: 'A new randomized response model' AI翻译
  作者：Grover, Lovleen Kumar; Kaur, Amanpreet
  作者单位：Guru Nanak Dev University
  摘要：We point out a minor mistake in published in 2006, 'A new randomized response model', which as been cited by various researchers, though no one has pointed out the mistake.
• Multiscale inference and long-run variance estimation in non-parametric regression with time series errors AI翻译
  作者：Khismatullina, Marina; Vogt, Michael
  作者单位：University of Bonn
  摘要：We develop new multiscale methods to test qualitative hypotheses about the function m in the non-parametric regression model Y-t,Y-T=m(t/T)+e(t) with time series errors e(t). In time series applications, m represents a non-parametric time trend. Practitioners are often interested in whether the trend m has certain shape properties. For example, they would like to know whether m is constant or whether it is increasing or decreasing in certain time intervals. Our multiscale methods enable us to ...
• Bayesian empirical likelihood inference with complex survey data AI翻译
  作者：Zhao, Puying; Ghosh, Malay; Rao, J. N. K.; Wu, Changbao
  作者单位：Yunnan University; State University System of Florida; University of Florida; Carleton University; University of Waterloo
  摘要：We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist...
• Renewable estimation and incremental inference in generalized linear models with streaming data sets AI翻译
  作者：Luo, Lan; Song, Peter X-K
  作者单位：University of Michigan System; University of Michigan
  摘要：The paper presents an incremental updating algorithm to analyse streaming data sets using generalized linear models. The method proposed is formulated within a new framework of renewable estimation and incremental inference, in which the maximum likelihood estimator is renewed with current data and summary statistics of historical data. Our framework can be implemented within a popular distributed computing environment, known as Apache Spark, to scale up computation. Consisting of two data-pro...