3期

最新文章

• Goodness-of-fit testing in high dimensional generalized linear models AI翻译
  作者：Jankova, Jana; Shah, Rajen D.; Buhlmann, Peter; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：We propose a family of tests to assess the goodness of fit of a high dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non-linearities and interaction effects, or for testing the significance of groups of variables. The methodology is based on extracting left-over signal in the residuals from an initial fit of a generalized linear model. This can be achieved by predicting this signal from the residu...
• Causal isotonic regression AI翻译
  作者：Westling, Ted; Gilbert, Peter; Carone, Marco
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：In observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose-response curve can be recovered by using the G-computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric assumptions, which carry significant risk of model misspecification. Non-parametric estimation in this context is challenging because in a non-parametr...
• Robust testing in generalized linear models by sign flipping score contributions AI翻译
  作者：Hemerik, Jesse; Goeman, Jelle J.; Finos, Livio
  作者单位：University of Oslo; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC); University of Padua
  摘要：Generalized linear models are often misspecified because of overdispersion, heteroscedasticity and ignored nuisance variables. Existing quasi-likelihood methods for testing in misspecified models often do not provide satisfactory type I error rate control. We provide a novel semiparametric test, based on sign flipping individual score contributions. The parameter tested is allowed to be multi-dimensional and even high dimensional. Our test is often robust against the mentioned forms of misspec...
• Inference for two-stage sampling designs AI翻译
  作者：Chauvet, Guillaume; Vallee, Audrey-Anne
  作者单位：Laval University
  摘要：Two-stage sampling designs are commonly used for household and health surveys. To produce reliable estimators with associated confidence intervals, some basic statistical properties like consistency and asymptotic normality of the Horvitz-Thompson estimator are desirable, along with the consistency of associated variance estimators. These properties have been mainly studied for single-stage sampling designs. In this work, we prove the consistency of the Horvitz-Thompson estimator and of associ...
• Causal mediation analysis for stochastic interventions AI翻译
  作者：Diaz, Ivan; Hejazi, Nima S.
  作者单位：Cornell University; Weill Cornell Medicine; University of California System; University of California Berkeley
  摘要：Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for...
• Testing relevant hypotheses in functional time series via self-normalization AI翻译
  作者：Dette, Holger; Kokot, Kevin; Volgushev, Stanislav
  作者单位：Ruhr University Bochum; University of Toronto
  摘要：We develop methodology for testing relevant hypotheses about functional time series in a tuning-free way. Instead of testing for exact equality, e.g. for the equality of two mean functions from two independent time series, we propose to test the null hypothesis of no relevant deviation. In the two-sample problem this means that an L2-distance between the two mean functions is smaller than a prespecified threshold. For such hypotheses self-normalization, which was introduced in 2010 by Shao, an...
• Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming AI翻译
  作者：Rosenblum, Michael; Fang, Ethan X.; Liu, Han
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Northwestern University
  摘要：Adaptive enrichment designs involve preplanned rules for modifying enrolment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g. based on a biomarker or risk score measured at baseline. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive enrichment designs are the decision rule for modifying enrolment, and the multiple-testin...
• Robust testing in generalized linear models by sign flipping score contributions Reply AI翻译
  作者：Singh, Sarjinder
  作者单位：Texas A&M University System; Texas A&M University Kingsville
• Unbiased Markov chain Monte Carlo methods with couplings AI翻译
  作者：Jacob, Pierre E.; O'Leary, John; Atchade, Yves F.
  作者单位：Harvard University; Boston University
  摘要：Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretica...
• A flexible framework for hypothesis testing in high dimensions AI翻译
  作者：Javanmard, Adel; Lee, Jason D.
  作者单位：University of Southern California; Princeton University
  摘要：Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high dimensional regime where the number of parameters exceeds the number of samples (p>n). To make informative inference, we assume that the model is approximately sparse, i.e. the effect of covariates on the response can be well approximated by conditioning on a relatively small number of covariates whose identities are unknown. We develop a framework for testing very ...