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最新文章

• Principal stratification with continuous post-treatment variables: nonparametric identification and semiparametric estimation AI翻译
  作者：Lu, Sizhu; Jiang, Zhichao; Ding, Peng
  作者单位：University of California System; University of California Berkeley; Sun Yat Sen University
  摘要：Post-treatment variables often complicate causal inference. They appear in many scientific problems, including non-compliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the mechanism of the treatment's im...
• A unified generalization of the inverse regression methods via column selection AI翻译
  作者：Jin, Yin; Luo, Wei
  作者单位：Zhejiang University
  摘要：A bottleneck of sufficient dimension reduction (SDR) in the modern era is that, among numerous methods, only sliced inverse regression (SIR) is generally applicable in high-dimensional settings. The higher-order inverse regression methods, which form a major family of SDR methods superior to SIR at the population level, suffer from the dimensionality of their intermediate matrix-valued parameters which have excessive columns. In this paper, we propose to use a small subset of columns of the ma...
• Empirical likelihood for manifolds AI翻译
  作者：Kurisu, Daisuke; Otsu, Taisuke
  作者单位：University of Tokyo; University of London; London School Economics & Political Science
  摘要：There has been growing interest in statistical analysis of random objects taking values in a non-Euclidean metric space. One important class of such objects consists of data on manifolds. This article is concerned with inference on the Fr & eacute;chet mean and related population objects on manifolds. We develop the concept of nonparametric likelihood for data on manifolds and propose general inference methods by adapting the theory of empirical likelihood. In addition to the basic asymptotic ...
• Online multivariate changepoint detection: leveraging links with computational geometry AI翻译
  作者：Pishchagina, Liudmila; Romano, Gaetano; Fearnhead, Paul; Runge, Vincent; Rigaill, Guillem
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Lancaster University; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); INRAE; Universite Paris Cite; INRAE; Universite Paris Saclay; AgroParisTech
  摘要：The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in p-dimensional data streams by leveraging a fascinating connection with computational geometry. This connection straightforwardly allows...
• Censored quantile regression with time-dependent covariates AI翻译
  作者：Chu, Chi Wing; Sit, Tony; Ying, Zhiliang
  作者单位：City University of Hong Kong; Chinese University of Hong Kong; Columbia University
  摘要：We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be properly handled. The new framework...
• Harmonized estimation of subgroup-specific treatment effects in randomized trials: the use of external control data AI翻译
  作者：Schwartz, Daniel; Saha, Riddhiman; Ventz, Steffen; Trippa, Lorenzo
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty ...
• Pretraining and the lasso AI翻译
  作者：Craig, Erin; Pilanci, Mert; Le Menestrel, Thomas; Narasimhan, Balasubramanian; Rivas, Manuel A.; Gullaksen, Stein-Erik; Dehghannasiri, Roozbeh; Salzman, Julia; Taylor, Jonathan; Tibshirani, Robert
  作者单位：Stanford University; Stanford University; Stanford University; Stanford University; University of Bergen; Haukeland University Hospital; University of Bergen; Stanford University; Stanford Medicine
  摘要：Pre-training is a powerful paradigm in machine learning to pass information across models. For example, suppose one has a modest-sized dataset of images of cats and dogs and plans to fit a deep neural network to classify them. With pre-training, we start with a neural network trained on a large corpus of images of not just cats and dogs but hundreds of classes. We fix all network weights except the top layer(s) and fine tune on our dataset. This often results in dramatically better performance...
• Identification and multiply robust estimation in causal mediation analysis across principal strata AI翻译
  作者：Cheng, Chao; Li, Fan
  作者单位：Yale University; Yale University
  摘要：We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consisten...
• Doubly robust conditional independence testing with generative neural networks AI翻译
  作者：Zhang, Yi; Huang, Linjun; Yang, Yun; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Maryland; University of Maryland College Park; Washington University (WUSTL); Washington University (WUSTL)
  摘要：This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a generative neural network (...
• Efficient nonparametric estimators of discrimination measures with censored survival data AI翻译
  作者：Breum, Marie Skov; Martinussen, Torben
  作者单位：University of Copenhagen
  摘要：Discrimination measures such as the concordance index and the cumulative-dynamic time-dependent area under the ROC-curve are widely used in the medical literature for evaluating the predictive accuracy of a scoring rule which relates a set of prognostic markers to the risk of experiencing a particular event. Often the scoring rule being evaluated in terms of discriminatory ability is the linear predictor of a survival regression model such as the Cox proportional hazards model. This has the un...