Journal of the Royal Statistical Society: Series B

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...
• An optimal design framework for lasso sign recovery AI翻译
  作者：Stallrich, Jonathan W.; Young, Kade; Weese, Maria L.; Smucker, Byran J.; Edwards, David J.
  作者单位：North Carolina State University; University System of Ohio; Miami University; University System of Ohio; Miami University; Henry Ford Health System; Henry Ford Health System; Michigan State University; Michigan State University College of Human Medicine; Virginia Commonwealth University; Citadel Military College South Carolina
  摘要：Supersaturated designs investigate more factors than there are runs and are often constructed under a criterion measuring a design's proximity to an unattainable orthogonal design. The most popular analysis identifies active factors by inspecting the solution path of a penalized estimator, such as the lasso. Recent criteria encouraging positive correlations between factors have been shown to produce designs with more definitive solution paths so long as the active factors have positive effects...
• Generalized universal inference on risk minimizers AI翻译
  作者：Dey, Neil; Martin, Ryan; Williams, Jonathan P.
  作者单位：North Carolina State University
  摘要：A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications. The present paper considers a very common case in machine learning, where the quantity of interest is the minimizer of a given risk (expe...
• 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 ...
• SymmPI: predictive inference for data with group symmetries AI翻译
  作者：Dobriban, Edgar; Yu, Mengxin
  作者单位：University of Pennsylvania; Washington University (WUSTL)
  摘要：Quantifying the uncertainty of predictions is a core problem in modern statistics. Methods for predictive inference have been developed under a variety of assumptions, often-for instance, in standard conformal prediction-relying on the invariance of the distribution of the data under special groups of transformations such as permutation groups. Moreover, many existing methods for predictive inference aim to predict unobserved outcomes in sequences of feature-outcome observations. Meanwhile, th...
• Moving beyond population variable importance: concept, theory and applications of individual variable importance AI翻译
  作者：Dai, Guorong; Shao, Lingxuan; Chen, Jinbo
  作者单位：Fudan University; University of Pennsylvania
  摘要：In a non-parametric regression setting, we introduce a novel concept of 'individual variable importance', which assesses the relevance of certain covariates to an outcome variable among individuals with specific characteristics. This concept holds practical importance for both risk assessment and association identification. For example, it can represent (i) the usefulness of expensive biomarkers in risk prediction for individuals at a specified baseline risk, or (ii) age-specific associations ...
• 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...
• Estimating maximal symmetries of regression functions via subgroup lattices AI翻译
  作者：Christie, Louis G.; Aston, John A. D.
  作者单位：University of Cambridge
  摘要：We present a method for estimating the maximal symmetry of a continuous regression function. Knowledge of such a symmetry can be used to significantly improve modelling by removing the modes of variation resulting from the symmetries. Symmetry estimation is carried out using hypothesis testing for invariance strategically over the subgroup lattice of a search group G acting on the feature space. We show that the estimation of the unique largest invariant subgroup of G generalizes useful tools ...
• Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling AI翻译
  作者：Chang, Jinyuan; Tang, Cheng Yong; Zhu, Yuanzheng
  作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Southwestern University of Finance & Economics - China
  摘要：In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to the complex optimization typi...