Biometrika

最新文章

• Spectral adjustment for spatial confounding AI翻译
  作者：Guan, Yawen; Page, Garritt L.; Reich, Brian J.; Ventrucci, Massimo; Yang, Shu
  作者单位：University of Nebraska System; University of Nebraska Lincoln; Brigham Young University; North Carolina State University; University of Bologna
  摘要：Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confoundin...
• Optimal row-column designs AI翻译
  作者：Zhou, Zheng; Zhou, Yongdao
  作者单位：Nankai University
  摘要：Row-column designs have been widely used in experiments involving double confounding. Among them, one that provides unconfounded estimation of all main effects and as many two-factor interactions as possible is preferred, and is called optimal. Most current work focuses on the construction of two-level row-column designs, while the corresponding optimality theory has been largely ignored. Moreover, most constructed designs contain at least one replicate of a full factorial design, which is not...
• Linearized maximum rank correlation estimation AI翻译
  作者：Shen, Guohao; Chen, Kani; Huang, Jian; Lin, Yuanyuan
  作者单位：Hong Kong Polytechnic University; Hong Kong University of Science & Technology; University of Iowa; Chinese University of Hong Kong
  摘要：We propose a linearized maximum rank correlation estimator for the single-index model. Unlike the existing maximum rank correlation and other rank-based methods, the proposed estimator has a closed-form expression, making it appealing in theory and computation. The proposed estimator is robust to outliers in the response and its construction does not need knowledge of the unknown link function or the error distribution. Under mild conditions, it is shown to be consistent and asymptotically nor...
• Optimal design of the Barker proposal and other locally balanced Metropolis-Hastings algorithms AI翻译
  作者：Vogrinc, Jure; Livingstone, Samuel; Zanella, Giacomo
  作者单位：University of Warwick; University of London; University College London; Bocconi University
  摘要：We study the class of first-order locally balanced Metropolis-Hastings algorithms introduced in Livingstone & Zanella (2022). To choose a specific algorithm within the class, the user must select a balancing function g : R+ -> R+ satisfying g(t) = tg(1/t) and a noise distribution for the proposal increment. Popular choices within the class are the Metropolis-adjusted Langevin algorithm and the recently introduced Barker proposal. We first establish a general limiting optimal acceptance rate of...
• Sampling distribution for single-regression Granger causality estimators AI翻译
  作者：Gutknecht, A. J.; Barnett, L.
  作者单位：University of Gottingen; University of Sussex
  摘要：The single-regression Granger-Geweke causality estimator has previously been shown to solve known problems associated with the more conventional likelihood ratio estimator; however, its sampling distribution has remained unknown. We show that, under the null hypothesis of vanishing Granger causality, the single-regression estimator converges to a generalized ?(2) distribution, which is well approximated by a & UGamma; distribution. We show that this holds too for Geweke's spectral causality av...
• On boosting the power of Chatterjee's rank correlation AI翻译
  作者：Lin, Z.; Han, F.
  作者单位：University of Washington; University of Washington Seattle
  摘要：The ingenious approach of Chatterjee (2021) to estimate a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020; Shi et al. 2022b) showed that independe...
• Gradient-based sparse principal component analysis with extensions to online learning AI翻译
  作者：Qiu, Yixuan; Lei, Jing; Roeder, Kathryn
  作者单位：Shanghai University of Finance & Economics; Carnegie Mellon University
  摘要：Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of mult...
• Nonparametric estimation of the intensity function of a spatial point process on a Riemannian manifold AI翻译
  作者：Ward, S.; Battey, H. S.; Cohen, E. A. K.
  作者单位：Imperial College London
  摘要：This paper is concerned with nonparametric estimation of the intensity function of a point process on a Riemannian manifold. It provides a first-order asymptotic analysis of the proposed kernel estimator for Poisson processes, supplemented by empirical work to probe the behaviour in finite samples and under other generative regimes. The investigation highlights the scope for finite-sample improvements by allowing the bandwidth to adapt to local curvature.
• Propensity scores in the design of observational studies for causal effects AI翻译
  作者：Rosenbaum, P. R.; Rubin, D. B.
  作者单位：University of Pennsylvania; Harvard University
  摘要：The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so the propensity score explicitly does not involve any outcome variables, in co...
• Testing generalized linear models with high-dimensional nuisance parameters AI翻译
  作者：Chen, Jinsong; Li, Quefeng; Chen, Hua Yun
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of North Carolina; University of North Carolina Chapel Hill; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. Here, we ...