Biometrika

最新文章

• Nonparametric data segmentation in multivariate time series via joint characteristic functions AI翻译
  作者：Mcgonigle, E. T.; Cho, H.
  作者单位：University of Southampton; University of Bristol
  摘要：Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to ...
• Resampling methods with multiply imputed data AI翻译
  作者：Robbins, Michael W.; Burgette, Lane
  作者单位：RAND Corporation; RAND Corporation
  摘要：Resampling techniques have become increasingly popular for estimation of uncertainty. However, data are often fraught with missing values that are commonly imputed to facilitate analysis. This article addresses the issue of using resampling methods such as a jackknife or bootstrap in conjunction with imputations that have been sampled stochastically, in the vein of multiple imputation. We derive the theory needed to illustrate two key points regarding the use of resampling methods in lieu of t...
• A ball divergence-based measure for conditional independence testing with a local wild bootstrap AI翻译
  作者：Banerjee, Bilol; Bhattacharya, Bhaswar B.; Ghosh, Anil K.
  作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; University of Pennsylvania
  摘要：In this paper we introduce a new measure of conditional dependence between two random vectors $ {\boldsymbol{X}} $ and $ {\boldsymbol{Y}} $, given another random vector $ \boldsymbol{Z} $ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose an estimator of the measure using a kernel-averaging technique and derive its asymptotic distribution. Using this estimator, we construct a test for conditional independence ba...
• Testing for latent structure via the Wilcoxon-Wigner random matrix of normalized rank statistics AI翻译
  作者：Liao, Jonquil Z.; Cape, Joshua
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：This paper considers the problem of testing for latent structure in large symmetric data matrices. The goal is to develop statistically principled methodology that is flexible in its applicability, computationally efficient and insensitive to extreme data variation, thereby overcoming limitations of existing approaches. To this end, we introduce and systematically study certain symmetric matrices, called Wilcoxon-Wigner random matrices, whose entries are normalized rank statistics derived from...
• Dynamic clustering for heterophilic stochastic block models with time-varying node memberships AI翻译
  作者：Lin, K. Z.; Lei, J.
  作者单位：University of Washington; University of Washington Seattle; Carnegie Mellon University
  摘要：We consider a time-ordered sequence of networks stemming from stochastic block models in which nodes gradually change their membership over time, and no network at any single time-point contains sufficient signal strength to recover its community structure. To estimate the time-varying community structure, we develop the kernel-debiased sum of squares method that performs spectral clustering after a debiased sum-of-squared aggregation of adjacency matrices. Our theory demonstrates, via a novel...
• Identifying the source of spikes: factor or mixture? AI翻译
  作者：Lin, Zeqin; Liu, Yiming; Pan, Guangming; Yao, Chi; Zhou, Jia
  作者单位：Nanyang Technological University; Jinan University; Anhui University; Hefei University of Technology
  摘要：We consider the problem of identifying the pattern of latent variables in high-dimensional linear latent variable models, which can also be interpreted as determining the source of spiked singular values in the data matrix. Specifically, we test whether the latent variables are continuous or categorical, a distinction that is crucial for data interpretation, but challenging in the high-dimensional regime. To address this inference problem, we analyse the asymptotic behaviour of empirical measu...
• Invariant probabilistic prediction AI翻译
  作者：Henzi, Alexander; Shen, Xinwei; Law, Michael; Buhlmann, Peter
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：In recent years, there has been growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with the squared error loss, this article turns the focus towards probabilistic predictions, which aim to comprehensively quantify the uncertainty of an outcome variable given covariates. Within a causality-inspired framework, we investigate the invariance and robustness...
• On the minimax robustness against correlation and heteroscedasticity of ordinary least squares among generalized least squares estimates of regression AI翻译
  作者：Wiens, Douglas P.
  作者单位：University of Alberta
  摘要：We revisit a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. In a statistical context in which such functions measure loss, this says that the least favourable form of dependence is in fact independence, so that a procedure optimal for independent and identically distributed data can be minimax. In particular, the ordinary least squares estimate of a correctly specified regression response is minimax among generalized...
• High-dimensional covariance estimation by pairwise likelihood truncation AI翻译
  作者：Casa, A.; Ferrari, D.; Huang, Z.
  作者单位：Free University of Bozen-Bolzano; Royal Melbourne Institute of Technology (RMIT)
  摘要：Pairwise likelihood is an approximation of the full likelihood function that facilitates the analysis of high-dimensional covariance models. By combining marginal bivariate likelihoods, it effectively simplifies high-dimensional dependencies, making the estimation process more manageable. We introduce estimation of sparse high-dimensional covariance matrices by maximizing a truncated version of the pairwise likelihood function, obtained by including pairwise terms corresponding to nonzero cova...
• Doubly robust identification for bivariate causal discovery under unmeasured confounding AI翻译
  作者：Li, Wei; Duan, Rui; Li, Sai
  作者单位：Renmin University of China; Renmin University of China; Harvard University; Tsinghua University
  摘要：Learning causal relationships between pairs of complex traits from observational studies is of great interest in many scientific fields. However, most existing methods assume the absence of unmeasured confounding and restrict causal relationships between two traits to be unidirectional, assumptions that may be violated in real-world systems. In this paper, we address the problem of bivariate causal discovery in the presence of unmeasured confounding and potential feedback loops, leveraging pos...