Biometrika

最新文章

• Partially factorized variational inference for high-dimensional mixed models AI翻译
  作者：Goplerud, M.; Papaspiliopoulos, O.; Zanella, G.
  作者单位：University of Texas System; University of Texas Austin; Bocconi University
  摘要：While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We...
• Powerful partial conjunction hypothesis testing via conditioning AI翻译
  作者：Liang, B.; Zhang, L.; Janson, L.
  作者单位：Harvard University
  摘要：A partial conjunction hypothesis test combines information across a set of base hypotheses to determine whether some subset is nonnull. Partial conjunction hypothesis tests arise in a diverse array of fields, but standard partial conjunction hypothesis testing methods can be highly conservative, leading to low power especially in low-signal settings commonly encountered in applications. In this paper, we introduce the conditional partial conjunction hypothesis test, a new method for testing a ...
• 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 ...
• Exact sampling of spanning trees via fast-forwarded random walks AI翻译
  作者：Tam, Edric; Dunson, David B.; Duan, Leo L.
  作者单位：Stanford University; Duke University; State University System of Florida; University of Florida
  摘要：Tree graphs are used routinely in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte Carlo methods tend to rely on local moves, often leading to poor mixing. A promising approach is to instead directly sample spanning trees on an auxiliary graph. Current spanning tree samplers, such as the celebrated Aldous-Broder algorithm, rely predominantly on simulating random walks that are required to visit all...
• Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models AI翻译
  作者：Xu, Tong; Taeb, Armeen; Kucukyavuz, Simge; Shojaie, Ali
  作者单位：Northwestern University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We ...
• 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...
• An omitted variable bias framework for sensitivity analysis of instrumental variables AI翻译
  作者：Cinelli, Carlos; Hazlett, Chad
  作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles
  摘要：We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (ext...
• Transfer learning for piecewise-constant mean estimation: optimality, l1 and l0 penalization AI翻译
  作者：Wang, F.; Yu, Y.
  作者单位：University of Warwick
  摘要：We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to target data. We first investigate transfer learning estimators that respectively employ l(0) and l(1) penalties for unisource data scenarios and then generalize these estimators to accommodate multisources. To further reduce estimation errors, especially when some sources significantly differ from the target, we introduce an informative source...
• Randomization inference when N equals one AI翻译
  作者：Liang, Tengyuan; Recht, Benjamin
  作者单位：University of Chicago; University of California System; University of California Berkeley
  摘要：For decades, $ N $-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been used in certain medical contexts. However, due to effects that accumulate over long time windows and interventions that have complex evolution, a lack of robust inference tools has limited the widespread applicability of such $ N $-of-1 designs. This work combines techniques from experimental design in causal inference and system identification from control theory to p...