Biometrika

最新文章

• Low-risk population size estimates in the presence of capture heterogeneity AI翻译
  作者：Johndrow, J. E.; Lum, K.; Manrique-Vallier, D.
  作者单位：Stanford University; Indiana University System; Indiana University Bloomington
  摘要：Population estimation methods are used for estimating the size of a population from samples of individuals. In many applications, the probability of being observed in the sample varies across individuals, resulting in sampling bias. We show that in this setting, estimators of the population size have high and sometimes infinite risk, leading to large uncertainty in the population size. As an alternative, we propose estimating the population of individuals with observation probability exceeding...
• Bootstrapping spectral statistics in high dimensions AI翻译
  作者：Lopes, Miles E.; Blandino, Andrew; Aue, Alexander
  作者单位：University of California System; University of California Davis
  摘要：Statistics derived from the eigenvalues of sample covariance matrices are called spectral statistics, and they play a central role in multivariate testing. Although bootstrap methods are an established approach to approximating the laws of spectral statistics in low-dimensional problems, such methods are relatively unexplored in the high-dimensional setting. The aim of this article is to focus on linear spectral statistics as a class of prototypes for developing a new bootstrap in high dimensi...
• Integrating the evidence from evidence factors in observational studies AI翻译
  作者：Karmakar, B.; French, B.; Small, D. S.
  作者单位：University of Pennsylvania; Radiation Effects Research Foundation - Japan
  摘要：A sensitivity analysis for an observational study assesses how much bias, due to nonrandom assignment of treatment, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. The evidence for a treatment effect can be strengthened if two different analyses, which could be affected by different types of biases, are both somewhat insensitive to bias. The finding from the observational study is then said to be replicated. Evidence factors...
• Discussion of Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Rosenblatt, Jonathan D.; Ritov, Ya'acov; Goeman, Jelle J.
  作者单位：Ben-Gurion University of the Negev; University of Michigan System; University of Michigan; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC
• Rejoinder: Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Sesia, M.; Sabatti, C.; Candes, E. J.
  作者单位：Stanford University
• Conjugate Bayes for probit regression via unified skew-normal distributions AI翻译
  作者：Durante, Daniele
  作者单位：Bocconi University
  摘要：Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference proceeds by updating the priors for the coefficients, typically taken to be Gaussians, with the likelihood induced by probit or logit regressions for the responses. In this updating, t...
• Recovering covariance from functional fragments AI翻译
  作者：Descary, M. -H.; Panaretos, V. M.
  作者单位：University of Quebec; University of Quebec Montreal; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：We consider nonparametric estimation of a covariance function on the unit square, given a sample of discretely observed fragments of functional data. When each sample path is observed only on a subinterval of length , one has no statistical information on the unknown covariance outside a -band around the diagonal. The problem seems unidentifiable without parametric assumptions, but we show that nonparametric estimation is feasible under suitable smoothness and rank conditions on the unknown co...
• Pseudo-population bootstrap methods for imputed survey data AI翻译
  作者：Chen, S.; Haziza, D.; Leger, C.; Mashreghi, Z.
  作者单位：University of Oklahoma System; University of Oklahoma Health Sciences Center; Universite de Montreal; University of Winnipeg
  摘要：The most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and populati...
• Discussion of Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Jewell, S. W.; Witten, D. M.
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• Counting process-based dimension reduction methods for censored outcomes AI翻译
  作者：Sun, Qiang; Zhu, Ruoqing; Wang, Tao; Zeng, Donglin
  作者单位：University of Toronto; University of Illinois System; University of Illinois Urbana-Champaign; Shanghai Jiao Tong University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimati...