513期

最新文章

• Generalizing Quantile Regression for Counting Processes With Applications to Recurrent Events AI翻译
  作者：Sun, Xiaoyan; Peng, Limin; Huang, Yijian; Lai, HuiChuan J.
  作者单位：Emory University; Rollins School Public Health; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：In survival analysis, quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest. In this article, we discuss how quantile regression can be extended to model counting processes and thus lead to a broader regression framework for survival data. We specifically investigate the proposed modeling of counting processes for recurrent events data. We show that the new recurrent events model retains the desirable features of quan...
• Comment AI翻译
  作者：Haneuse, Sebastien; Rivera, Claudia
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
• Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian Hierarchical Approach AI翻译
  作者：Pham, Lisa M.; Carvalho, Luis; Schaus, Scott; Kolaczyk, Eric D.
  作者单位：Boston University; Boston University
  摘要：Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here, our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression ...
• Statistical Inference in Hidden Markov Models Using k-Segment Constraints AI翻译
  作者：Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher
  作者单位：Athens University of Economics & Business; University of Oxford; Wellcome Centre for Human Genetics; University of Oxford; UK Research & Innovation (UKRI); Medical Research Council UK (MRC)
  摘要：Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward-backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-t...
• Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization AI翻译
  作者：Kang, Hyunseung; Zhang, Anru; Cai, T. Tony; Small, Dylan S.
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments' validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments' validity is equival...
• A Potential Tale of Two-by-Two Tables From Completely Randomized Experiments AI翻译
  作者：Ding, Peng; Dasgupta, Tirthankar
  作者单位：Harvard University
  摘要：Causal inference in completely randomized treatment-control studies with binary outcomes is discussed from Fisherian, Neymanian, and Bayesian perspectives, using the potential outcomes model. A randomization-based justification of Fisher's exact test is provided. Arguing that the crucial assumption of constant causal effect is often unrealistic, and holds only for extreme cases, some new asymptotic and Bayesian inferential procedures are proposed. The proposed procedures exploit the intrinsic ...
• Comment AI翻译
  作者：Louis, Thomas A.; Keiding, Niels
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of Copenhagen
• Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data AI翻译
  作者：Bartolucci, Francesco; Lupparelli, Monia
  作者单位：University of Perugia; University of Bologna
  摘要：In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and between clusters. For this aim, we propose an approach based on nested hidden (latent) Markov chains, which are associated with every sample unit and with every cluster. The approach allows us to account for the previously mentioned forms of unobserved heterogeneity in a dynamic fashion; it also a...
• Comment: Addressing the Need for Portability in Big Data Model Building and Calibration AI翻译
  作者：Patel, Chirag J.; Dominici, Francesca
  作者单位：Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health
• Minimax and Adaptive Estimation of Covariance Operator for Random Variables Observed on a Lattice Graph AI翻译
  作者：Cai, T. Tony; Yuan, Ming
  作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison
  摘要：Covariance structure plays an important role in high-dimensional statistical inference. In a range of applications including imaging analysis and fMRI studies, random variables are observed on a lattice graph. In such a setting, it is important to account for the lattice structure when estimating the covariance operator. In this article, we consider both minimax and adaptive estimation of the covariance operator over collections of polynomially decaying and exponentially decaying parameter spa...