526期

最新文章

• Functional Data Analysis of Dynamic PET Data AI翻译
  作者：Chen, Yakuan; Goldsmith, Jeff; Ogden, R. Todd
  作者单位：AT&T; Columbia University
  摘要：One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to refle...
• High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models AI翻译
  作者：Ghosh, Satyajit; Khare, Kshitij; Michailidis, George
  作者单位：State University System of Florida; University of Florida; State University System of Florida; University of Florida
  摘要：Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for regularized estimates of the ...
• Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables AI翻译
  作者：Frot, Benjamin; Jostins, Luke; McVean, Gilean
  作者单位：University of Oxford; University of Oxford; Wellcome Centre for Human Genetics; University of Oxford; Kennedy Institute for Rheumatology; University of Oxford
  摘要：We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as sparsistent (i.e., capable of recovering the graph structure). We then show how prox...
• Batch Effects Correction with Unknown Subtypes AI翻译
  作者：Luo, Xiangyu; Wei, Yingying
  作者单位：Chinese University of Hong Kong
  摘要：High-throughput experimental data are accumulating exponentially in public databases. Unfortunately, however, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed batch effects, and the latter is often modeled by subtypes. Existing methods either tackle batch effects provided that subtypes are known or cluster subtypes assuming that batch effects are absent. Consequently, there ...
• On Sensitivity Value of Pair-Matched Observational Studies AI翻译
  作者：Zhao, Qingyuan
  作者单位：University of Pennsylvania
  摘要：This article proposes a new quantity called the sensitivity value, which is defined as the minimum strength of unmeasured confounders needed to change the qualitative conclusions of a naive analysis assuming no unmeasured confounder. We establish the asymptotic normality of the sensitivity value in pair-matched observational studies. The theoretical results are then used to approximate the power of a sensitivity analysis and select the design of a study. We explore the potential to use sensiti...
• Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models AI翻译
  作者：Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo
  作者单位：Duke University; Massachusetts Institute of Technology (MIT); University of Tokyo
  摘要：This work proposes new inference methods for a regression coefficient of interest in a (heterogenous) quantile regression model. We consider a high-dimensional model where the number of regressors potentially exceeds the sample size but a subset of them suffices to construct a reasonable approximation to the conditional quantile function. The proposed methods are (explicitly or implicitly) based on orthogonal score functions that protect against moderate model selection mistakes, which are oft...
• Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization AI翻译
  作者：Wickramasuriya, Shanika L.; Athanasopoulos, George; Hyndman, Rob J.
  作者单位：University of Auckland; Monash University
  摘要：Large collections of time series often have aggregation constraints due to product or geographical groupings. The forecasts for the most disaggregated series are usually required to add-up exactly to the forecasts of the aggregated series, a constraint we refer to as coherence. Forecast reconciliation is the process of adjusting forecasts to make them coherent. The reconciliation algorithm proposed by Hyndman et al. (2011) is based on a generalized least squares estimator that requires an esti...
• Interpretable High-Dimensional Inference Via Score Projection With an Application in Neuroimaging AI翻译
  作者：Vandekar, Simon N.; Reiss, Philip T.; Shinohara, Russell T.
  作者单位：University of Pennsylvania; University of Haifa
  摘要：In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with ...
• Joint Indirect Standardization When Only Marginal Distributions are Observed in the Index Population AI翻译
  作者：Wang, Yifei; Tancredi, Daniel J.; Miglioretti, Diana L.
  作者单位：University of California System; University of California San Francisco; University of California System; University of California Davis; University of California System; University of California Davis
  摘要：It is a common interest in medicine to determine whether a hospital meets a benchmark created from an aggregate reference population, after accounting for differences in distributions of multiple covariates. Due to the difficulties of collecting individual-level data, however, it is often the case that only marginal distributions of the covariates are available, making covariate-adjusted comparison challenging. We propose and evaluate a novel approach for conducting indirect standardization wh...
• Priors for the Long Run AI翻译
  作者：Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
  作者单位：Federal Reserve System - USA; Federal Reserve Bank - New York; Centre for Economic Policy Research - UK; European Central Bank; Northwestern University; National Bureau of Economic Research
  摘要：We propose a class of prior distributions that discipline the long-run behavior of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run prediction...