2022

最新文章

• Bayesian context trees: Modelling and exact inference for discrete time series AI翻译
  作者：Kontoyiannis, Ioannis; Mertzanis, Lambros; Panotopoulou, Athina; Papageorgiou, Ioannis; Skoularidou, Maria
  作者单位：University of Cambridge; Dartmouth College; University of Cambridge; University of Cambridge; MRC Biostatistics Unit
  摘要：We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact p...
• Bayesian inference for risk minimization via exponentially tilted empirical likelihood AI翻译
  作者：Tang, Rong; Yang, Yun
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The celebrated Bernstein von-Mises theorem ensures credible regions from a Bayesian posterior to be well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or partly specified, for example, in quantile regression, risk minimization based supervised/unsupervised learning and robust estimation...
• Model identification via total Frobenius norm of multivariate spectra AI翻译
  作者：McElroy, Tucker S.; Roy, Anindya
  作者单位：University System of Maryland; University of Maryland Baltimore County
  摘要：We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density-this is more demanding than Kullback-Leibler discrepancy, which is instead related to one-step ahead forecasting performance. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and make two applications ...
• High-dimensional quantile regression: Convolution smoothing and concave regularization AI翻译
  作者：Tan, Kean Ming; Wang, Lan; Zhou, Wen-Xin
  作者单位：University of Michigan System; University of Michigan; University of Miami; University of California System; University of California San Diego
  摘要：l(1)-penalized quantile regression (QR) is widely used for analysing high-dimensional data with heterogeneity. It is now recognized that the l(1)-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized M-estimation with strongly convex loss functions have been well studied, the extant literature on QR is relatively sile...
• A graph-theoretic approach to randomization tests of causal effects under general interference AI翻译
  作者：Puelz, David; Basse, Guillaume; Feller, Avi; Toulis, Panos
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Stanford University; University of California System; University of California Berkeley; University of Chicago
  摘要：Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighbouring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a given null hypothesis is shar...
• Jiwei Zhao's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Zhao, Jiwei
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• A statistical test to reject the structural interpretation of a latent factor model AI翻译
  作者：VanderWeele, Tyler J.; Vansteelandt, Stijn
  作者单位：Harvard University; Harvard University; Ghent University
  摘要：Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally efficacious. The assumption that...
• Structure learning for extremal tree models AI翻译
  作者：Engelke, Sebastian; Volgushev, Stanislav
  作者单位：University of Geneva; University of Toronto; University of Geneva
  摘要：Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we develop a data-driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and a new summary statistic, which we call the extremal variogram, can be used as weights for a mini...
• Robust generalised Bayesian inference for intractable likelihoods AI翻译
  作者：Matsubara, Takuo; Knoblauch, Jeremias; Briol, Francois-Xavier; Oates, Chris J.
  作者单位：Newcastle University - UK; Alan Turing Institute; University of London; University College London
  摘要：Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this context, the Stein discrepancy circumvents evaluation of the normalisation constant and produces...
• A kernel-expanded stochastic neural network AI翻译
  作者：Sun, Yan; Liang, Faming
  作者单位：Purdue University System; Purdue University
  摘要：The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression as the first hidden layer and reformulates the neural network as a latent variable model. The former maps the input vector into an infini...