2期

最新文章

• Identifying the latent space geometry of network models through analysis of curvature AI翻译
  作者：Lubold, Shane; Chandrasekhar, Arun G.; McCormick, Tyler H.
  作者单位：University of Washington; University of Washington Seattle; Stanford University; National Bureau of Economic Research; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：A common approach to modelling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positive manifold curvature encourages more and tighter communities; negative curvature induces repulsion. We consistently estimate manifold type, dimension, and curvature from simply connected, complete Riemannian manifolds of constant curvature. We represent the graph as a noisy distance matrix based on the ties between ...
• Designing to detect heteroscedasticity in a regression model AI翻译
  作者：Lanteri, Alessandro; Leorato, Samantha; Lopez-Fidalgo, Jesus; Tommasi, Chiara
  作者单位：University of Milan; University of Navarra; University of Milan
  摘要：We consider the problem of designing experiments to detect the presence of a specified heteroscedastity in Gaussian regression models. We study the relationship of the D-s- and KL-criteria with the noncentrality parameter of the asymptotic chi-squared distribution of a likelihood-based test, for local alternatives. We found that, when the heteroscedastity depends on one parameter, the two criteria coincide asymptotically and that the D-1-criterion is proportional to the noncentrality parameter...
• John Kent's contribution to the Discussion of the 'Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Li, Jie; Fearnhead, Paul; Fryzlewicz, Piotr; Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science; Lancaster University
• Kanti Mardia's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Mardia, Kanti V.
  作者单位：University of Leeds
• Monotone response surface of multi-factor condition: estimation and Bayes classifiers AI翻译
  作者：Cheung, Ying Kuen; Diaz, Keith M.
  作者单位：Columbia University; Columbia University
  摘要：We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called product-of-independent-probability-escalation (PIPE)-classifiers) is a projection of Bayes classifiers on the constrained space. We prove that the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is conducted. The methods are applie...
• Nonparametric estimation of the continuous treatment effect with measurement error AI翻译
  作者：Huang, Wei; Zhang, Zheng
  作者单位：University of Melbourne; Renmin University of China; Renmin University of China
  摘要：We identify the average dose-response function (ADRF) for a continuously valued error-contaminated treatment by a weighted conditional expectation. We then estimate the weights nonparametrically by maximising a local generalised empirical likelihood subject to an expanding set of conditional moment equations incorporated into the deconvolution kernels. Thereafter, we construct a deconvolution kernel estimator of ADRF. We derive the asymptotic bias and variance of our ADRF estimator and provide...
• Model identification via total Frobenius norm of multivariate spectra AI翻译
  作者：McElroy, Tucker S.; Roy, Anindya
  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. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and apply the integrated Frobenius norm to fit time series models and test whether model residuals are white noise. The case of structural time series ...
• Bayesian Pyramids: identifiable multilayer discrete latent structure models for discrete data AI翻译
  作者：Gu, Yuqi; Dunson, David B.
  作者单位：Columbia University; Duke University
  摘要：High-dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable parsimonious models that perform dimension reduction and uncover meaningful latent structures from such discrete data. Identifiability is a fundamental requirement for valid modeling and inference in such scenarios, yet is challenging to address when there are complex latent structures. In this article, we propose a class of identifiable multilayer (pote...
• Causal inference on distribution functions AI翻译
  作者：Lin, Zhenhua; Kong, Dehan; Wang, Linbo
  作者单位：National University of Singapore; University of Toronto
  摘要：Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from the Euclidean space Rp. However, it is increasingly common that complex datasets are best summarized as data points in nonlinear spaces. In this paper, we present a novel framework of causal effects for outcomes from the Wasserstein space of cumulative distribution functions, which in contrast to the Euclidean sp...
• Debiased and thresholded ridge regression for linear models with heteroskedastic and correlated errors AI翻译
  作者：Zhang, Yunyi; Politis, Dimitris N.
  作者单位：The Chinese University of Hong Kong, Shenzhen; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：High-dimensional linear models with independent errors have been well-studied. However, statistical inference on a high-dimensional linear model with heteroskedastic, dependent (and possibly nonstationary) errors is still a novel topic. Under such complex assumptions, the paper at hand introduces a debiased and thresholded ridge regression estimator that is consistent, and is able to recover the model sparsity. Moreover, we derive a Gaussian approximation theorem for the estimator, and apply a...