4期

最新文章

• BEYOND HC: MORE SENSITIVE TESTS FOR RARE/WEAK ALTERNATIVES AI翻译
  作者：Porter, Thomas; Stewart, Michael
  作者单位：University of Melbourne; University of Sydney
  摘要：Higher criticism (HC) is a popular method for large-scale inference problems based on identifying unusually high proportions of small p-values. It has been shown to enjoy a lower-order optimality property in a simple normal location mixture model which is shared by the 'tailor-made' parametric generalised likelihood ratio test (GLRT) for the same model; however, HC has also been shown to perform well outside this 'narrow' model. We develop a higher-order framework for analysing the power of th...
• BAYESIAN ANALYSIS OF THE COVARIANCE MATRIX OF A MULTIVARIATE NORMAL DISTRIBUTION WITH A NEW CLASS OF PRIORS AI翻译
  作者：Berger, James O.; Sun, Dongchu; Song, Chengyuan
  作者单位：Duke University; University of Nebraska System; University of Nebraska Lincoln; East China Normal University
  摘要：Bayesian analysis for the covariance matrix of a multivariate normal distribution has received a lot of attention in the last two decades. In this paper, we propose a new class of priors for the covariance matrix, including both inverse Wishart and reference priors as special cases. The main motivation for the new class is to have available priors-both subjective and objective- that do not force eigenvalues apart, which is a criticism of inverse Wishart and Jeffreys priors. Extensive compariso...
• ADAPTIVE ESTIMATION IN STRUCTURED FACTOR MODELS WITH APPLICATIONS TO OVERLAPPING CLUSTERING AI翻译
  作者：Bing, Xin; Bunea, Florentina; Ning, Yang; Wegkamp, Marten
  作者单位：Cornell University; Cornell University
  摘要：This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a latent factor model X = AZ + E, for an observable random vector X is an element of R-p, with correlated unobservable factors Z is an element of R-K, with K unknown, and uncorrelated noise E. Each row of A is scaled, and allowed to be sparse. In order to identify the loading matrix A, we require the existence of pure variables, which are components of X that are associated, via A...
• DISCUSSION OF: NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION AI翻译
  作者：Shamir, Ohad
  作者单位：Weizmann Institute of Science
• PARTIAL IDENTIFIABILITY OF RESTRICTED LATENT CLASS MODELS AI翻译
  作者：Gu, Yuqi; Xu, Gongjun
  作者单位：University of Michigan System; University of Michigan
  摘要：Latent class models have wide applications in social and biological sciences. In many applications, prespecified restrictions are imposed on the parameter space of latent class models, through a design matrix, to reflect practitioners' assumptions about how the observed responses depend on subjects' latent traits. Though widely used in various fields, such restricted latent class models suffer from nonidentifiability due to their discreteness nature and complex structure of restrictions. This ...
• ADAPTIVE DISTRIBUTED METHODS UNDER COMMUNICATION CONSTRAINTS AI翻译
  作者：Szabo, Botond; van Zanten, Harry
  作者单位：Leiden University; Leiden University - Excl LUMC; Vrije Universiteit Amsterdam
  摘要：We study estimation methods under communication constraints in a distributed version of the nonparametric random design regression model. We derive minimax lower bounds and exhibit methods that attain those bounds. Moreover, we show that adaptive estimation is possible in this setting.
• DISCUSSION OF: NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION AI翻译
  作者：Ghorbani, Behrooz; Mei, Song; Misiakiewicz, Theodor; Montanari, Andrea
  作者单位：Stanford University; Stanford University; Stanford University
• REJOINDER: NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION AI翻译
  作者：Schmidt-Hieber, Johannes
  作者单位：University of Twente
• MODEL SELECTION FOR HIGH-DIMENSIONAL LINEAR REGRESSION WITH DEPENDENT OBSERVATIONS AI翻译
  作者：Ing, Ching-Kang
  作者单位：National Tsing Hua University
  摘要：We investigate the prediction capability of the orthogonal greedy algorithm (OGA) in high-dimensional regression models with dependent observations. The rates of convergence of the prediction error of OGA are obtained under a variety of sparsity conditions. To prevent OGA from overfitting, we introduce a high-dimensional Akaike's information criterion (HDAIC) to determine the number of OGA iterations. A key contribution of this work is to show that OGA, used in conjunction with HDAIC, can achi...
• CONVERGENCE RATES OF VARIATIONAL POSTERIOR DISTRIBUTIONS AI翻译
  作者：Zhang, Fengshuo; Gao, Chao
  作者单位：University of Chicago
  摘要：We study convergence rates of variational posterior distributions for non-parametric and high-dimensional inference. We formulate general conditions on prior, likelihood and variational class that characterize the convergence rates. Under similar prior mass and testing conditions considered in the literature, the rate is found to be the sum of two terms. The first term stands for the convergence rate of the true posterior distribution, and the second term is contributed by the variational appr...