5期

最新文章

• UNIFYING MARKOV PROPERTIES FOR GRAPHICAL MODELS AI翻译
  作者：Lauritzen, Steffen; Sadeghi, Kayvan
  作者单位：University of Copenhagen; University of Cambridge
  摘要：Several types of graphs with different conditional independence interpretations-also known as Markov properties-have been proposed and used in graphical models. In this paper, we unify these Markov properties by introducing a class of graphs with four types of edges-lines, arrows, arcs and dotted lines-and a single separation criterion. We show that independence structures defined by this class specialize to each of the previously defined cases, when suitable subclasses of graphs are considere...
• BACKWARD NESTED DESCRIPTORS ASYMPTOTICS WITH INFERENCE ON STEM CELL DIFFERENTIATION AI翻译
  作者：Huckemann, Stephan F.; Eltzner, Benjamin
  作者单位：University of Gottingen
  摘要：For sequences of random backward nested subspaces as occur, say, in dimension reduction for manifold or stratified space valued data, asymptotic results are derived. In fact, we formulate our results more generally for backward nested families of descriptors (BNFD). Under rather general conditions, asymptotic strong consistency holds. Under additional, still rather general hypotheses, among them existence of a.s. local twice differentiable charts, asymptotic joint normality of a BNFD can be sh...
• ADAPTATION IN LOG-CONCAVE DENSITY ESTIMATION AI翻译
  作者：Kim, Arlene K. H.; Guntuboyina, Adityanand; Samworth, Richard J.
  作者单位：University of Cambridge; Sungshin Women's University; University of California System; University of California Berkeley
  摘要：The log-concave maximum likelihood estimator of a density on the real line based on a sample of size n is known to attain the minimax optimal rate of convergence of O(n(-4/5)) with respect to, for example, squared Hellinger distance. In this paper, we show that it also enjoys attractive adaptation properties, in the sense that it achieves a faster rate of convergence when the logarithm of the true density is k-affine (i.e., made up of k-affine pieces), or close to k-affine, provided in each ca...
• VARIABLE SELECTION WITH HAMMING LOSS AI翻译
  作者：Butucea, Cristina; Ndaoud, Mohamed; Stepanova, Natalia A.; Tsybakov, Alexandre B.
  作者单位：Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Universite Gustave-Eiffel; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Institut Polytechnique de Paris; Ecole Polytechnique; Universite Paris Saclay; ENSAE Paris; Carleton University
  摘要：We derive nonasymptotic bounds for the minimax risk of variable selection under expected Hamming loss in the Gaussian mean model in R-d for classes of at most s-sparse vectors separated from 0 by a constant a > 0. In some cases, we get exact expressions for the nonasymptotic minimax risk as a function of d, s, a and find explicitly the minimax selectors. These results are extended to dependent or non-Gaussian observations and to the problem of crowdsourcing. Analogous conclusions are obtained ...
• SMOOTH BACKFITTING FOR ERRORS-IN-VARIABLES ADDITIVE MODELS AI翻译
  作者：Han, Kyunghee; Park, Byeong U.
  作者单位：Seoul National University (SNU)
  摘要：In this work, we develop a new smooth backfitting method and theory for estimating additive nonparametric regression models when the covariates are contaminated by measurement errors. For this, we devise a new kernel function that suitably deconvolutes the bias due to measurement errors as well as renders a projection interpretation to the resulting estimator in the space of additive functions. The deconvolution property and the projection interpretation are essential for a successful solution...
• ROBUST COVARIANCE AND SCATTER MATRIX ESTIMATION UNDER HUBER'S CONTAMINATION MODEL AI翻译
  作者：Chen, Mengjie; Gao, Chao; Ren, Zhao
  作者单位：University of Chicago; University of Chicago; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：Covariance matrix estimation is one of the most important problems in statistics. To accommodate the complexity of modern datasets, it is desired to have estimation procedures that not only can incorporate the structural assumptions of covariance matrices, but are also robust to outliers from arbitrary sources. In this paper, we define a new concept called matrix depth and then propose a robust covariance matrix estimator by maximizing the empirical depth function. The proposed estimator is sh...
• WEAK CONVERGENCE OF A PSEUDO MAXIMUM LIKELIHOOD ESTIMATOR FOR THE EXTREMAL INDEX AI翻译
  作者：Berghaus, Betina; Buecher, Axel
  作者单位：Ruhr University Bochum
  摘要：The extremes of a stationary time series typically occur in clusters. A primary measure for this phenomenon is the extremal index, representing the reciprocal of the expected cluster size. Both disjoint and sliding blocks estimator for the extremal index are analyzed in detail. In contrast to many competitors, the estimators only depend on the choice of one parameter sequence. We derive an asymptotic expansion, prove asymptotic normality and show consistency of an estimator for the asymptotic ...
• EMPIRICAL BEST PREDICTION UNDER A NESTED ERROR MODEL WITH LOG TRANSFORMATION AI翻译
  作者：Molina, Isabel; Martin, Nirian
  作者单位：Universidad Carlos III de Madrid; Complutense University of Madrid
  摘要：In regression models involving economic variables such as income, log transformation is typically taken to achieve approximate normality and stabilize the variance. However, often the interest is predicting individual values or means of the variable in the original scale. Under a nested error model for the log transformation of the target variable, we show that the usual approach of back transforming the predicted values may introduce a substantial bias. We obtain the optimal (or best) predict...
• ESTIMATION OF A MONOTONE DENSITY IN s-SAMPLE BIASED SAMPLING MODELS AI翻译
  作者：Chan, Kwun Chuen Gary; Ling, Hok Kan; Sit, Tony; Yam, Sheung Chi Phillip
  作者单位：University of Washington; University of Washington Seattle; Columbia University; Chinese University of Hong Kong
  摘要：We study the nonparametric estimation of a decreasing density function go in a general s-sample biased sampling model with weight (or bias) functions w(i )for i = 1, ...,s. The determination of the monotone maximum likelihood estimator (g) over cap (n) and its asymptotic distribution, except for the case when s = 1, has been long missing in the literature due to certain nonstandard structures of the likelihood function, such as nonseparability and a lack of strictly positive second order deriv...
• COMMUNITY DETECTION IN DEGREE-CORRECTED BLOCK MODELS AI翻译
  作者：Gao, Chao; Ma, Zongming; Zhang, Anderson Y.; Zhou, Harrison H.
  作者单位：University of Chicago; University of Pennsylvania; Yale University
  摘要：Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCBMs). We first derive asymptotic minimax risks of the problem for a misclassification proportion loss under appropriate conditions. The minimax risks are shown to depend on degree...