Biometrika

最新文章

• Bayesian sparse multiple regression for simultaneous rank reduction and variable selection AI翻译
  作者：Chakraborty, Antik; Bhattacharya, Anirban; Mallick, Bani K.
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coefficients which obviates the need to specify a prior on the rank, and shrinks the regression matrix towards low-rank and row-sparse structures. We provide theoretical support to the proposed methodology by proving minimax optimality of the posterior me...
• Consistent community detection in multi-layer network data AI翻译
  作者：Lei, Jing; Chen, Kehui; Lynch, Brian
  作者单位：Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：We consider multi-layer network data where the relationships between pairs of elements are reflected in multiple modalities, and may be described by multivariate or even high-dimensional vectors. Under the multi-layer stochastic block model framework we derive consistency results for a least squares estimation of memberships. Our theorems show that, as compared to single-layer community detection, a multi-layer network provides much richer information that allows for consistent community detec...
• Spatial blind source separation AI翻译
  作者：Bachoc, Francois; Genton, Marc G.; Nordhausen, Klaus; Ruiz-Gazen, Anne; Virta, Joni
  作者单位：Universite de Toulouse; Universite Toulouse III - Paul Sabatier; King Abdullah University of Science & Technology; Technische Universitat Wien; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics; University of Turku
  摘要：Recently a blind source separation modelwas suggested for spatial data, along with an estimator based on the simultaneous diagonalization of two scatter matrices. The asymptotic properties of this estimator are derived here, and a new estimator based on the joint diagonalization of more than two scatter matrices is proposed. The asymptotic properties and merits of the novel estimator are verified in simulation studies. A real-data example illustrates application of the method.
• Network cross-validation by edge sampling AI翻译
  作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
  作者单位：University of Virginia; University of Michigan System; University of Michigan
  摘要：While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is a...
• Ensemble estimation and variable selection with semiparametric regression models AI翻译
  作者：Shin, Sunyoung; Liu, Yufeng; Cole, Stephen R.; Fine, Jason P.
  作者单位：University of Texas System; University of Texas Dallas; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function may be ...
• Sparse semiparametric canonical correlation analysis for data of mixed types AI翻译
  作者：Yoon, Grace; Carroll, Raymond J.; Gaynanova, Irina
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this paper is a truncated latent Gaussian copula model for data with excess zeros, which allows us to ...
• Adaptive nonparametric regression with the K-nearest neighbour fused lasso AI翻译
  作者：Padilla, Oscar Hernan Madrid; Sharpnack, James; Chen, Yanzhen; Witten, Daniela M.
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Davis; Hong Kong University of Science & Technology; University of Washington; University of Washington Seattle
  摘要：The fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the K-nearest-neighbours fused lasso, involves computing the K-nearest-neighbours graph of the design points and then performing the fused lasso over this graph. We show that ...
• Classification via local manifold approximation AI翻译
  作者：Li, Didong; Dunson, David B.
  作者单位：Duke University; Duke University
  摘要：Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to achieve accurate classification when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, we propose a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighbourhood of the fea...
• Estimation from cross-sectional data under a semiparametric truncation model AI翻译
  作者：Heuchenne, C.; De Una-Alvarez, J.; Laurent, G.
  作者单位：University of Liege; Universidade de Vigo
  摘要：Cross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper, we consider a semiparametric truncation model in which the truncating variable is assumed to belong to a certain parametric family. We examine two methods of estimating both the truncation and the lifetime distributions. We obtain asymptotic representations of the estimators for the lifetime distribution and establish their weak convergence. Both of t...
• Rejoinder: 'Network cross-validation by edge sampling' AI翻译
  作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
  作者单位：University of Virginia; University of Michigan System; University of Michigan