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作者：Zhao, Anqi; Ding, Peng
作者单位：National University of Singapore; University of California System; University of California Berkeley
摘要：Factorial designs are widely used because of their ability to accommodate multiple factors simultaneously. Factor-based regression with main effects and some interactions is the dominant strategy for downstream analysis, delivering point estimators and standard errors simultaneously via one least-squares fit. Justification of these convenient estimators from the design-based perspective requires quantifying their sampling properties under the assignment mechanism while conditioning on the pote...
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作者：Vats, D.; Flegal, J. M.
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; University of California System; University of California Riverside
摘要：Lag windows are commonly used in time series analysis, econometrics, steady-state simulation and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of positive correlation in the underlying process, estimators of this matrix almost always exhibit significant negative bias, leading to undesirable finite-sample properties. We propose a new family of lag windows specifically designed to improve finite-sample performance by offsetting this negative bias. Any exi...
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作者：Chandna, S.; Olhede, S. C.; Wolfe, P. J.
作者单位：University of London; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Purdue University System; Purdue University
摘要：We consider local linear estimation of the graphon function, which determines probabilities of pairwise edges between nodes in an unlabelled network. Real-world networks are typically characterized by node heterogeneity, with different nodes exhibiting different degrees of interaction. Existing approaches to graphon estimation are limited to local constant approximations, and are not designed to estimate heterogeneity across the full network. In this paper, we show how continuous node covariat...
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作者：MacDonald, P. W.; Levina, E.; Zhu, J.
作者单位：University of Michigan System; University of Michigan
摘要：Latent space models are frequently used for modelling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well developed for more complex network structures, which are becoming increasingly common in practice. In this article we propose a new latent space model for multiplex networks, i.e., multiple heterogeneous networks observed on a shared node set. Multiplex networks can represent a network...
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作者：Berrett, T. B.
作者单位：University of Warwick
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作者：Henzi, Alexander; Ziegel, Johanna F.
作者单位：University of Bern
摘要：Probability forecasts for binary events play a central role in many applications. Their quality is commonly assessed with proper scoring rules, which assign forecasts numerical scores such that a correct forecast achieves a minimal expected score. In this paper, we construct e-values for testing the statistical significance of score differences of competing forecasts in sequential settings. E-values have been proposed as an alternative to p-values for hypothesis testing, and they can easily be...
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作者：Schrab, A.; Jitkrittum, W.; Szabo, Z.; Sejdinovic, D.; Gretton, A.
作者单位：University of London; University College London; Alphabet Inc.; Google Incorporated; University of London; London School Economics & Political Science; University of Oxford; University of London; University College London
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作者：Chen, Y.; Li, X.
作者单位：University of London; London School Economics & Political Science; University of Minnesota System; University of Minnesota Twin Cities
摘要：As a generalization of the classical linear factor model, generalized latent factor models are useful for analysing multivariate data of different types, including binary choices and counts. This paper proposes an information criterion to determine the number of factors in generalized latent factor models. The consistency of the proposed information criterion is established under a high-dimensional setting, where both the sample size and the number of manifest variables grow to infinity, and d...
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作者：Zhang, Likun; Shaby, Benjamin A.
作者单位：United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Colorado State University System; Colorado State University Fort Collins
摘要：The three-parameter generalized extreme value distribution arises from classical univariate extreme value theory, and is in common use for analysing the far tail of observed phenomena, yet important asymptotic properties of likelihood-based estimation under this standard model have not been established. In this paper we prove that the maximum likelihood estimator is global and unique. An interesting secondary result entails the uniform consistency of a class of limit relations in a tight neigh...
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作者：ZAPATA, J.; OH, S. Y.; PETERSEN, A.
作者单位：University of California System; University of California Santa Barbara
摘要：The covariance structure of multivariate functional data can be highly complex, especially if the multivariate dimension is large, making extensions of statistical methods for standard multivariate data to the functional data setting challenging. For example, Gaussian graphical models have recently been extended to the setting of multivariate functional data by applying multivariate methods to the coefficients of truncated basis expansions. However, compared with multivariate data, a key diffi...
