1期

最新文章

• A GENERAL THEORY FOR NONLINEAR SUFFICIENT DIMENSION REDUCTION: FORMULATION AND ESTIMATION AI翻译
  作者：Lee, Kuang-Yao; Li, Bing; Chiaromonte, Francesca
  作者单位：Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：In this paper we introduce a general theory for nonlinear sufficient dimension reduction, and explore its ramifications and scope. This theory subsumes recent work employing reproducing kernel Hilbert spaces, and reveals many parallels between linear and nonlinear sufficient dimension reduction. Using these parallels we analyze the properties of existing methods and develop new ones. We begin by characterizing dimension reduction at the general level of sigma-fields and proceed to that of clas...
• CONVERGENCE RATE OF MARKOV CHAIN METHODS FOR GENOMIC MOTIF DISCOVERY AI翻译
  作者：Woodard, Dawn B.; Rosenthal, Jeffrey S.
  作者单位：Cornell University; Cornell University; University of Toronto
  摘要：We analyze the convergence rate of a simplified version of a popular Gibbs sampling method used for statistical discovery of gene regulatory binding motifs in DNA sequences. This sampler satisfies a very strong form of ergodicity (uniform). However, we show that, due to multimodality of the posterior distribution, the rate of convergence often decreases exponentially as a function of the length of the DNA sequence. Specifically, we show that this occurs whenever there is more than one true rep...
• SPATIALLY-ADAPTIVE SENSING IN NONPARAMETRIC REGRESSION AI翻译
  作者：Bull, Adam D.
  作者单位：University of Cambridge
  摘要：While adaptive sensing has provided improved rates of convergence in sparse regression and classification, results in nonparametric regression have so far been restricted to quite specific classes of functions. In this, paper, we describe an adaptive-sensing algorithm which is applicable to general nonparametric-regression problems. The algorithm is spatially adaptive, and achieves improved rates of convergence over spatially inhomogeneous functions. Over standard function classes, it likewise...
• UNIVERSALLY OPTIMAL CROSSOVER DESIGNS UNDER SUBJECT DROPOUT AI翻译
  作者：Zheng, Wei
  作者单位：Indiana University System; Indiana University Indianapolis
  摘要：Subject dropout is very common in practical applications of crossover designs. However, there is very limited design literature taking this into account. Optimality results have not yet been well established due to the complexity of the problem. This paper establishes feasible, as well as necessary and sufficient conditions for a crossover design to be universally optimal in approximate design theory in the presence of subject dropout. These conditions are essentially linear equations with res...
• AN ALGORITHM TO COMPUTE THE POWER OF MONTE CARLO TESTS WITH GUARANTEED PRECISION AI翻译
  作者：Gandy, Axel; Rubin-Delanchy, Patrick
  作者单位：Imperial College London; University of Bristol
  摘要：This article presents an algorithm that generates a conservative confidence interval of a specified length and coverage probability for the power of a Monte Carlo test (such as a bootstrap or permutation test). It is the first method that achieves this aim for almost any Monte Carlo test. Previous research has focused on obtaining as accurate a result as possible for a fixed computational effort, without providing a guaranteed precision in the above sense. The algorithm we propose does not hav...
• FIDUCIAL THEORY AND OPTIMAL INFERENCE AI翻译
  作者：Taraldsen, Gunnar; Lindqvist, Bo Henry
  作者单位：SINTEF; Norwegian University of Science & Technology (NTNU)
  摘要：It is shown that the fiducial distribution in a group model, or more generally a quasigroup model, determines the optimal equivariant frequentist inference procedures. The proof does not rely on existence of invariant measures, and generalizes results corresponding to the choice of the right Haar measure as a Bayesian prior. Classical and more recent examples show that fiducial arguments can be used to give good candidates for exact or approximate confidence distributions. It is here suggested...
• CONVERGENCE OF LATENT MIXING MEASURES IN FINITE AND INFINITE MIXTURE MODELS AI翻译
  作者：Xuanlong Nguyen
  作者单位：University of Michigan System; University of Michigan
  摘要：This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i.e., Wasserstein metrics). The relationship between Wasserstein distances on the space of mixing measures and f-divergence functionals such as Hellinger and Kullback-Leibler distances on the space of mixture distributions is investigated in detail using various identifiability conditions. Convergence in Wasserstein metrics for discrete measures im...
• EFFICIENT ESTIMATION IN SUFFICIENT DIMENSION REDUCTION AI翻译
  作者：Ma, Yanyuan; Zhu, Liping
  作者单位：Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics; Shanghai University of Finance & Economics
  摘要：We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model. This conversion allows us to derive an efficient estimator which reaches the optimal semiparametric efficiency bound. The resulting efficient estimator can exhaustively estimate the central subspace without imposing a...
• QUANTILE-ADAPTIVE MODEL-FREE VARIABLE SCREENING FOR HIGH-DIMENSIONAL HETEROGENEOUS DATA AI翻译
  作者：He, Xuming; Wang, Lan; Hong, Hyokyoung Grace
  作者单位：University of Michigan System; University of Michigan; University of Minnesota System; University of Minnesota Twin Cities; City University of New York (CUNY) System; Baruch College (CUNY); City University of New York (CUNY) System
  摘要：We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the set of active variables to vary across quantiles, thus making it more flexible to accommodate heterogeneity; (2) it is model-free and avoids the difficult task of specifying the form of a statistical model in a high dimensional space. Our nonlinear independence screening procedure employs spline approximations to mo...
• OPTIMAL DESIGN FOR LINEAR MODELS WITH CORRELATED OBSERVATIONS AI翻译
  作者：Dette, Holger; Pepelyshev, Andrey; Zhigljavsky, Anatoly
  作者单位：Ruhr University Bochum; RWTH Aachen University; Cardiff University
  摘要：In the common linear regression model the problem of determining optimal designs for least squares estimation is considered in the case where the observations are correlated. A necessary condition for the optimality of a given design is provided, which extends the classical equivalence theory for optimal designs in models with uncorrelated errors to the case of dependent data. If the regression functions are eigenfunctions of an integral operator defined by the covariance kernel, it is shown t...