6A期

最新文章

• CONSTRUCTION OF NESTED SPACE-FILLING DESIGNS AI翻译
  作者：Qian, Peter Z. G.; Ai, Mingyao; Wu, C. F. Jeff
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Peking University; University System of Georgia; Georgia Institute of Technology
  摘要：New types of designs called nested space-filling designs have been proposed for conducting multiple computer experiments with different levels of accuracy. In this article, we develop several approaches to constructing Such designs. The development of these methods also leads to the introduction of several new discrete mathematics concepts, including nested orthogonal arrays and nested difference matrices.
• ESTIMATION OF FUNCTIONAL DERIVATIVES AI翻译
  作者：Hall, Peter; Mueller, Hans-Georg; Yao, Fang
  作者单位：University of Melbourne; University of California System; University of California Davis; University of Toronto
  摘要：Situations of a functional predictor paired with a scalar response are increasingly encountered in data analysis. Predictors are often appropriately modeled as square integrable smooth random functions. Imposing minimal assumptions on the nature of the functional relationship, we aim to estimate the directional derivatives and gradients of the response with respect to the predictor functions. In statistical applications and data analysis, functional derivatives provide a quantitative measure o...
• BUILDING AND USING SEMIPARAMETRIC TOLERANCE REGIONS FOR PARAMETRIC MULTINOMIAL MODELS AI翻译
  作者：Liu, Jiawei; Lindsay, Bruce G.
  作者单位：University System of Georgia; Georgia State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：We introduce a semiparametric tubular neighborhood of a parametric model in the multinomial setting. It consists of all multinomial distributions lying in a distance-based neighborhood of the parametric model of interest. Fitting such a tubular model allows one to use a parametric model while treating it as an approximation to the true distribution. In this paper, the Kullback-Leibler distance is used to build the tubular region. Based on this idea one can define the distance between the true ...
• ESTIMATING THE GUMBEL SCALE PARAMETER FOR LOCAL ALIGNMENT OF RANDOM SEQUENCES BY IMPORTANCE SAMPLING WITH STOPPING TIMES AI翻译
  作者：Park, Yonil; Sheetlin, Sergey; Spouge, John L.
  作者单位：National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM)
  摘要：The gapped local alignment score of two random sequences follows a Gumbel distribution. If computers could estimate the parameters of the Gumbel distribution within one second, the use of arbitrary alignment scoring schemes could increase the sensitivity of searching biological sequence databases over the web. Accordingly, this article gives a novel equation for the scale parameter of the relevant Gumbel distribution. We speculate that the equation is exact, although present numerical evidence...
• ASYMPTOTIC EQUIVALENCE AND ADAPTIVE ESTIMATION FOR ROBUST NONPARAMETRIC REGRESSION AI翻译
  作者：Cai, T. Tony; Zhou, Harrison H.
  作者单位：University of Pennsylvania; Yale University
  摘要：Asymptotic equivalence theory developed in the literature so far are only for bounded loss functions. This limits the potential applications of the theory because many commonly used loss functions in statistical inference are unbounded. In this paper we develop asymptotic equivalence results for robust nonparametric regression with unbounded loss functions. The results imply that all the Gaussian nonparametric regression procedures can be robustified in a unified way. A key step in our equival...
• ON THE PATH DENSITY OF A GRADIENT FIELD AI翻译
  作者：Genovese, Christopher R.; Perone-Pacifico, Marco; Verdinelli, Isabella; Wasserman, Larry
  作者单位：Carnegie Mellon University; Sapienza University Rome
  摘要：We consider the problem of reliably finding filaments in point clouds. Realistic data sets often have numerous filaments of various sizes and shapes. Statistical techniques exist for finding one (or a few) filaments but these methods do not handle noisy data sets with many filaments. Other methods can be found in the astronomy literature but they do not have rigorous statistical guarantees. We propose the following method. Starting at each data point we construct the steepest ascent path along...
• A CONJUGATE PRIOR FOR DISCRETE HIERARCHICAL LOG-LINEAR MODELS AI翻译
  作者：Massam, Helene; Liu, Jinnan; Dobra, Adrian
  作者单位：York University - Canada; University of Washington; University of Washington Seattle
  摘要：In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this paper, we define a flexible family of conjugate priors for the wide class of discrete hierarchical log-linear models, which includes the class of graphical models. These priors are defined as the Diaconis-Ylvisaker conjugate priors on the log-linear parameters subject to baseline constraints under mul...
• ON NONPARAMETRIC AND SEMIPARAMETRIC TESTING FOR MULTIVARIATE LINEAR TIME SERIES AI翻译
  作者：Yajima, Yoshihiro; Matsuda, Yasumasa
  作者单位：University of Tokyo; Tohoku University
  摘要：We formulate nonparametric and semiparametric hypothesis testing of multivariate stationary linear time series in a unified fashion and propose new test statistics based on estimators of the spectral density matrix. The limiting distributions of these test statistics under null hypotheses are always normal distributions, and they can be implemented easily for practical use. If null hypotheses are false, as the sample size goes to infinity, they diverge to infinity and consequently are consiste...
• ROBUST NEAREST-NEIGHBOR METHODS FOR CLASSIFYING HIGH-DIMENSIONAL DATA AI翻译
  作者：Chan, Yao-Ban; Hall, Peter
  作者单位：University of Melbourne
  摘要：We suggest a robust nearest-neighbor approach to classifying high-dimensional data. The method enhances sensitivity by employing a threshold and truncates to a sequence of zeros and ones in order to reduce the deleterious impact of heavy-tailed data. Empirical rules are suggested for choosing the threshold. They require the bare minimum of data only one data vector is needed from each population. Theoretical and numerical aspects of performance are explored, paying particular attention to the ...
• A UNIFIED APPROACH TO MODEL SELECTION AND SPARSE RECOVERY USING REGULARIZED LEAST SQUARES AI翻译
  作者：Lv, Jinchi; Fan, Yingying
  作者单位：University of Southern California
  摘要：Model selection and sparse recovery are two important problems for which many regularization methods have been proposed. We study the properties of regularization methods in both problems under the unified framework of regularized least squares with concave penalties. For model selection, we establish conditions under which a regularized least squares estimator enjoys a nonasymptotic property, called the weak oracle property, where the dimensionality can grow exponentially with sample size. Fo...