2期

最新文章

• Objective priors for the bivariate normal model AI翻译
  作者：Berger, James O.; Sun, Dongchu
  作者单位：Duke University; University of Missouri System; University of Missouri Columbia
  摘要：Study of the bivariate normal distribution raises the full range of issues involving objective Bayesian inference, including the different types of objective priors (e.g., Jeffreys, invariant, reference, matching), the different modes of inference (e.g., Bayesian, frequentist, fiducial) and the criteria involved in deciding on optimal objective priors (e.g., ease of computation, frequentist performance, marginalization paradoxes). Summary recommendations as to optimal objective priors are made...
• Semiparametric efficiency in GMM models with auxiliary data AI翻译
  作者：Chen, Xiaohong; Hong, Han; Tarozzi, Alessandro
  作者单位：Yale University; Stanford University; Duke University
  摘要：We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two data sets. The auxiliary sample can be independent of the primary sample, or can be a subset of it. Fo...
• Closed-form likelihood expansions for multivariate diffusions AI翻译
  作者：Ait-Sahalia, Yacine
  作者单位：Princeton University; National Bureau of Economic Research
  摘要：This paper provides closed-form expansions for the log-likelihood function of multivariate diffusions sampled at discrete time intervals. The coefficients of the expansion are calculated explicitly by exploiting the special structure afforded by the diffusion model. Examples of interest in financial statistics and Monte Carlo evidence are included, along with the convergence of the expansion to the true likelihood function.
• Normalized least-squares estimation in time-varying ARCH models AI翻译
  作者：Fryzlewicz, Piotr; Sapatinas, Theofanis; Rao, Suhasini Subba
  作者单位：University of Bristol; University of Cyprus; Texas A&M University System; Texas A&M University College Station; Ruprecht Karls University Heidelberg
  摘要：We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalized-least-squares (kernel-NLS) estimator which has a closed form, and thu...
• Quadratic distances on probabilities: A unified foundation AI翻译
  作者：Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit; Yang, Ke; Chen, Shu-Chuan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; Boston University; Cytokinetics, Inc.; Arizona State University; Arizona State University-Tempe; National Cheng Kung University
  摘要：This work builds a unified framework for the study of quadratic form distance measures as they are used in assessing the goodness of fit of models. Many important procedures have this structure, but the theory for these methods is dispersed and incomplete. Central to the statistical analysis of these distances is the spectral decomposition of the kernel that generates the distance. We show how this determines the limiting distribution of natural goodness-of-fit tests. Additionally, we develop ...
• Estimation of a semiparametric transformation model AI翻译
  作者：Linton, Oliver; Sperlich, Stefan; Van Keilegom, Ingrid
  作者单位：University of London; London School Economics & Political Science; University of Gottingen; Universite Catholique Louvain
  摘要：This paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or multiplicative separability. We give results for the estimation of the transformation when the rest of the model is estimated non- or semi-parametrically and fulfills some consistency conditions. We propose two methods for the estimation of the transformation parame...
• On deconvolution with repeated measurements AI翻译
  作者：Delaigle, Aurore; Hall, Peter; Meister, Alexander
  作者单位：University of Bristol; University of Melbourne; University of Stuttgart
  摘要：In a large class of statistical inverse problems it is necessary to suppose that the transformation that is inverted is known. Although, in many applications, it is unrealistic to make this assumption, the problem is often insoluble without it. However, if additional data are available, then it is possible to estimate consistently the unknown error density. Data are seldom available directly on the transformation, but repeated, or replicated, measurements increasingly are becoming available. S...
• Ranking and empirical minimization of U-statistics AI翻译
  作者：Clemencon, Stephan; Lugosi, Gabor; Vayatis, Nicolas
  作者单位：IMT - Institut Mines-Telecom; IMT Atlantique; Pompeu Fabra University; Universite Paris Saclay
  摘要：The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one is better, with minimum ranking risk. Since the natural estimates of the risk are of the form of a U-statistic, results of the theory of U-processes are required for investigating the consistency of...
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models AI翻译
  作者：Huang, Jian; Horowitz, Joel L.; Ma, Shuangge
  作者单位：University of Iowa; Northwestern University; Yale University
  摘要：We Study the asymptotic properties of bridge estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase to infinity with the sample size. We are particularly interested in the use of bridge estimators to distinguish between covariates whose coefficients are zero and covariates whose coefficients are nonzero. We show that under appropriate conditions, bridge estimators correctly select covariates with nonzero coefficients with probability converg...
• Statistical performance of support vector machines AI翻译
  作者：Blanchard, Gilles; Bousquet, Olivier; Massart, Pascal
  作者单位：Fraunhofer Gesellschaft; Fraunhofer Germany; Alphabet Inc.; Google Incorporated; Universite Paris Saclay
  摘要：The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regularization procedure. From this point of view, it can also be interpreted as a model selection principle us...