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最新文章

• Fitting a bivariate additive model by local polynomial regression AI翻译
  作者：Opsomer, JD; Ruppert, D
  作者单位：Iowa State University; Cornell University
  摘要：While the additive model is a popular nonparametric regression method, many of its theoretical properties are not well understood, especially when the backfitting algorithm is used for computation of the estimators. This article explores those properties when the additive model is fitted by local polynomial regression. Sufficient conditions guaranteeing the asymptotic existence of unique estimators for the bivariate additive model are given. Asymptotic approximations to the bias and the varian...
• Bayesian methods and maximum entropy for ill-posed inverse problems AI翻译
  作者：Gamboa, F; Gassiat, E
  作者单位：Universite Paris Saclay; Universite Paris Saclay; Universite Paris 13
  摘要：In this paper, we study linear inverse problems where some generalized moments of an unknown positive measure are observed, We introduce a new construction, called the maximum entropy on the mean method (MEM), which relies on a suitable sequence of finite-dimensional discretized inverse problems. Its advantage is threefold: It allows us to interpret all usual deterministic methods as Bayesian methods; it gives a very convenient way of taking into account prior information; it also leads to new...
• Time series regression with long-range dependence AI翻译
  作者：Robinson, PM; Hidalgo, FJ
  作者单位：University of London; London School Economics & Political Science
  摘要：A central limit theorem is established for time series regression estimates which include generalized least squares, in the presence of long-range dependence in both errors and stochastic regressors. The setting and results differ significantly from earlier work on regression with long-range-dependent errors. Spectral singularities are permitted at any frequency. When sufficiently strong spectral singularities in the error and a regressor coincide at the same frequency, least squares need no l...
• Semiparametric single index versus fixed line function modelling AI翻译
  作者：Härdle, W; Spokoiny, V; Sperlich, S
  作者单位：Humboldt University of Berlin; Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：Discrete choice models are frequently used in statistical and econometric practice. Standard models such as legit models are based on exact knowledge of the form of the link and linear index function. Semiparametric models avoid possible misspecification but often introduce a computational burden especially when optimization over nonparametric and parametric components are to be done iteratively. It is therefore interesting to decide between approaches. Here we propose a test of semiparametric...
• Markov chains for Monte Carlo tests of genetic equilibrium in multidimensional contingency tables AI翻译
  作者：Lazzeroni, LC; Lange, K
  作者单位：Stanford University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Hardy-Weinberg equilibrium and linkage equilibrium are fundamental concepts in population genetics. In practice, testing linkage equilibrium in haplotype data is equivalent to testing independence in a large, sparse, multidimensional contingency table. Testing Hardy-Wieinberg and linkage equilibrium simultaneously on multilocus genotype data introduces the additional complications of missing information and symmetry constraints on marginal probabilities. To avoid unreliable large-sample approx...
• Consistent estimation of joint distributions for sufficiently mixing random fields AI翻译
  作者：Steif, JE
  作者单位：Chalmers University of Technology
  摘要：The joint distribution of a d-dimensional random field restricted to a box of size k can be estimated by looking at a realization in a box of size n >> k and computing the empirical distribution. This is done by sliding a box of size a around in the box of size n and computing frequencies. We show that when k = ii(n) grows as a function of n, then the total variation distance between this empirical distribution and the true distribution goes to 0 a.s. as n --> infinity provided k(n)(d) less th...
• Semiparametric inference in a partial linear model AI翻译
  作者：Bhattacharya, PK; Zhao, PL
  作者单位：University of California System; University of California Davis; Merck & Company
  摘要：In a partial linear model, the dependence of a response variate Y on covariates (W, X) is given by Y = W beta + eta(X)+ E, where E is independent of (W, X) with densities g and f, respectively. In this paper an asymptotically efficient estimator of beta is constructed solely under mild smoothness assumptions on the unknown eta, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.