6期

最新文章

• Identification of multitype branching processes AI翻译
  作者：Maaouia, F; Touati, A
  作者单位：Universite de Tunis-El-Manar; Faculte des Sciences de Tunis (FST)
  摘要：We solve the problem of constructing an asymptotic global confidence region for the means and the covariance matrices of the reproduction distributions involved in a supercritical multitype branching process. Our approach is based on a central limit theorem associated with a quadratic law of large numbers performed by the maximum likelihood or the multidimensional Lotka-Nagaev estimator of the reproduction law means. The extension of this approach to the least squares estimator of the mean mat...
• Parameter estimates for fractional autoregressive spatial processes AI翻译
  作者：Boissy, Y; Bhattacharyya, BB; Li, X; Richardson, GD
  作者单位：State University System of Florida; University of Central Florida; North Carolina State University
  摘要：A binomial-type operator on a stationary Gaussian process is introduced in order to model long memory in the spatial context. Consistent estimators of model parameters are demonstrated. In particular, it is shown that (d) over cap (N) - d = Op((Log N)3)/(N)), where d = (d(1), d(2)) denotes the long memory parameter.
• Nonquadratic estimators of a quadratic functional AI翻译
  作者：Cai, TT; Low, MG
  作者单位：University of Pennsylvania
  摘要：Estimation of a quadratic functional over parameter spaces that are not quadratically convex is considered. It is shown, in contrast to the theory for quadratically convex parameter spaces, that optimal quadratic rules are often rate suboptimal. In such cases minimax rate optimal procedures are constructed based on local thresholding. These nonquadratic procedures are sometimes fully efficient even when optimal quadratic rules have slow rates of convergence. Moreover, it is shown that when est...
• Functional linear regression analysis for longitudinal data AI翻译
  作者：Yao, F; Müller, HG; Wang, JL
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Davis
  摘要：We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudinal studies, the number of repeated measurements per subject is often small and may be modeled as a disc...
• Universal optimality of Patterson's crossover designs AI翻译
  作者：Shah, KR; Bose, M; Raghavarao, D
  作者单位：University of Waterloo; Indian Statistical Institute; Indian Statistical Institute Kolkata; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：We show that the balanced crossover designs given by Patterson [Biometrika 39 (1952) 32-48] are (a) universally optimal (UO) for the joint estimation of direct and residual effects when the competing class is the class of connected binary designs and (b) UO for the estimation of direct (residual) effects when the competing class of designs is the class of connected designs (which includes the connected binary designs) in which no treatment is given to the same subject in consecutive periods. I...
• Optimal designs for three-dimensional shape analysis with spherical harmonic descriptors AI翻译
  作者：Dette, H; Melas, VB; Pepelyshev, A
  作者单位：Ruhr University Bochum; Saint Petersburg State University
  摘要：We determine optimal designs for some regression models which are frequently used for describing three-dimensional shapes. These models are based on a Fourier expansion of a function defined on the unit sphere in terms of spherical harmonic basis functions. In particular, it is demonstrated that the uniform distribution on the sphere is optimal with respect to all Phi(p) criteria proposed by Kiefer in 1974 and also optimal with respect to a criterion which maximizes a p mean of the r smallest ...
• Partially observed information and inference about non-Gaussian mixed linear models AI翻译
  作者：Jiang, JM
  作者单位：University of California System; University of California Davis
  摘要：In mixed linear models with nonnormal data, the Gaussian Fisher information matrix is called a quasi-information matrix (QUIM). The QUIM plays an important role in evaluating the asymptotic covariance matrix of the estimators of the model parameters, including the variance components. Traditionally, there are two ways to estimate the information matrix: the estimated information matrix and the observed one. Because the analytic form of the QUIM involves parameters other than the variance compo...
• On recursive estimation for time varying autoregressive processes AI翻译
  作者：Moulines, E; Priouret, P; Roueff, F
  作者单位：IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
  摘要：This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise estimates is shown to ...