The Annals of Statistics

最新文章

• Variable length Markov chains AI翻译
  作者：Bühlmann, P; Wyner, AJ
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Pennsylvania
  摘要：We study estimation in the class of stationary variable length Markov chains (VLMC) on a finite space. The processes in this class are still Markovian of high order, but with memory of variable length yielding a much bigger and structurally richer class of models than ordinary high-order Markov chains. From an algorithmic view, the VLMC model class has attracted interest in information theory and machine learning, but statistical properties have not yet been explored. Provided that good estima...
• Discontinuous versus smooth regression AI翻译
  作者：Müller, HG; Stadtmüller, U
  作者单位：University of California System; University of California Davis; Ulm University
  摘要：Given measurements (x(i), y(i)), i = 1,..., n, we discuss methods to assess whether an underlying regression function is smooth (continuous or differentiable) or whether it has discontinuities. The variance of the measurements is assumed to be unknown, and is estimated simultaneously. By regressing squared differences of the data formed with various span sizes on the span size itself, we obtain an asymptotic linear model with dependent errors. The parameters of this asymptotic linear model inc...
• Local asymptotic normality for regression models with long-memory disturbance AI翻译
  作者：Hallin, M; Taniguchi, M; Serroukh, A; Choy, K
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; University of Osaka; Imperial College London; Komazawa University
  摘要：The local asymptotic normality property is established fur a regression model with fractional ARIMA(p, d, q) errors. This result allows for solving, in an asymptotically optimal way, a variety of inference problems In the long-memory context: hypothesis testing, discriminant analysis, rank-based testing, locally asymptotically minimax and adaptive estimation, etc. The problem of testing linear constraints on the parameters, the discriminant analysis problem, and the construction of locally asy...
• Multivariate analysis by data depth - Discussion AI翻译
  作者：Baggerly, KA; Scott, DW
  作者单位：Rice University
• Multivariate analysis by data depth: Descriptive statistics, graphics and inference AI翻译
  作者：Liu, RY; Parelius, JM; Singh, K
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：A data depth can be used to measure the depth or outlyingness of a given multivariate sample with respect to its underlying distribution. This leads to a natural center-outward ordering of the sample points. Based on this ordering, quantitative and graphical methods are introduced for analyzing multivariate distributional characteristics such as location, scale, bias, skewness and kurtosis, as well as for comparing inference methods. All graphs are one-dimensional curves in the plane and can b...
• Estimating a tail exponent by modelling departure from a Pareto distribution AI翻译
  作者：Feuerverger, A; Hall, P
  作者单位：University of Toronto; Australian National University
  摘要：We suggest two semiparametric methods for accommodating departures from a Pareto model when estimating a tail exponent by fitting the model to extreme-value data. The methods are based on approximate likelihood and on least squares, respectively. The latter is somewhat simpler to use and more robust against departures from classical extreme-value approximations, but produces estimators with approximately 64% greater variance when conventional extreme-value approximations are appropriate. Relat...
• Robust improvement in estimation of a covariance matrix in an elliptically contoured distribution AI翻译
  作者：Kubokawa, T; Srivastava, MS
  作者单位：University of Tokyo; University of Toronto
  摘要：This paper derives an extended version of the Haff or, more appropriately, Stein-Haff identity for an elliptically contoured distribution (ECD). This identity is then used to show that the minimax estimators of the covariance matrix obtained under normal models remain robust under the ECD model.
• Dimension reduction for censored regression data AI翻译
  作者：Li, KC; Wang, JL; Chen, CH
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Davis; Academia Sinica - Taiwan
  摘要：Without parametric assumptions, high-dimensional regression analysis is already complex. This is made even harder when data are subject to censoring. In this article, we seek ways of reducing the dimensionality of the regressor before applying nonparametric smoothing techniques. If the censoring time is independent of the lifetime, then the method of sliced inverse regression can be applied directly. Otherwise, modification is needed to adjust for the censoring bias. A key identity leading to ...
• Large sample Bayesian analysis for geo/G/1 discrete-time queueing models AI翻译
  作者：Conti, PL
  作者单位：University of Bologna; Duke University
  摘要：In this paper, a nonparametric Bayesian analysis of queueing models with geometric input and general service time is performed. In particular, statistical inference for the probability generating function (p.g.f.) of the equilibrium waiting time distribution is considered. The consistency of the posterior distribution for such a p.g.f., as well as the weak convergence to a Gaussian process of a suitable rescaling, are proved. As by-products, results on statistical inference for queueing charac...
• Statistical estimation in varying coefficient models AI翻译
  作者：Fan, JQ; Zhang, WY
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Chinese University of Hong Kong
  摘要：Varying coefficient models are a useful extension of classical linear models. They arise naturally when one wishes to examine how regression coefficients change over different groups characterized by certain covariates such as age. The appeal of these models is that the coefficient functions can easily be estimated via a simple local regression. This yields a simple one-step estimation procedure. We show that such a one-step method cannot be optimal when different coefficient functions admit d...