2期

最新文章

• Nonparametric Bayesian estimators for counting processes AI翻译
  作者：Kim, YD
  作者单位：Hankuk University Foreign Studies
  摘要：This paper is concerned with nonparametric Bayesian inference of the Aalen's multiplicative counting process model. For a desired nonparametric prior distribution of the cumulative intensity function, a dass of Levy processes is considered, and it is shown that the class of Levy processes is conjugate for the multiplicative counting process model, and formulas for obtaining a posterior process are derived. Finally, our results are applied to several practically important models such as one poi...
• A characterization of a neutral to the right prior via an extension of Johnson's sufficientness postulate AI翻译
  作者：Walker, S; Muliere, P
  作者单位：Imperial College London; University of Pavia
  摘要：In this paper we present a new characterization and perspective on a neutral to the right prior. This characterization is based on a sequence of predictive laws which provides explicitly the posterior parameters and Bayes estimators for such a prior.
• Beneath the noise, chaos AI翻译
  作者：Lalley, SP
  作者单位：Purdue University System; Purdue University
  摘要：The problem of extracting a signal x(n) from a noise-corrupted time series y(n) = x(n) + e(n) is considered. The signal x(n) is assumed to be generated by a discrete-time, deterministic, chaotic dynamical system F, in particular, x(n) = F-n(x(0)), where the initial point x(0) is assumed to lie in a compact hyperbolic F-invariant set. It is shown that (1) if the noise sequence e(n) is Gaussian then it is impossible to consistently recover the signal x(n), but (2) if the noise sequence consists ...
• Estimating the probability of a rare event AI翻译
  作者：de Haan, L; Sinha, AK
  作者单位：Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
  摘要：Let (X-1, Y-1), (X-2, Y-2),..., (X-n, Y-n) be a random sample from a bivariate distribution function F which is in the domain of attraction of a bivariate extreme value distribution function G. A subset C of R-2 is given, which contains none of the observations. We shall give an asymptotic confidence interval for Pr((X-i, Y-i) is an element of C) under certain conditions.
• Asymptotically optimal estimation of smooth functionals for interval censoring, case 2 AI翻译
  作者：Geskus, R; Groeneboom, P
  作者单位：Delft University of Technology
  摘要：For a version of the interval censoring model, case 2, in which the observation intervals are allowed to be arbitrarily small, we consider estimation of functionals that are differentiable along Hellinger differentiable paths. The asymptotic information lower bound for such functionals can be represented as the squared L-2-norm of the canonical gradient in the observation space. This canonical gradient has an implicit expression as a solution of an integral equation that does not belong to one...
• Synchronizing sample curves nonparametrically AI翻译
  作者：Wang, KM; Gasser, T
  作者单位：State University of New York (SUNY) System; SUNY Downstate Health Sciences University; University of Zurich
  摘要：More and more often, the outcome of a study is not a random variable but a noisy function for each experimental unit, resulting in a sample of curves. Typically, the individual curves vary not only in amplitude or intensity, but also with respect to the time axis: different subjects experience certain events sooner or later. Analyzing such data involves finding out the time changes (or curve registration) among curves. Following our previous work where modified dynamic time warping is applied ...
• 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...
• 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.
• Brown's paradox in the estimated confidence approach AI翻译
  作者：Wang, HY
  作者单位：Academia Sinica - Taiwan
  摘要：A widely held notion of classical conditional theory is that statistical inference in the presence of ancillary statistics should he independent of the distribution of those ancillary statistcs. In this paper, ancillary paradoxes which contradict this notion are presented for two scenarios involving confidence estimation. These results are related to Brown's ancillary paradox in point estimation. Moreover, the confidence coefficient, the usual constant coverage probability estimator, is shown ...