-
作者：Ishwaran, H
作者单位：Cleveland Clinic Foundation
-
作者：Jones, GL; Hobert, JP
作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; University of Florida
摘要：We consider Gibbs and block Gibbs samplers for a Bayesian hierarchical version of the one-way random effects model. Drift and minorization conditions are established for the underlying Markov chains. The drift and minorization are used in conjunction with results from J. S. Rosenthal [J. Amer. Statist. Assoc. 90 (1995) 558-566] and G. O. Roberts and R. L. Tweedie [Stochastic Process. Appl. 80 (1999) 211-229] to construct analytical upper bounds on the distance to stationarity. These lead to up...
-
作者：Nan, B; Emond, M; Wellner, JA
作者单位：University of Michigan System; University of Michigan; University of Washington; University of Washington Seattle
摘要：We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in case-cohort designs, a type of two-phase design often used in cohort studies. The derivations make use of key lemmas appearing in Robins, Rotnitzky and Zhao [J. Amer Statist. Assoc. 89 (1994) 846-866] and Robins, Hsieh and Newey [J. Roy. Statist. Soc. Ser. B 57 (1995) 409-424], but in a form suited...
-
作者：Mercurio, D; Spokoiny, V
作者单位：Humboldt University of Berlin; Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
摘要：This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally adaptive volatility, es...
-
作者：Rosset, S; Zhu, J
作者单位：International Business Machines (IBM); IBM USA; University of Michigan System; University of Michigan
-
作者：Turlach, BA
作者单位：University of Western Australia
-
作者：Baraud, Y
作者单位：Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：Starting from the observation of an R(n)-Gaussian vector of mean f and covariance matrix sigma(2)I(n) (I(n) is the identity matrix), we propose a method for building a Euclidean confidence ball around f, with prescribed probability of coverage. For each n, we describe its nonasymptotic property and show its optimality with respect to some criteria.
-
作者：Stine, RA
作者单位：University of Pennsylvania
-
作者：Schick, A; Wefelmeyer, W
作者单位：State University of New York (SUNY) System; Binghamton University, SUNY; University of Cologne
摘要：Suppose we observe an invertible linear process with independent mean-zero innovations and with coefficients depending on a finite-dimensional parameter, and we want to estimate the expectation of some function under the stationary distribution of the process. The usual estimator would be the empirical estimator. It can be improved using the fact that the innovations are centered. We construct an even better estimator using the representation of the observations as infinite-order moving averag...
-
作者：Phillips, PCB; Shimotsu, K
作者单位：Yale University; Queens University - Canada
摘要：Asymptotic properties of the local Whittle estimator in the nonstationary case (d > 1/2) are explored. For 1/2 < d less than or equal to 1, the estimator is shown to be consistent, and its limit distribution and the rate of convergence depend on the value of d. For d = 1, the limit distribution is mixed normal. For d > 1 and when the process has a polynomial trend of order alpha > 1/2, the estimator is shown to be inconsistent and to converge in probability to unity.
