-
作者：JONES, MC; LINTON, O; NIELSEN, JP
作者单位：Yale University
摘要：A new method for bias reduction in nonparametric density estimation is proposed. The method is a simple, two-stage multiplicative bias correction. Its theoretical properties are investigated, and simulations indicate its practical potential. The method is easy to compute and to analyse, and extends simply to multivariate and other estimation problems.
-
作者：GEOFFARD, PY; PHILIPSON, T
作者单位：University of Chicago
摘要：This paper discusses the falsifiable implications of the heterogeneous mixing model of infectious disease. It is shown that the model implies a proportional hazard restriction across the set of susceptible survival functions of the subpopulations. Using this implication, the testable restrictions and identification of the model are discussed when subpopulations are observable as well as unobservable.
-
作者：HADDAD, JN
摘要：The covariance matrix of a first order moving average process is expressed as the product of the covariance matrix of the dual autoregressive process of order one and a near identity matrix. Its inverse is then obtained. The closed form of the likelihood function is derived. A comparison is made with some approximate likelihood functions.
-
作者：WALDEN, AT; MCCOY, EJ; PERCIVAL, DB
作者单位：University of Washington; University of Washington Seattle
摘要：The bandwidth of a spectral estimator is a measure of the minimum separation in frequency between approximately uncorrelated spectral estimates. We determine an effective bandwidth measure for multitaper spectral estimators, a relatively new and very powerful class of spectral estimators proving to be very valuable whenever the spectrum of interest is detailed and/or varies rapidly with a large dynamic range. The multitaper spectral estimator is the average of several direct spectral estimator...
-
作者：GLAD, IK; SEBASTIANI, G
摘要：Synthetic magnetic resonance imaging involves the estimation, based on a set of measured images with noise, of three basic physical quantities that are nonlinearly related to the observations. The methods currently available for this ill-conditoned inverse problem either do not provide sufficiently accurate estimates or require time-consuming data collection. We formulate this nonlinear problem in a Bayesian framework, taking into account knowledge about the physics of the magnetic resonance i...
-
作者：LINTON, O; NIELSEN, JP
摘要：We define a simple kernel procedure based on marginal integration that estimates the relevant univariate quantity in both additive and multiplicative nonparametric regression.
-
作者：ELSTER, C; NEUMAIER, A
作者单位：University of Vienna
摘要：Screening experiments aim to identify the relevant variables within some process potentially depending on a large number of variables. In this paper we introduce a new class of experimental designs called edge designs. These designs allow a model-independent estimate of the set of relevant variables, thus providing more robustness than traditional designs. We give a bound on the determinant of the information matrix of certain edge designs, and show that a large class of edge designs meeting t...
-
作者：HALL, P; HOROWITZ, JL; JING, BY
作者单位：University of Iowa; Hong Kong University of Science & Technology
摘要：We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal to n(1/3), n(1/4) and n(1/5) in the cases of variance or bias estimation, estimation of a one-sided distribution function, and estimation of a two-sided distribution function, respectively. A clear intuitive explanation of this phenomenon is given, together with outlines of theoretical arguments in specific case...
-
作者：HJELLVIK, V; TJOSTHEIM, D
摘要：We introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance. The tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum. Asymptotic expressions give bad approximations, and the null distribution under linearity is constructed using resampling of the best linear approximation. The new tests perform well on the examples tested.
