1998

最新文章

• A class of local likelihood methods and near-parametric asymptotics AI翻译
  作者：Eguchi, S; Copas, J
  作者单位：University of Warwick; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：The local maximum likelihood estimate <(theta)over cap>(t) of a parameter in a statistical model f(x, theta) is defined by maximizing a weighted version of the likelihood function which gives more weight to observations in the neighbourhood of t. The paper studies the sense in which f(t, <(theta)over cap>(t)) is closer to the true distribution g(t) than the usual estimate f(t, <(theta)over cap>) is. Asymptotic results are presented for the case in which the model misspecification becomes vanis...
• Nonparametric estimation of the mode of a distribution of random curves AI翻译
  作者：Gasser, T; Hall, P; Presnell, B
  作者单位：Australian National University; University of Zurich
  摘要：Motivated by the need to develop meaningful empirical approximations to a 'typical' data value, we introduce methods for density and mode estimation when data are in the form of random curves. Our approach is based on finite dimensional approximations via generalized Fourier expansions on an empirically chosen basis. The mode estimation problem is reduced to a problem of kernel-type multivariate estimation from vector data and is solved using a new recursive algorithm for finding the empirical...
• Additive nonparametric regression with autocorrelated errors AI翻译
  作者：Smith, M; Wong, CM; Kohn, R
  作者单位：University of New South Wales Sydney; Monash University; Hong Kong University of Science & Technology
  摘要：A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially non-linear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterized in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of...
• Bias-corrected confidence bands in nonparametric regression AI翻译
  作者：Xia, YC
  作者单位：University of Hong Kong
  摘要：Bias-corrected confidence bands for general nonparametric regression models are considered. We use local polynomial fitting to construct the confidence bands and combine the cross-validation method and the plug-in method to select the bandwidths. Related asymptotic results are obtained. Our simulations show that confidence bands constructed by local polynomial fitting have much better coverage than those constructed by using the Nadaraya-Watson estimator. The results are also applicable to non...
• Estimating smooth monotone functions AI翻译
  作者：Ramsay, JO
  作者单位：McGill University
  摘要：Many situations call for a smooth strictly monotone function f of arbitrary flexibility. The family of functions defined by the differential equation D(2)f = w Df, where w is an unconstrained coefficient function, comprises the strictly monotone twice differentiable functions. The solution to this equation is f = C-0 + C-1 D-1{exp(D(-1)w)}, where C-0 and C-1 are arbitrary constants and D-1 is the partial integration operator. A basis for expanding w is suggested that permits explicit integrati...
• Calibrating the excess mass and dip tests of modality AI翻译
  作者：Cheng, MY; Hall, P
  作者单位：Australian National University
  摘要：Nonparametric tests of modality are a distribution-free way of assessing evidence about inhomogeneity in a population, provided that the potential subpopulations are sufficiently well separated. They include the excess mass and dip tests, which are equivalent in univariate settings and are alternatives to the bandwidth test. Only very conservative forms of the excess mass and dip tests are available at present, however, and for that reason they are generally not competitive with the bandwidth ...
• Second-derivative functional regression with applications to near infra-red spectroscopy AI翻译
  作者：Goutis, C
  作者单位：Universidad Carlos III de Madrid
  摘要：A linear regression method to predict a scalar from a discretized smooth function is presented. The method takes into account the functional nature of the predictors and the importance of the second derivative in spectroscopic applications. This motivates a functional inner product that can be used as a roughness penalty. Using this inner product, we derive a linear prediction method that is similar to ridge regression but with different shrinkage characteristics. We describe its practical imp...
• Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion AI翻译
  作者：Hurvich, CM; Simonoff, JS; Tsai, CL
  作者单位：New York University; University of California System; University of California Davis
  摘要：Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AIC(C), is derived and examined as a way to choose the smoothing parameter. Unlik...
• On the statistical inference of a machine-generated autoregressive AR(1) model AI翻译
  作者：Stockis, JP; Tong, H
  作者单位：University of Kent
  摘要：We have obtained the asymptotic bias and the limiting distribution for the Yule-Walker estimator of the autoregressive parameter under a considerably weaker assumption than that of independence in the noise sequence. Among other things, these suggest robustness of the classical results and throw some light on the use of simulations based on pseudorandom numbers in verifying these results.