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Correlated Errors (correlated + error)

Distribution by Scientific Domains
Mathematics and Statistics80%

Selected Abstracts

The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 582 2002
Z.-Q. Liu
Abstract In this paper, the optimal configurations of model resolution, observation resolution and observation density are investigated in a simple one-dimensional framework. In this context, the representativeness error is formalized and estimated before being used in the analysis-error formulation. Some optimal and suboptimal assimilation-schemes, differing from different approximations of observation-error covariance and observation operator, are compared. The optimal observation-extent is determined as a function of model resolution. Increasing the observation density is usually beneficial, except for suboptimal schemes similar to the ones used in operational practice. The impact of thinning the observations with correlated error is also studied from a suboptimal viewpoint. Copyright © 2002 Royal Meteorological Society [source]

Smoothing splines for trend estimation and prediction in time series

ENVIRONMETRICS, Issue 3 2009
Richard Morton
Abstract We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, and avoids the tendency to under-smooth or to interpolate the data that can occur with other data-driven methods used to choose the smoothing parameter. The method is applied to data from a chemical process and to stream salinity measurements. Copyright © 2008 John Wiley & Sons, Ltd. [source]

On inference for a semiparametric partially linear regression model with serially correlated errors

THE CANADIAN JOURNAL OF STATISTICS, Issue 4 2007
Jinhong You
Abstract The authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference procedure consisting of a bandwidth selection method, an efficient semiparametric generalized least squares estimator of the parametric component, a goodness-of-fit test based on the bootstrap, and a technique for selecting significant covariates in the parametric component. They assess their approach through simulation studies and illustrate it with a concrete application. L'inférence dans le cadre d'un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série Les auteurs s'intéressent à un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série. Ils proposent une façon originale d'estimer la structure d'erreur qui a l'avantage de ne faire intervenir aucune estimation non paramétrique. Ceci leur permet de développer une procédure d'inférence comportant un choix de fen,tre, l'emploi de la méthode des moindres carrés généralisés pour l'estimation semiparamétrique efficace de la composante paramétrique, un test d'adéquation fondé sur le rééchantillonnage et une technique de sélection des covariables significatives de la composante paramétrique. Ils évaluent leur approche par voie de simulation et en donnent une illustration concrète. [source]

Varying Coefficient Model with Unknown Within-Subject Covariance for Analysis of Tumor Growth Curves

BIOMETRICS, Issue 4 2008
Robert T. Krafty
Summary In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback,Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors. [source]