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Rates of convergence in semi-parametric modelling of longitudinal data.

Moyeed, R. and Diggle, Peter J. (1994) Rates of convergence in semi-parametric modelling of longitudinal data. Australian Journal of Statistics, 36 (1). pp. 75-93.

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We consider the problem of semi-parametric regression modelling when the data consist of a collection of short time series for which measurements within series are correlated. The objective is to estimate a regression function of the form E[Y(t) | x] =x'ß+μ(t), where μ(.) is an arbitrary, smooth function of time t, and x is a vector of explanatory variables which may or may not vary with t. For the non-parametric part of the estimation we use a kernel estimator with fixed bandwidth h. When h is chosen without reference to the data we give exact expressions for the bias and variance of the estimators for β and μ(t) and an asymptotic analysis of the case in which the number of series tends to infinity whilst the number of measurements per series is held fixed. We also report the results of a small-scale simulation study to indicate the extent to which the theoretical results continue to hold when h is chosen by a data-based cross-validation method.

Item Type: Journal Article
Journal or Publication Title: Australian Journal of Statistics
Uncontrolled Keywords: Autocorrelation • cross-validation • kernel regression • longitudinal data • semi-parametric regression • smoothing • time series
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
Faculty of Health and Medicine > Medicine
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ID Code: 19611
Deposited By: ep_ss_importer
Deposited On: 12 Nov 2008 15:19
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2018 03:42
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