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\footnotesize \noindent Statistica Sinica {\bf 21} (2011), 1785-1806

\footnotesize \noindent doi:http://dx.doi.org/10.5705/ss.2009.292

\vspace{3pc} \normalsize \centerline{\large\bf QUASI-DEVIANCE
FUNCTIONS FOR SPATIALLY} \vspace{2pt} \centerline{\large\bf
CORRELATED DATA} \vspace{.25cm}
\author{Pei-Sheng Lin}
\vspace{.4cm} \centerline{\it National Health Research Institutes
and National Chung Cheng University} \vspace{.55cm} 
\begin{quotation}
\noindent {\it Abstract:} This paper introduces a quasi-deviance
function for model selection of given spatial data.  The proposed
deviance function involves only the mean and covariance of
responses, and therefore avoids the difficulty of specifying a
full-likelihood function. We show that, under certain regularity
conditions, the deviance function with quasi-likelihood estimating
equation has a limiting chi-squared distribution. The asymptotic
quadratic form of the deviance function provides a consistent method
for selecting the true model.  We also conduct simulations to
evaluate the performance of the proposed method, and use the East
Lansing Woods data to illustrate the application.\par \vspace{9pt}
\noindent {\it Key words and phrases:} Deviance function, model
selection, quasi-likelihood estimate, spatial data.\par
\end{quotation}

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