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The result is a shape model consisting of the mean model and the major modes of variation with a dense correspondence map between individual shapes.

Stationary R-squared is a measure that compares stationary part of the model to a simple mean model and is preferable to ordinary R-squared when there is a trend or seasonal pattern.

We mathematically show that the best representative of all the local models is a certain "mean" model, and empirically show that this model can be approximated quite well by generating artificial samples from the local models using sampling techniques, and then fitting a global model of a chosen parametric form to these samples.

Because the serum PFOA concentrations appear to be lognormally distributed, the log transformation is appropriate for both the mean model and for the residuals.

By using the mean model and default filtering parameter (p value threshold < 0.67), the searching process identified five groups of genes which had very similar periodic expression patterns).

Selection of the mean model and variance-covariance structure was done based on quasi likelihood under the independence model criterion (QIC).

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Outliers significantly affect the conditional mean models and as a result, it affects the measurement of the central location, which may be misleading.

The problem of classification of Gaussian random field observations into one of two populations specified by different regression mean models and common stationary covariance function is considered.

The proposed approach utilizes both the histogram equalization technique for matching the acoustic mean models and an SNR-dependent linear interpolation-based method for adapting the covariance models into test environments.

Survival data models, considering cost as the 'time' variable, can relax the parametric assumptions of the exponential conditional mean models, and might be advantageous particularly for data with skewness, heavy tails and multimodality and are thus reviewed here.

Following convention (Singer and Willett 2003), we first report results from fitting an unconditional means model and an unconditional growth model to the data.

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