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Frequently, ROC curves cannot be fitted using standard methods due to "degenerate" data distributions.
Model (2) can be fitted using standard Maximum Likelihood Estimation (MLE) ignoring the clustering.
Because the model is dynamic and involves the non-linear cosine function it cannot be fitted using standard methods, such as maximum likelihood.
The latter condition is not an intrinsic assumption, but the model cannot be fitted using standard software unless it is met.
Firstly, the model can be fitted using standard statistical software such as using the GENMOD procedure in the SAS statistical package [ 12].
Since the linear regression modelling used by this new method may be fitted using standard software libraries for ordinary least squares regression, it is very easy to include in any software library for analysis of ChIP-seq data.
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After specifying a model for { R k, t }, we have a regression model with time series errors, that can be fit using standard maximum likelihood methods.
These models will be fit using standard structural equation modeling software packages (e.g., Mplus), which can adjust the standard errors via bootstrapping to reduce bias and also remove exogenous measurement error.
Static models were fitted using standard linear regression methods, such as polyfit.
The data were fitted using standard ordinary least squares regression, and the effect on the intervention wards is captured by the difference in the difference.
Separate models were fitted using standard statistical packages (R package cmprsk with 'cuminc' function for cumulative incidences, the R package nlme for longitudinal analyses, and WinBUGs) though other statistical packages such as SAS SAS Inc.., Cary, NC) could have been used [ 47].
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