Performance comparison of estimation methods.

<p>Estimated RMR time series by means of linear regression, TEE averaging for zero activity, Kalman filtering and penalised spline modelling are shown for a typical simulated (<b>A</b>) and experimental (<b>B</b>) dataset. The overall estimation accuracy in average RMR (<b>C</b>) and time-dependent RMR (<b>D</b>) was calculated based on 500 simulated datasets (expressed as Root Mean Square Error). For the estimation error in the average RMR the bias-variance decomposition was calculated to gain more insight in the origin of the error (<b>E</b>). Error bars indicate half the standard deviation.