Next, we describe estimation of reaction rate constants via quantum mechanical and statistical mechanical methods as well as semi-empirical methods.
Before describing estimation of this quantity, we consider 2 differences between the data presented here and the data used in empirical Bayes analysis of microarray experiments.
In practical applications, it is necessary to describe estimation uncertainty.
95% confidence intervals (CIs) are used to describe estimation uncertainty.
To describe estimation uncertainty, it is necessary to calculate confidence intervals for these new effect measures.
Continuous data were described using means, medians, and ranges, and 95% confidence intervals (CI) were used to describe estimation uncertainty.
We will start be describing estimation of multiplicative interactions as are seen in the logistic model context, for binary predictors.
In this article, we describe estimation methods that are based on optimizing two superficially different objective functions.
To assess false-positive rates "on the fly" for obtained total scores S, a previously described estimation methodology [ 17, 20, 79, 80] is used that follows the spirit of BLAST p-values [ 81].
For a functional assessment of risk, the American College of Cardiology/American Heart Association guidelines describe estimation of METS (metabolic equivalents; Duke Activity Status Index [ 17]), with one MET representing adult resting oxygen consumption (VO2) and four METS or less representing poor cardiorespiratory function and, therefore, high risk.
