A minority of studies have not demonstrated an association and this may be attributed to significant differences in the methodologies employed for sample selection, immunostaining techniques, vessel counting and statistical analysis, although a number of biological differences may account for the discrepancy.
The combination of random sampling and feature selection techniques were used to ensure the necessary diversity level of classifiers at the first step.
Because of the small sample size, backward model selection techniques were employed.
Both purposeful and simple random sampling procedures were used as techniques for sample selection.
On the other hand, as only a small subset of genes is potentially relevant for distinguishing the sample classes, feature selection technique is crucial to reduce the number of features, removes irrelevant, noisy, and redundant data, and results in acceptable classification accuracy.
In addition, it is necessary for researchers to develop standardized patient recruitment techniques to analyse sample selection bias.
Based upon these observations, optimal methods for sample selection are proposed and a variety of techniques for age estimation are explored.
For all endmember selection techniques, the number of selected endmembers varied across 25 random training samples.
Sample selection was based on stratified random sampling technique according to demography of adult population in year 2009 (Department of Statistics, Income, Expenditure and Population Statistics Division, Singapore).
This study used the propensity score matching technique to eliminate the possible sample selection bias since the data were from a survey study.
