For this purpose, conventional methods such as reduction, precipitation, adsorption, oxidation and ion exchange are commonly used.
It is difficult to compare estimates across studies because of differences in study purpose and methods, such as sample characteristics, definitions of disability and unmet needs, type of activity considered, and data-collection approach (e.g. expert evaluation or self-reports).
These techniques have been found to be particularly useful in cases where only presence data are available and other more general-purpose statistical methods, such as multivariate logistic regression, are not applicable [24] [26].
Many efforts have been devoted for this purpose using various methods such as empirical approaches and numerical modeling.
General purpose data cleansing methods, such as the Kalman filter [20], do not have the specific knowledge needed to effectively eliminate the outliers to detect the borderline of a cell.
It is more appropriate for the present purpose than simpler methods such as direct comparison of sequence Hamming distances, because the present interest is in the active base pairs in the site, rather than base pair differences that have little functional impact on binding.
For comparison purposes, other popular methods, such as Support Vector Machine Recursive Feature Elimination (SVMRFE), logistic regression based Wald t-test and Logic regression (LOGICFS) for SNP selection and disease classification were compared.
Thus, there is a need to use alternative methods, such as bootstrapping or incremental net benefits (INB) for this purpose [ 15].
To this purpose, field and laboratory methods, such as: Seismic Refraction Tomography (SRT), Time Domain Reflectometry (TDR), and geotechnical tests were applied.
For this purpose, unfortunately, the traditional methods such as tensile measurements do not scale well into the micrometer- and nanometer-regions.
