1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

The Mahalanobis distance

A review of variable selection methods in Partial Least Squares Regression

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

A comparison of multiple linear regression (MLR) with partial least-squares (PLS) regression is presented, for the multivariate modeling of hydroxyl number in a certain polymer of a heterogeneous near-IR spectroscopic data set. The MLR model was performed by selecting the variables with a genetic algorithm. A good model could be obtained with both methods. It was shown that the MLR and PLS solutions are very similar. The two models include the same number of variables, and the first variables in each model have similar, chemically understandable functions. It is concluded that both solutions are equivalent and that each has some advantages and disadvantages. This also means that even in very complex situations such as here, MLR can replace PLS in certain cases.

Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration

The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra.

An approach aiming at extracting the relevant component for multivariate calibration is introduced, and its performance is compared with the “uninformative variable elimination” approach and with the standard PLS method for the modeling of near-infrared data. The extraction of the relevant component is carried out in the wavelet domain. The PLS results on these relevant features are better, and therefore, it seems that this approach can successfully be used to remove noise and irrelevant information from spectra for multivariate calibration.

Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration.

Comparison of multivariate methods based on latent vectors and methods based on wavelength selection for the analysis of near-infrared spectroscopic data

Delphine Jouan-Rimbaud

Papers

The Mahalanobis distance

Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration

The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra.

Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration.

Comparison of multivariate methods based on latent vectors and methods based on wavelength selection for the analysis of near-infrared spectroscopic data