Now showing items 1-6 of 6
Non-parametric regression modelling of in situ fCO2 in the Southern Ocean
(Stellenbosch : Stellenbosch University, 2012-12)
ENGLISH ABSTRACT: The Southern Ocean is a complex system, where the relationship between CO2 concentrations and its drivers varies intra- and inter-annually. Due to the lack of readily available in situ data in the ...
The implementation of noise addition partial least squares
(Stellenbosch : University of Stellenbosch, 2009-03)
When determining the chemical composition of a specimen, traditional laboratory techniques are often both expensive and time consuming. It is therefore preferable to employ more cost effective spectroscopic techniques such ...
A comparison of support vector machines and traditional techniques for statistical regression and classification
(Stellenbosch : Stellenbosch University, 2004-04)
ENGLISH ABSTRACT: Since its introduction in Boser et al. (1992), the support vector machine has become a popular tool in a variety of machine learning applications. More recently, the support vector machine has also ...
Aspects of model development using regression quantiles and elemental regressions
(Stellenbosch : Stellenbosch University, 2007-03)
ENGLISH ABSTRACT: It is well known that ordinary least squares (OLS) procedures are sensitive to deviations from the classical Gaussian assumptions (outliers) as well as data aberrations in the design space. The two major ...
Influential data cases when the C-p criterion is used for variable selection in multiple linear regression
(Stellenbosch : Stellenbosch University, 2003)
ENGLISH ABSTRACT: In this dissertation we study the influence of data cases when the Cp criterion of Mallows (1973) is used for variable selection in multiple linear regression. The influence is investigated in terms ...
Edgeworth-corrected small-sample confidence intervals for ratio parameters in linear regression
(Stellenbosch : Stellenbosch University, 2002-03)
ENGLISH ABSTRACT: In this thesis we construct a central confidence interval for a smooth scalar non-linear function of parameter vector f3 in a single general linear regression model Y = X f3 + c. We do this by ...