Abstract: We compare two linear dimension-reduction methods for statisti-cal discrimination in terms of average probabilities of misclassification in re-duced dimensions. Using Monte Carlo simulation we compare the dimension-reduction methods over several different parameter configurations of multi-variate normal populations and find that the two methods yield very different results. We also apply the two dimension-reduction methods examined here to data from a study on football helmet design and neck injuries. Key words: Dimension reduction, discriminant analysis, singular value de-composition
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
A common problem in multivariate statistical analysis involves testing for differences in the mean v...
We describe a computer intensive method for linear dimension reduction which minimizes the classific...
Includes bibliographical references (p. 110-114).This dissertation consists of three selected topics...
This dissertation is comprised of four chapters. In the first chapter, we define the concept of lin...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
In this paper, we consider a linear supervised dimension reduction method for classification setting...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Classification studies with high-dimensional measurements and relatively small sample sizes are incr...
AbstractIn this paper some ideas on experimental designs are used in discriminant analysis. By consi...
In the multivariate single classification or one way analysis of variance model the mean vectors of ...
Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysi...
Abstract — In this paper, we consider a linear supervised dimension reduction method for classificat...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
A common problem in multivariate statistical analysis involves testing for differences in the mean v...
We describe a computer intensive method for linear dimension reduction which minimizes the classific...
Includes bibliographical references (p. 110-114).This dissertation consists of three selected topics...
This dissertation is comprised of four chapters. In the first chapter, we define the concept of lin...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
In this paper, we consider a linear supervised dimension reduction method for classification setting...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Classification studies with high-dimensional measurements and relatively small sample sizes are incr...
AbstractIn this paper some ideas on experimental designs are used in discriminant analysis. By consi...
In the multivariate single classification or one way analysis of variance model the mean vectors of ...
Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysi...
Abstract — In this paper, we consider a linear supervised dimension reduction method for classificat...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
A common problem in multivariate statistical analysis involves testing for differences in the mean v...
We describe a computer intensive method for linear dimension reduction which minimizes the classific...