Discriminant analysis, Advanced Statistics

A term which covers the large number of techniques for the analysis of the multivariate data which have in common the aim to assess whether or not the set of variables distinguish or discriminate between the two or more groups of the individuals. In medicine, for instance, this type of methods are generally applied to the problem of using optimally the results from the various tests or the observations of various symptoms to make the diagnosis which can only be confirmed perhaps by the post-mortem examination. In the two group case the mainly used method is Fisher's linear discriminant function, in which a linear function of variables giving the maximal separation between the groups is then determined. This results in the classification rule which may be used to assign the new patient to one of the two groups. The derivation of the linear function supposes that the variance-covariance matrices of the two groups are the same. If they are not then a quadratic discriminant function might be essential to distinguish between the groups. Such a function comprises of powers and cross-products of variables. The sample of the observations from which the discriminant function is derived is commonly known as the training set. When more than two groups are involved then it is possible to determine the several linear functions of the variables for separating them. In common the number of such functions which can be derived is the smaller of q and g-1 where q is the number of variables and g is the number of groups. The collection of the linear functions for discrimination is called as canonical discriminant functions or simply as canonical variates.

Posted Date: 7/27/2012 3:17:02 AM | Location : United States







Related Discussions:- Discriminant analysis, Assignment Help, Ask Question on Discriminant analysis, Get Answer, Expert's Help, Discriminant analysis Discussions

Write discussion on Discriminant analysis
Your posts are moderated
Related Questions
Oracle property is a name given to techniques for estimating the regression parameters in the models fitted to high-dimensional data which have the property that they can correctl

Hill-climbing algorithm is  an algorithm which is made in use in those techniques of cluster analysis which seek to find the partition of n individuals into g clusters by optimizin

Geo statistics: The body of methods useful for understanding and modelling spatial variability in a course of interest. Central to these techniques is the idea that measurements t


I need help solving a problem using excel.

The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307

1) Let N1(t) and N2(t) be independent Poisson processes with rates, ?1 and ?2, respectively. Let N (t) = N1(t) + N2(t). a) What is the distribution of the time till the next epoch

Regression dilution is the term which is applied when a covariate in the model cannot be measured directly and instead of that a related observed value must be used in analysis. I

Censored observations : An observation xi on some variable of interest is consired to be censored if it is known that xi Li (left-censored)or xi Ui (right-censored) where Li and Ui

The objective of this assignment is to test your understanding in the learning outcome (LO2) and learning outcome (LO3) and learning outcome (LO4). 1) This is a grouped assignme