Eigenvalue-based rules, Applied Statistics

Henry Kaiser suggested a rule for selecting a number of components m less than the number needed for perfect reconstruction: set m equal to the number of eigenvalues greater than I. This rule is often used in common factor analysis as well as in PCA. Several lines of thought lead to Kaiser's rule, but the simplest is that since an eigenvalue is the amount of variance explained by one more component, it  doesn't make sense to add a component that explains less variance than is contained in one variable. Since a component analysis is supposed to summarize a set of data, to use a component that explains less than a variance of I is something like writing a summary'of a book in which one section of the summary is longer than the book sectio~it summarizes--which makes no sense. However, Kaiser's ma-jor justification for th5 rule was that it matched pretty well the ultimate rule of doing several component analyses with diff-nt- numbers of komponents, and seeing which analysis made sense. That ultimate rule is much easier today than it was a generation ago, so Kaiser's rule seems obsolete.

Posted Date: 4/4/2013 3:46:01 AM | Location : United States







Related Discussions:- Eigenvalue-based rules, Assignment Help, Ask Question on Eigenvalue-based rules, Get Answer, Expert's Help, Eigenvalue-based rules Discussions

Write discussion on Eigenvalue-based rules
Your posts are moderated
Related Questions
This box plot displays the diversity wfood; the data ranges from 0.05710 being the minimum value and 0.78900 being the maximum value. The box plot is slightly positively skewed at

Do people of different age groups differ in their response to e-mail messages? A survey by the Cent of the Digital Future of the University of Southern California reported that 70.

Skewness Meaning and Definition  Literal meaning of skewness is lack of symmetry; it is a numerical measure which reveals asymmetry of a statistical series. According t

Analytical Approach We will illustrate this through an example. Example 1 A firm sells a product in a market with a few competitors. The average price charged by the

The prevalence of undetected diabetes in a population to be screened is approximately 1.5% and it is assumed that 10,000 persons will be screened. The screening test will measure

A researcher hypothesized that the pulse rates of long-distance athletes differ from those of other athletes. He believed that the runners’ pulses would be slower. He obtained a ra

Correlation Analysis Correlation Analysis is performed to measure the degree of association between two variables. The measure is called coefficient of correlation. The coeffic

implications of multicollinearity

A.    Do the correlation matrix table. B.    Which variable (s) has the largest correlation coeffieient which is not a perfect correlation? C.    Which variable (s) has the s

A sample of 43 houses that were purchased in the Southern California town Monrovia within a month was collected. We are interested in the study of the relationships between Price a