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This chapter reviews efficient index design methods for factor indices referred to as smart factor investing.
The literature demonstrates a variety of methods for factor extraction [ 40].
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Methods for factoring large whole numbers are of great importance in public-key cryptography, and on such methods rests the security (or lack thereof) of data transmitted over the Internet.
The effect of age class structure of a forest landscape resulting from past practices and disturbances before the reference year 1990 should be excluded, but methods for "factoring out" the effects of this age class legacy on carbon emissions and removals are lacking.
WLSMV is considered to be a strong estimation method for factor analysis with categorical data [ 27].
Thus an oblique rotation method for factor analysis was performed in this study, forcing a four-factor solution to replicate the original work [ 3, 6, 19].
An unweighted least squares factor analysis (ULS) was the method for factor extraction used in our exploratory factor analysis (EFA) in view of its robustness to failure of normality and heteroscedasticity of the data.
In the multivariate analysis, after adjusting for age and gender as well as diabetes and other risk factors (hypertension, smoking, hyperlipidaemia), a likelihood ratio method for factor inclusion was employed.
Using eigenvalues greater than 1 has been considered to be the least accurate method for factor retention and thus, this study also employed alternative methods such as scree test and parallel analysis [ 28].
We present a new method for factoring a real polynomial into the product of two polynomials which have their zeros inside and outside the unit circle, respectively.
Multivariate analyses were performed using a stepwise, descending method for factors with significance on univariate analyses.
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