Treatments of Non-metric Variables in Partial Least Squares and Principal Component Analysis

This paper reviews various treatments of non-metric variables in Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms. The performance of different treatments is compared in the extensive simulation study under several typical data generating processes and recommendations are made. An application of PLS and PCA algorithms with non-metric variables to the generation of a wealth index is considered.

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Year of publication:	2015-03-27
Authors:	Yoon, Jisu ; Krivobokova, Tatyana
Institutions:	Courant Research Centre PEG
Subject:	Principal Component Analysis \| PCA \| Partial Least Squares \| PLS \| non-metric variables \| simulation \| wealth index

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Extent:	application/pdf
Series:	Courant Research Centre: Poverty, Equity and Growth - Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	Number 172
Classification:	C15 - Statistical Simulation Methods; Monte Carlo Methods ; C43 - Index Numbers and Aggregation ; R20 - Household Analysis. General
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011212213