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
Authors:	Yoon, Jisu ; Krivobokova, Tatyana
Publisher:	Göttingen : Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
Subject:	Principal Component Analysis \| PCA \| Partial Least Squares \| PLS \| non-metric variables \| simulation \| wealth index

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Series:	Discussion Papers ; 172
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	821236776 [GVK] hdl:10419/109027 [Handle]
Classification:	C15 - Statistical Simulation Methods; Monte Carlo Methods ; C43 - Index Numbers and Aggregation ; R20 - Household Analysis. General
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010504410