Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Papers ; No. 172
Klassifikation
Wirtschaft
Statistical Simulation Methods: General
Index Numbers and Aggregation; Leading indicators
Urban, Rural, Regional, Real Estate, and Transportation Economics: Household Analysis: General
Statistical Simulation Methods: General
Index Numbers and Aggregation; Leading indicators
Urban, Rural, Regional, Real Estate, and Transportation Economics: Household Analysis: General
Thema
Principal Component Analysis
PCA
Partial Least Squares
PLS
non-metric variables
simulation
wealth index
PCA
Partial Least Squares
PLS
non-metric variables
simulation
wealth index
Ereignis
Geistige Schöpfung
(wer)
Yoon, Jisu
Krivobokova, Tatyana
Krivobokova, Tatyana
Ereignis
Veröffentlichung
(wer)
Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
(wo)
Göttingen
(wann)
2015
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
Objekttyp
- Arbeitspapier
Beteiligte
- Yoon, Jisu
- Krivobokova, Tatyana
- Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
Entstanden
- 2015
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