Estimating female labor force participation through statistical and machine learning methods: A comparison (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Estimating female labor force participation through statistical and machine learning methods: A comparison (Contributo in atti di convegno) (literal)

Anno

• 2005-01-01T00:00:00+01:00 (literal)

Alternative label

• O. Zambrano, C. M. Rocco, M. Muselli (2005)
  Estimating female labor force participation through statistical and machine learning methods: A comparison
  in 8th Joint Conference on Information Sciences, Salt Lake City, Utah, USA, 21-26 Luglio 2005
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• O. Zambrano, C. M. Rocco, M. Muselli (literal)

Pagina inizio

• 952 (literal)

Pagina fine

• 955 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

• Proceedings of the 8th Joint Conference on Information Sciences (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 3 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Google Scholar (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• O. Zambrano: Inter-American Development Bank and Kennedy School of Government MPA/ID’05, USA C. M. Rocco: Facultad de Ingeniería, Universidad Central, Caracas, Venezuela M. Muselli: CNR-IEIIT, Genova, Italy (literal)

Titolo

• Estimating female labor force participation through statistical and machine learning methods: A comparison (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

• Blair, S; Chakraborty, U; Chen, SH; Cheng, HD; Chiu, DKY; Das, S; Denker, G; Duro, R; Romay, MG; Hung, D; Kerre, EE; VaLeong, H; Lu, CT; Lu, J; Maguire, L; Ngo, CW; Sarfraz, M; Tseng, C; Tsumoto, S; Ventura, D; Wang, PP; Yao, X; Zhang, CN; Zhang, K (literal)

Abstract

• Female Labor Force Participation (FLFP) is perhaps one of the most relevant theoretical issues within the scope of studies of both labor and behavioral economics. Many statistical models have been used for evaluating the relevance of explanatory variables. However, the decision to participate in the labor market can be also modeled as a binary classification problem. For this reason, in this paper, we compare four techniques to estimate the Female Labor Force Participation. Two of them, Probit and Logit are from the statistical area, while Support Vector Machines (SVM) and Hamming Clustering (HC) are from the machine learning paradigm. The comparison, performed using data from the Venezuelan Household Survey for the second semester 1999, shows the advantages and disadvantages of the two methodological paradigms that could provide a basic motivation for combining the best of both approaches. (literal)

Prodotto di

• Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) (Istituto)

Autore CNR

• MARCO MUSELLI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Estimating female labor force participation through statistical and machine learning methods: A comparison" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) (Istituto)
• http://www.cnr.it/ontology/cnr/individuo/modulo/ID2764

Autore CNR di

• MARCO MUSELLI (Unità di personale interno)

Insieme di parole chiave di

• Keywords of "Estimating female labor force participation through statistical and machine learning methods: A comparison" (Insieme di parole chiave)