By Isabella Morlini, Tommaso Minerva, Maurizio Vichi
This edited quantity makes a speciality of contemporary learn leads to category, multivariate facts and laptop studying and highlights advances in statistical versions for information research. the quantity presents either methodological advancements and contributions to a variety of program parts comparable to economics, advertising, schooling, social sciences and surroundings. The papers during this quantity have been first provided on the ninth biannual assembly of the category and knowledge research workforce (CLADAG) of the Italian Statistical Society, held in September 2013 on the college of Modena and Reggio Emilia, Italy.
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Additional info for Advances in Statistical Models for Data Analysis
24, 313–328 (2006) 7. : Bayesian Markov Switching Stochastic Correlation Models, Working Papers No 2013:11, Department of Economics, University of Venice Ca’ Foscari. Female Labour Force Participation and Selection Effect: Southern vs Eastern European Countries Rosalia Castellano, Gennaro Punzo, and Antonella Rocca Abstract The aim of this paper is to explore the main determinants of women’s job search propensity as well as the mechanism underlying the selection effect across the four European countries (Italy, Greece, Hungary and Poland) with the lowest female labour force participation.
Econ. 66, 281–302 (1958) 14. : Selection, investment and women’s relative wage over time. Q. J. Econ. 123(3), 219–277 (2008) 15. : Investment in human capital. Am. Econ. Rev. 51(1), 1–17 (1961) 16. : Job search behavior of unemployed in Russia. Bank of Finland Discussion Papers 13, pp. 1–36 (2003) 17. : Labor Economics, Routledge, London (2003) Asymptotics in Survey Sampling for High Entropy Sampling Designs Pier Luigi Conti and Daniela Marella Abstract The aim of the paper is to establish asymptotics in sampling finite populations.
Stat. 36, 1324–1345 (2008) 3 The authors thank Marco Riani and Agustìn Mayo Iscar for the fruitful discussions on the approach. Robust Clustering of EU Banking Data 25 7. : A review of robust clustering methods. In: Advanced in Data Analysis and Classification, pp. 89–109. Springer, New York (2010) 8. : Exploring the number of groups in robust model based clustering. Stat. Comput. 21(4), 585–599 (2011) 9. : A constrained robust proposal for mixture modeling avoiding spurious solutions. Adv. Data Anal.