Modelowanie czasu trwania pierwszej pracy z wykorzystaniem Bayesowskich modeli przyspieszonej porażki AFT
DOI:
https://doi.org/10.18778/0208-6018.330.02Słowa kluczowe:
parametryczne modele przeżycia, modele AFT, podejście Bayesowskie, MCMC, zatrudnienieAbstrakt
W niniejszym artykule poddano analizie czas trwania pierwszej pracy osób w wieku 18–30 lat. Celem badania jest znalezienie rozkładu, który najlepiej opisuje badane zjawisko. W modelowaniu wykorzystano modele przyspieszonej porażki AFT w ujęciu Bayesowskim. Wykorzystanie podejścia Bayesowskiego rozszerzyło dotychczasowe badania przez możliwość uwzględnienia w badaniu informacji a priori oraz umożliwiło porównywanie rozkładów parametrów modeli. Ponadto dało możliwość porównania mocy wyjaśniającej konkurencyjnych modeli na gruncie teorii Bayesowskiej. Z wykorzystaniem zaproponowanych metod porównano czas trwania pierwszej pracy dla kobiet i mężczyzn.
Pobrania
Bibliografia
Akaike H. (1973), Information theory and an extension of the maximum likelihood principle, [in:] B.N. Petrov, F. Csaki (eds.), Second International Symposium on Information Theory, Akademiai Kiado, Budapest.
Google Scholar
Allison P.D. (1995), Survival Analysis Using the SAS®: A Practical Guide, 2nd ed., SAS Institute Inc., Cary.
Google Scholar
Ando T. (2010), Bayesian Model Selection and Statistical Modeling, CRC Press, Boca Raton.
Google Scholar
Bolstad W.M. (2007), Introduction to Bayesian Statistics, Wiley & Sons, USA.
Google Scholar
Casella G., George E.I. (1992), Explaining the Gibbs sample, “The American Statistician”, no. 46, pp. 167–174.
Google Scholar
Central Statistical Office (2014), Labour Force Survey (LFS).
Google Scholar
Congdon P. (2006), Bayesian Statistical Modelling, 2nd ed., John Wiley & Sons Inc., United Kingdom.
Google Scholar
Cox D.R. (1972), Regression models and life‑tables, “Journal of the Royal Statistical Society”, Series B, vol. 34, no. 2, pp. 187–220.
Google Scholar
Drobnič S., Frątczak E. (2001), Employment Patterns of Parried Women in Poland, Careers of Couples in Contemporary Society, Oxford University Press, New York.
Google Scholar
Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000), Bayesian Data Analysis, Chapman & Hall/ CRC, London.
Google Scholar
Generations and Gender Programme, http://www.ggp‑i.org/ [accessed: 1.09.2016].
Google Scholar
Gilks W., Wild P. (1992), Adaptive rejection sampling for Gibbs sampling, “Applied Statistics”, no. 41, pp. 337–348.
Google Scholar
Gill J. (2008), Bayesian Method: a Social and Behavioral Sciences Approach, Chapman & Hall/ CRC, London.
Google Scholar
Grzenda W. (2013), The significance of prior information in Bayesian parametric survival models, “Acta Universitatis Lodziensis. Folia Oeconomica”, no. 285, pp. 31–39.
Google Scholar
Ibrahim J.G., Chen M‑H., Sinha D. (2001), Bayesian Survival Analysis, Springer‑Verlag, New York.
Google Scholar
Jeffreys H. (1961), Theory of Probability, 3rd ed., Oxford University Press, Oxford.
Google Scholar
Kalbfleisch J.D., Prentice R.L. (2002), The Statistical Analysis of Failure Time Data, 2nd ed., Wiley Series in Probability and Statistics, Hoboken, New Jersey.
Google Scholar
Kass R.E., Raftery A.E. (1995), Bayes factors, “Journal of the American Statistical Association”, no. 90, pp. 773–795.
Google Scholar
Kim S.W., Ibrahim J.G. (2000), On Bayesian inference for parametric proportional hazards models using noninformative priors, “Lifetime Data Analysis”, no. 6, pp. 331–341.
Google Scholar
Lancaster T. (1979), Econometric methods for the duration of unemployment, “Econometrica”, vol. 47, no. 4, pp. 939–956.
Google Scholar
Landmesser J. (2013), The Use of Methods of Analysis of the Duration of the Labour Force Survey in Poland, Warsaw University of Life Sciences, Warsaw.
Google Scholar
Lawless J.L. (2003), Statistical Models and Methods for Lifetime Data, Wiley‑Interscience, Hoboken, New Jersey.
Google Scholar
Lee E.T., Wang J.W. (2003), Statistical Methods for Survival Data Analysis, John Wiley & Sons, Inc., Hoboken, New Jersey.
Google Scholar
Marzec J. (2008), Bayesian Models of Variable Quality and Limited Research Loans in Default, Cracow University of Economics, Cracow.
Google Scholar
Newton M.A., Raftery A.E. (1994), Approximate Bayesian inference by the weighted likelihood bootstrap, “Journal of the Royal Statistical Society”, Series B (Methodological), vol. 56, no. 1, pp. 3–48.
Google Scholar
Osiewalski J. (2001), Bayesian Econometrics Applications, Cracow University of Economics, Cracow.
Google Scholar
Raftery A. (1996), Bayesian model selection in social research, “Sociological Methodology”, no. 25, pp. 111–163.
Google Scholar
Spiegelhalter D., Best N., Carlin B., Linde A. van der (2002), Bayesian measures of model complexity and fit, “Journal of the Royal Statistical Society”, Series B, no. 64, pp. 583–639.
Google Scholar
Walker S., Mallick B.K. (1999), A Bayesian semiparametric accelerated failure time model, “Biometrics”, no. 55, pp. 477–483.
Google Scholar
Wei L.J. (1992), The accelerated failure time model: A useful alternative to the Cox regression model in survival analysis, “Statistics in Medicine”, no. 11, pp. 1871–1879.
Google Scholar
Wilks S.S. (1935), The likelihood test of independence in contingency tables, “The Annals of Mathematical Statistics”, no. 6, pp. 190–196.
Google Scholar
Wilks S.S. (1938), The large‑sample distribution of the likelihood ratio for testing composite hypotheses, “The Annals of Mathematical Statistics”, no. 9, p. 60–62.
Google Scholar