THE ADVANTAGES OF BAYESIAN METHODS OVER CLASSICAL METHODS IN THE CONTEXT OF CREDIBLE INTERVALS

• Authors: Grzenda Wioletta
• Languages of publication: EN

Abstracts

EN

The growing computational power of modern computer systems enables the efficient execution of algorithms. This is particularly important in Bayesian statistics, in which, nowadays, the key role is played by Markov Chain Monte Carlo methods. The primary objective of this work is to show the benefits arising from the use of Bayesian inference, especially confidence intervals in the context of logistic regression. The empirical analysis is based on "Household budgets" survey of Central Statistical Office. In this paper the unemployment among people over 55 will be investigated.

Keywords

EN

Bayesian inference; confidence intervals; MCMC; logistic regression; unemployment

• Publisher: Katedra Informatyki Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Information Systems in Management
• Year: 2015
• Volume: 4
• Issue: 1
• Pages: 53-63

Dates

published

2015

Contributors

author

Grzenda Wioletta

• Institute of Statistics and Demography, Warsaw School of Economics (SGH)

References

• Albert J.H., Chib S. (1993) Bayesian analysis of binary and polychotomos response data, Journal of the American Statistical Association 88, 669-679.
• Bernardo J., Smith A. (2004) Bayesian Theory, Wiley Series in Probability and Statistics, Wiley & Sons, New York.
• Bolstad W.M. (2007) Introduction to Bayesian statistics, Wiley & Sons, USA.
• Błędowski P. (2013) Older People in the labour market, in: Labour market and demographic change (ed. Kiełkowska M.), Demographic Publications, 52-63 (in Polish).
• Collier W. (2003) The Impact of Demographic and Individual Heterogeneity on Unemployment Duration: A Regional Study, Studies in Economics, 0302.
• Congdon P. (2007) Bayesian Statistical Modelling, Wiley & Sons, New York.
• Fisz M. (1967) Theory of Probability and mathematical statistic, PWN, Warsaw (in Polish).
• Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000) Bayesian data analysis, Chapman & Hall/CRC, USA.
• Geweke J. (1992) Evaluating the Accuracy of Sampling-based Approaches to Calculating Posterior Moments, in: Bernardo, J., Berger, J., Dawiv, A., Smith, A., Bayesian Statistics 4, 169-193.
• Gill J. (2008) Bayesian method: a social and behavioral sciences approach, Chapman & Hall/CRC, London.
• Gilks W., Best N., Tan K. (1995) Adaptive rejection Metropolis sampling with Gibbs sampling, Applied Statistics 44, 455-472.
• Grzenda W. (2011) The use of decision trees and logistic regression models to analyse demographic and socio-economic factors influencing the chances of finding a job, Economics Studies 95, 271-277 (in Polish).
• Grzenda W. (2012) Introduction to Bayesian Statistics, SGH Publishing House, Warsaw (in Polish).
• W. Grzenda. (2013) The significance of prior information in Bayesian parametric survival models, Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31-39.
• Ibrahim J.G., Chen M.H. (2000) Power Prior Distributions for Regression Models, Statistical Science, 15 (1), 46-60.
• Larose D.T. (2006) Data Mining Methods and Models, Wiley, New York,USA.
• Robert Ch.P., Casella G. (2004) Monte Carlo Statistical Methods, Springer, USA.
• http://stat.gov.pl/cps/rde/xbcr/gus/pw_kwart_inf_aktywnosc_ekonomiczna_ludnosci_1-4kw2010.zip (in Polish, 2010.01.10).

Identifiers

ISSN

2084-5537