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Autor
Grzenda Wioletta (Warsaw School of Economics, Poland)
Tytuł
Informative Versus Non-Informative Prior Distributions and Their Impact on the Accuracy of Bayesian Inference
Źródło
Statistics in Transition, 2016, vol. 17, nr 4, s. 763-780, tab., wykr., bibliogr. s. 780
Słowa kluczowe
Modele regresji, Wnioskowanie bayesowskie, Metoda Monte Carlo, Bezrobocie
Regression models, Bayesian inference, Monte Carlo method, Unemployment
Uwagi
summ., Materiały z konferencji Multivariate Statistical Analysis 2015, Łódź
Abstrakt
In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and noninformative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
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Bibliografia
Pokaż
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  2. BOLSTAD, W. M., (2007). Introduction to Bayesian statistics, USA: Wiley & Sons.
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  8. GILL, J., (2008). Bayesian Methods, A Social and Behavioral Science Approach, USA: Chapman&Hall/CRC.
  9. GOŁATA, E., (2004). Indirect Estimation of unemployment for the local labour market, Poznan: Publisher Academy of Economics in Poznan (in Polish).
  10. GRZENDA, W., (2013). The significance of prior information in Bayesian parametric survival models. Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31-39.
  11. HOSMER, D. W., LEMESHOW, S., (2000). Applied Logistic Regression, New York: Wiley.
  12. JAPKOWICZ, N., SHAH, M., (2011). Evaluating Learning Algorithms. A Classification Perspective, New York: Cambridge University Press.
  13. KOOP, G., (2003). Bayesian Econometrics, Chichester, UK: Wiley.
  14. LANCASTER, T., (2004). An Introduction to Modern Bayesian Econometrics, Oxford, UK: Blackwell Publishing.
  15. PROVOST, F., FAWCETT, T., (2013). Data Science for Business: What You Need to Know About Data Mining and Data-analytic Thinking, USA: O'Reilly Media, Inc.
  16. TUFFÉRY, S., (2011). Data Mining and Statistics for Decision Making, Chichester, UK: Wiley.
  17. VEHTARI, A., OJANEN, J., (2012). A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys, 6, 142-228.
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ISSN
1234-7655
Język
eng
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