Informative Versus Non-Informative Prior Distributions and Their Impact on the Accuracy of Bayesian Inference

Grzenda, Wioletta

Author

Grzenda Wioletta (Warsaw School of Economics, Poland)

Title

Informative Versus Non-Informative Prior Distributions and Their Impact on the Accuracy of Bayesian Inference

Source

Statistics in Transition, 2016, vol. 17, nr 4, s. 763-780, tab., wykr., bibliogr. s. 780

Keyword

Bezrobocie, Modele regresji, Wnioskowanie bayesowskie, Metoda Monte Carlo
Unemployment, Regression models, Bayesian inference, Monte Carlo method

Note

summ.
Materiały z konferencji Multivariate Statistical Analysis 2015, Łódź

Abstract

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)

Accessibility

The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice

Full text

Bibliography

ALBERT, J. H., CHIB, S., (1993). Bayesian analysis of binary and polychotomos response data. Journal of the American Statistical Association, 88, 669-679.
BOLSTAD, W. M., (2007). Introduction to Bayesian statistics, USA: Wiley & Sons.
CONGDON, P., (2006). Bayesian Statistical Modelling, 2nd ed., UK: John Wiley & Sons Inc.
DRAPER, N., SMITH, H., (1981). Applied Regression Analysis, 2nd ed., New York: John Wiley & Sons.
FINNEY, D. J., (1972). Probit Analysis, London: Cambridge University Press.
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.
GELMAN, A., CARLIN, J. B., STERN, H. S., RUBIN, D. B., (2000). Bayesian data analysis, London: Chapman & Hall/CRC.
GILL, J., (2008). Bayesian Methods, A Social and Behavioral Science Approach, USA: Chapman&Hall/CRC.
GOŁATA, E., (2004). Indirect Estimation of unemployment for the local labour market, Poznan: Publisher Academy of Economics in Poznan (in Polish).
GRZENDA, W., (2013). The significance of prior information in Bayesian parametric survival models. Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31-39.
HOSMER, D. W., LEMESHOW, S., (2000). Applied Logistic Regression, New York: Wiley.
JAPKOWICZ, N., SHAH, M., (2011). Evaluating Learning Algorithms. A Classification Perspective, New York: Cambridge University Press.
KOOP, G., (2003). Bayesian Econometrics, Chichester, UK: Wiley.
LANCASTER, T., (2004). An Introduction to Modern Bayesian Econometrics, Oxford, UK: Blackwell Publishing.
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.
TUFFÉRY, S., (2011). Data Mining and Statistics for Decision Making, Chichester, UK: Wiley.
VEHTARI, A., OJANEN, J., (2012). A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys, 6, 142-228.

Cited by

ISSN

1234-7655

Language

eng

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