- Autor
- Lahiri Partha (University of Maryland), Suntornchost Jiraphan (Chulalongkorn University, Thailand)
- Tytuł
- A General Bayesian Approach to Meet Different Inferential Goals in Poverty Research for Small Areas
- Źródło
- Statistics in Transition, 2020, vol. 21, nr 4 Special Issue, s. 237-253, tab., bibliogr. s. 251-253
- Słowa kluczowe
- Modele bayesowskie, Symulacja Monte Carlo, Statystyka małych obszarów, Wskaźniki ubóstwa
Bayesian models, Monte Carlo simulation, Small area estimates, Poverty indicators - Uwagi
- summ.
- Kraj/Region
- Chile
Chile - Abstrakt
- Poverty mapping that displays spatial distribution of various poverty indices is most useful to policymakers and researchers when they are disaggregated into small geographic units, such as cities, municipalities or other administrative partitions of a country. Typically, national household surveys that contain welfare variables such as income and expenditures provide limited or no data for small areas. It is well-known that while direct survey-weighted estimates are quite reliable for national or large geographical areas they are unreliable for small geographic areas. If the objective is to find areas with extreme poverty, these direct estimates will often select small areas due to the high variability in the estimates. Empirical best prediction and Bayesian methods have been proposed to improve on the direct point estimates. These estimates are, however, not appropriate for different inferential purposes. For example, for identifying areas with extreme poverty, these estimates would often select areas with large sample sizes. In this paper, using real life data, we illustrate how appropriate Bayesian methodology can be developed to address different inferential problems. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach - Pełny tekst
- Pokaż
- Bibliografia
- BELL, W. R., BASEL, W. W., MAPLES, J. J., (2016). An overview of the U.S. Census Bureaus Small Area Income and Poverty Estimates Program. In M. Pratesi (Ed.) Analysis of poverty data by small area estimation (pp. 349-377). West Sussex: Wiley & Sons, Inc.
- CASAS-CORDERO VALENCIA, C. ENCINA, J., LAHIRI, P., (2016). Poverty mapping in Chilean comunas. In M. Pratesi (Ed.) Analysis of Poverty Data by Small Area Estimation (pp. 379-403). West Sussex: Wiley & Sons, Inc.
- CHATTERJEE, S., LAHIRI, P., LI, H., (2008). Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models. The Annals of Statistics, 36, 3, pp. 1221-1245.
- CLAYTON, D., KALDOR, J., (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, pp. 671-681.
- EFRON, B., MORRIS, C., (1975). Data analysis using Stein's estimator and its generalizations. Journal of the American Statistical Association, 70, pp. 311-319.
- ELBERS, C., LANJOUW, J. O., LANJOUW, P., (2003). Micro-level estimation of poverty and inequality. Econometrica, 71, pp. 355-364.
- FAY, R. E., HARRIOT, R., (1979). Estimation of income from small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74, pp. 269-277.
- FOSTER, J., GREER, J., THORBECKE, E., (1984). A class of decomposable poverty measures. Econometrica, 52, pp. 761-766.
- FRONCO, C., BELL, W. R., (2015). Borrowing information over time in Binomial/Logit normal models for small area estimation. Joint Special Issue of Statistics in Transition and Survey Methodology, 16, 4, pp. 563-584.
- GELMAN, A., (2015). 3 New priors you can't do without, for coefficients and variance parameters in multilevel regression. Statistical Modeling, Causal Inference, and Social Science blog, 7 Nov. http://andrewgelman.com/2015/11/07/priorsfor-coefficients-and-variance-parameters-in-multilevel-regression/
- GELMAN, A., PRICE, P. N., (1999). All maps of parameter estimates are misleading. Statistics in Medicine, 18, pp. 3221-3234.
- GELMAN, A., RUBIN, D. B., (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7,4, pp. 457-511.
- GHOSH, M., (1992). Constrained Bayes estimation with applications. Journal of the American Statistical Association, 87, 418, pp. 533-540.
- JIANG, J., LAHIRI, P., (2006). Mixed model prediction and small area estimation. TEST, 15, pp. 1-96.
- JONES, H. E., SPIEGELHALTER, D. J., (2011). The identification of unusual healthcare providers from a hierarchical model. American Statistician, 65, pp. 154-163, DOI: 10.1198/tast.2011.10190.
- LAHIRI, P., (1990), "Adjusted" Bayes and empirical Bayes estimation in finite population sampling, Sankhya, B, 52, pp. 50-66.
- LANGFORD, I. H., LEWIS, T., (1998). Outliers in multilevel data. Journal of the Royal Statistical Society, Ser. A, 161, pp. 121-160.
- LI, H. and LAHIRI, P., (2010). Adjusted maximum method for solving small area estimation problems, Journal of Multivariate Analysis, 101, pp. 882-892.
- LOUIS, T. A., (1984). Estimating a population of parameter values using Bayes and empirical Bayes methods. Journal of the American Statistical Association, 79, 386, pp. 393-398.
- MARSHALL, R. J., (1991). Mapping disease and mortality rates using empirical Bayes estimators. Appllied Statistics, 40, pp. 283-294.
- MOLINA, I., NANDRAM, B., RAO, J. N. K., (2014). Small area estimation of general parameters with application to poverty indicators: a hierarchical Bayes approach. The Annals of Applied Statistics, Vol. 8, No. 2, pp. 852-885.
- MOLINA, I., RAO, J. N. K., (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics, 38, pp. 369-385.
- MOLINA, A., RICHARDSON, S., (1991). Empirical Bayes estimates of cancer mortality rates using spatial models. Statistics in Medicine, 10, pp. 95-112.
- MORRIS, C. N., CHRISTIANSEN, C. L., (1996). Hierarchical models for ranking and for identifying extremes, with applications, in Bayesian statistics 5, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F.M. Smith, Oxford: Oxford University Press, pp. 277-296.
- NANDRAM, B., SEDRANSK, J., PICKLE, L. W., (2000). Bayesian analysis and mapping of mortality rates for chronic obstructive pulmonary disease. Journal of American Statistical Association, 95, pp. 1110-1118.
- PFEFFERMANN, D., (2013). New important developments in small area estimation. Statistical Science, 28, pp. 40-68.
- PRASAD, N. G. N., RAO, J. N. K., (1990). The estimation of the mean squared error of small-area estimators. Journal of American Statistical Association, 85, pp. 163-171.
- RAO, J.N.K. and MOLINA, A., (2015). Small area estimation. Wiley.
- RAVALLION, M., (1994). Poverty comparisons. Chur, Switzerland: Harwood Academic Press.
- SHEN, W., LOUIS, T. A., (1998). Triple-goal estimates in two-stage hierarchical models. Journal of Royal Statistical Society Series B, 60,Part 2, pp. 455-471.
- TSUTUKAWA, R. K., SHOOP, G. L., MARIENFELD. C. J., (1985). Empirical Bayes estimation of cancer mortality rates. Statistics in Medicine, 4, pp. 201-212.
- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng
- URI / DOI
- http://dx.doi.org/10.21307/stattrans-2020-040