References
- Chung, Y. S., Lee, K. and Kim, B. C. (2003). Adjustment of unemployment estimates based on small area estimation in Korea. Survey Methodology, 29, 45-52.
- Craven, P. and Wahba, G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31, 377-403.
- Datta, G. S., Lahiri, P., Maiti, T. and Lu, K. L. (1999). Hierarchical Bayes estimation of unemployment rates for the states of the U.S. Journal of the American Statistical Association, 94, 1074-1082. https://doi.org/10.1080/01621459.1999.10473860
- Ghosh, M., Natarajan, K., Stroud, T. W. F. and Carlin, B. P. (1998). Generalized linear models for small area estimation. Journal of the American Statistical Association, 93, 273-282. https://doi.org/10.1080/01621459.1998.10474108
- Hwang, C. (2010). Support vector quantile regression for longitudinal data. Journal of Korean Data & Information Science Society, 21, 309-316.
- Hwang, C. (2011). Asymmetric least squares regression estimation using weighted least squares support vector machine. Journal of Korean Data & Information Science Society, 22, 995-1005.
- Hwang, C. and Shim, J. (2012). Mixed effects least squares support vector machine for survival data analysis. Journal of Korean Data & Information Science Society, 23, 739-748. https://doi.org/10.7465/jkdi.2012.23.4.739
- Jeong, S. and Shin, K. (2012). Small area estimation via nonparametric mixed effects model. The Korean Journal of Applied Statistics, 25, 457-464. https://doi.org/10.5351/KJAS.2012.25.3.457
- Khoshgooyanfard, A. and Monazzah, M. T. (2006). A cost effective strategy for provincial unemployment estimation: A small area approach. Survey Methodology, 32, 105-114.
- Kim, Y. and Choi, H. (2004). Small area estimation techniques based on logistic model to estimate unemployment rate. Communications of the Korean Statistical Society, 11, 583-595. https://doi.org/10.5351/CKSS.2004.11.3.583
- Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22. https://doi.org/10.1093/biomet/73.1.13
- Marker, D. A. (1999). Organization of small area estimators using a generalized linear regression framework. Journal of Official Statistics, 15, 1-24.
- Nelder, J. A. andWedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society A, 135, 370-384. https://doi.org/10.2307/2344614
- Noble, A., Haslett, S. and Arnold, G. (2002). Small area estimation via generalized linear models. Journal of Official Statistics, 18, 45-60.
- Opsomer, J. D., Claeskens, G., Ranalli, M. G., Kauermann, G. and Breidt, F. J. (2008). Non-parametric small area estimation using penalized spline regression. Journal of Royal Statistical Society B, 70, 265-286. https://doi.org/10.1111/j.1467-9868.2007.00635.x
- Pereira, L. N., Mendes, J. M. and Coelho, P. S. (2013). Model-based estimation of unemployment rates in small areas of Portugal. Communications in Statistics - Theory and Methods, 42, 1325-1342. https://doi.org/10.1080/03610926.2012.749989
- Pfeffermann, D. (2013). New important developments in small area estimation. Statistical Science, 28, 40-68. https://doi.org/10.1214/12-STS395
- Rao, J. N. K. (2003). Small area estimation, John Wiley & Sons, New Jersey.
- Salvati, N., Chandra, H., Ranalli, M. G. and Chambers, R. (2010). Small area estimation using a nonparametric model-based direct estimator. Computational Statistics and Data Analysis, 54, 2159-2171. https://doi.org/10.1016/j.csda.2010.03.023
- Shim, J. and Hwang, C. (2012). M-quantile kernel regression for small area estimation. Journal of Korean Data & Information Science Society, 23, 749-756. https://doi.org/10.7465/jkdi.2012.23.4.749
- Shim, J. and Hwang, C. (2013). Expected shortfall estimation using kernel machines. Journal of Korean Data & Information Science Society, 24, 12-20.
- Ugarte, M. D., Goicoa, T., Militino, A. F. and Sagaseta-Lopez, M. (2009). Estimating unemployment in very small areas. SORT, 33, 49-70.
- Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
- Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika, 61, 439-447.
- Yeo, I., Son, K. and Kim, Y. (2008). Small area estimation via generalized estimating equations and the panel analysis of unemployment rates. The Korean Journal of Applied Statistics, 21, 665-674. https://doi.org/10.5351/KJAS.2008.21.4.665
- You, Y., Rao, J. N. K. and Gambino, J. (2003). Model-based unemployment rate estimation for the Canadian labour force survey: A hierarchical Bayes approach. Survey Methodology, 29, 25-32.
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