Warianty tytułu
Języki publikacji
Abstrakty
In economic studies researchers are often interested in the estimation of the distribution function or certain functions of the distribution function such as quantiles. This work focuses on the estimation quantiles as inverses of the estimates of the distribution function in the presence of auxiliary information that is correlated with the study variable. In the paper a plug-in estimator of the distribution function is proposed which is used to obtain quantiles in the population and in the small areas. Performance of the proposed method is compared with other estimators of the distribution function and quantiles using the simulation study. The obtained results show that the proposed method usually has smaller relative biases and relative RMSE comparing to other methods of obtaining quantiles based on inverting the distribution function.(original abstract)
Czasopismo
Rocznik
Tom
Numer
Strony
97--114
Opis fizyczny
Twórcy
autor
- University of Economics in Katowice, Poland
Bibliografia
- Basuki, Widyanti, R., & Rajiani, I. (2021). Nascent entrepreneurs of millennial generations in the emerging market of Indonesia. Entrepreneurial Business and Economics Review, 9(2), 151-165.
- Beil, S., Kolb, J. P., & Münnich, R. (2011). Policy use of Laeken indicators. (Proceedings of the New Techniques and Technologies for Statistics 2011). Brussels, Belgium. https://doi.org/10.13140/2.1.4027.7764
- Berger, Y. G., & Muñoz, J. F. (2015). On estimating quantiles using auxiliary information. Journal of Official Statistics, 31(1), 101-119. https://doi.org/10.1515/JOS-2015-0005
- Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208. https://doi.org/10.1137/0916069
- Chambers, R. L., & Dunstan, R. (1986). Estimating distribution functions from survey data. Biometrika, 73(3), 597-604. https://doi.org/10.2307/2336524
- Chwila, A., & Żądło, T. (2020). On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction. Statistics in Transition, 21(2), 35-60. https://doi.org/10.21307/stattrans-2020-013
- Dorfman, A. H. (2009). Inference on distribution functions and quantiles. In D. Pfeffermann & C. R. Rao (Eds.), Handbook of statistics. Volume 29B Sample Surveys: Inference and Analysis (pp. 371-395). Amsterdam: Elsevier.
- Hajek, J. (1971) Comment on an essay on the logical foundations of survey sampling by Basu D. In V.P. Godambe & D.A. Sprott (Eds.), Foundations of statistical inference (pp. 36). Holt: Rinehart and Winston.
- Kuk, A. Y. C., & Mak, T. K. (1989). Median estimation in the presence of auxiliary information. Journal of the Royal Statistical Society: Series B (Methodological), 51(2), 261-269. https://doi.org/10.1111/j.2517-6161.1989.tb01763.x
- Mazurek, G., Korzyński, P., & Górska, A. (2019). Social media in the marketing of higher education institutions in Poland: Preliminary empirical studies. Entrepreneurial Business and Economics Review, 7(1), 117-133. https://doi.org/10.15678/ EBER.2019.070107
- McGrath, S., Sohn, H., Steele, R., & Benedetti, A. (2019). Meta-analysis of the difference of medians. Biometrical Journal, 69(2), 69-98.
- Mihi-Ramirez, A., Arteaga-Ortíz, J., & Ojeda-González, S. (2019). The international movements of capital and labour: A study of foreign direct investment and migration flows. Entrepreneurial Business and Economics Review, 7(3), 143-160. https:// doi.org/10.15678/EBER.2019.070308
- Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. The Canadian Journal of Statistics, 38, 369-385. https://doi.org/10.1002/cjs.10051
- Osier, G. (2009). Variance estimation for complex indicators of poverty and inequality using linearization techniques. Journal of the European Survey Research Association, 3, 167-195. https://doi.org/10.18148/srm/2009.v3i3.369
- R Core Team. (2020). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
- Rao, J. N. K., & Molina, I. (2015). Small area estimation. Hoboken: John Wiley & Sons.
- Ren, R., & Chambers, R. (2003). Outlier robust imputation of survey data via reverse calibration. (S3RI Methodology Working Papers No. M03/19). Southampton: Southampton Statistical Sciences Research Institute.
- Rueda, M., & Arcos, A. (2001). On estimating the median from survey data using multiple auxiliary information. Metrika, 54, 59-76. https://doi.org/10.1007/ s001840100116
- Rueda, M., Martinez, S., Martinez, H., & Arcos, A. (2007). Estimation of the distribution function with calibration methods. Journal of Statistical Planning and Inference, 137(2), 435-448. https://doi.org/10.1016/j.jspi.2005.12.011
- Salvati, N., Chandra, H., & Chambers, R. (2012). Model-based direct estimation of small-area distributions. Australian & New Zealand Journal of Statistics, 54(1), 103- -123. https://doi.org/10.1111/j.1467-842X.2012.00658.x
- Särndal, C. E., Swenson, B., & Wretman, J. (1992). Model assisted survey sampling. New York: Springer-Verlag.
- Serfling, R. J. (1980). Approximation theorems of mathematical statistics. New York: John Wiley & Sons.
- Silva, N., & Skinner, C. J. (1995). Estimating distribution functions with auxiliary information using poststratification. Journal of Official Statistics, 11(3), 277-294.
- Stachurski, T. (2018). A simulation analysis of the accuracy of median estimators for different sampling designs. In L. Vachova, V. Kratochvil (Eds.), Conference proceedings: 36th International Conference Mathematical Methods in Economics. MME 2018 (pp. 50-514). Praha: MatfyzPress.
- Tzavidis, N., Marchetti, S., & Chambers, R. (2010). Robust estimation of small area means and quantiles. Australian & New Zealand Journal of Statistics, 52(2), 167- -186. https://doi.org/10.1111/j.1467-842X.2010.00572.x
- Vijay, V., & Betti, G. (2011). Taylor linearization sampling errors and design effects for poverty measures and other complex statistics. Journal of Applied Statistics, 38(8), 1549-1576. https://doi.org/10.1080/02664763.2010.515674
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171628968

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