IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v21y2020i4p40-44n2.html
   My bibliography  Save this article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Author

Listed:
  • Molina Isabel

    (Department of Statistics, Universidad Carlos III de Madrid, Madrid, ; Spain)

Abstract

No abstract is available for this item.

Suggested Citation

  • Molina Isabel, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 40-44, August.
    • Handle: RePEc:vrs:stintr:v:21:y:2020:i:4:p:40-44:n:2
    DOI: 10.21307/stattrans-2020-026
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2020-026
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2020-026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    2. Guadarrama, María & Molina, Isabel & Rao, J.N.K., 2018. "Small area estimation of general parameters under complex sampling designs," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 20-40.
    3. Monique Graf & J. Miguel Marín & Isabel Molina, 2019. "A generalized mixed model for skewed distributions applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 565-597, June.
    4. Mamadou S. Diallo & J. N. K. Rao, 2018. "Small area estimation of complex parameters under unit‐level models with skew‐normal errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(4), pages 1092-1116, December.
    5. Yolanda Marhuenda & Isabel Molina & Domingo Morales & J. N. K. Rao, 2017. "Poverty mapping in small areas under a twofold nested error regression model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1111-1136, October.
    6. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Isabel Molina, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 40-44, August.
    2. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    3. Guadarrama, María & Morales, Domingo & Molina, Isabel, 2021. "Time stable empirical best predictors under a unit-level model," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Paul Walter & Marcus Groß & Timo Schmid & Nikos Tzavidis, 2021. "Domain prediction with grouped income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1501-1523, October.
    5. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    6. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    7. Juan Manuel Espejo Benítez & José María Millán Tapia, 2023. "Población en riesgo de pobreza y/o exclusión social. Propuesta metodológica para la estimación del indicador AROPE en los municipios de Andalucía," Hacienda Pública Española / Review of Public Economics, IEF, vol. 246(3), pages 101-135, September.
    8. Stefano Marchetti & Maciej Beręsewicz & Nicola Salvati & Marcin Szymkowiak & Łukasz Wawrowski, 2018. "The use of a three‐level M‐quantile model to map poverty at local administrative unit 1 in Poland," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1077-1104, October.
    9. Agne Bikauskaite & Isabel Molina & Domingo Morales, 2022. "Multivariate mixture model for small area estimation of poverty indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 724-755, December.
    10. María Dolores Esteban & María José Lombardía & Esther López‐Vizcaíno & Domingo Morales & Agustín Pérez, 2022. "Empirical best prediction of small area bivariate parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1699-1727, December.
    11. Natalia Rojas‐Perilla & Sören Pannier & Timo Schmid & Nikos Tzavidis, 2020. "Data‐driven transformations in small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 121-148, January.
    12. Aldo Gardini & Enrico Fabrizi & Carlo Trivisano, 2022. "Poverty and inequality mapping based on a unit‐level log‐normal mixture model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2073-2096, October.
    13. Chwila Adam & Żądło Tomasz, 2020. "On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 35-60, June.
    14. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    15. Adam Chwila & Tomasz Żądło, 2020. "On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 35-60, June.
    16. Bijlsma Ineke & van den Brakel Jan & van der Velden Rolf & Allen Jim, 2020. "Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data," Journal of Official Statistics, Sciendo, vol. 36(2), pages 251-274, June.
    17. John A. Maluccio, 2009. "Household targeting in practice: The Nicaraguan Red de Protección Social," Journal of International Development, John Wiley & Sons, Ltd., vol. 21(1), pages 1-23.
    18. Claudio A. Agostini & Philip H. Brown, 2010. "Local Distributional Effects Of Government Cash Transfers In Chile," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(2), pages 366-388, June.
    19. Tomoki Fujii, 2013. "Geographic decomposition of inequality in health and wealth: evidence from Cambodia," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(3), pages 373-392, September.
    20. Antonio Yúnez Naude & Jesús Arellano González & Jimena Méndez Navarro, 2010. "Cambios en el bienestar de 1990 a 2005: un estudio espacial para México," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 25(2), pages 363-406.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:stintr:v:21:y:2020:i:4:p:40-44:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://stat.gov.pl/en/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.