Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v106i493y2011p362-374.html
   My bibliography  Save this article

Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data

Author

Listed:
  • Datta, Gauri S.
  • Hall, Peter
  • Mandal, Abhyuday
Abstract
No abstract is available for this item.

Suggested Citation

  • Datta, Gauri S. & Hall, Peter & Mandal, Abhyuday, 2011. "Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 362-374.
    • Handle: RePEc:bes:jnlasa:v:106:i:493:y:2011:p:362-374
    as

    Download full text from publisher

    File URL: http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.tm10036
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2017. "Transforming response values in small area prediction," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 47-60.
    2. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2017. "Bayesian estimators in uncertain nested error regression models," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 52-63.
    3. repec:csb:stintr:v:17:y:2016:i:1:p:67-90 is not listed on IDEAS
    4. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    5. Elżbieta Gołata, 2015. "SAE education challenges to academics and NSI," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 611-630, December.
    6. 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.
    7. Sun, Hanmei & Jiang, Jiming & Nguyen, Thuan & Luan, Yihui, 2018. "Best look-alike prediction: Another look at the Bayesian classifier and beyond," Statistics & Probability Letters, Elsevier, vol. 143(C), pages 37-42.
    8. Shonosuke Sugasawa & Tatsuya Kubokawa & Kota Ogasawara, 2017. "Empirical Uncertain Bayes Methods in Area-level Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 684-706, September.
    9. Jiming Jiang & Mahmoud Torabi, 2022. "Goodness-of-fit test with a robustness feature," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 76-100, March.
    10. Ghosh Malay, 2020. "Small area estimation: its evolution in five decades," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 1-22, August.
    11. Elaheh Torkashvand & Mohammad Jafari Jozani & Mahmoud Torabi, 2017. "Clustering in small area estimation with area level linear mixed models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1253-1279, October.
    12. Xiaohui Liu & Yuanyuan Li & Jiming Jiang, 2021. "Simple measures of uncertainty for model selection," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 673-692, September.
    13. Adrijo Chakraborty & Gauri Sankar Datta & Abhyuday Mandal, 2016. "A Two-Component Normal Mixture Alternative To The Fay-Herriot Model," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 67-90, March.
    14. Nikos Tzavidis & Li‐Chun Zhang & Danny Pfeffermann & Partha Lahiri, 2017. "Preface to the papers on ‘Small area estimation’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1035-1037, October.
    15. Stefano Marchetti & Caterina Giusti & Monica Pratesi, 2016. "The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy [Die Nutzung von Twitter Daten um die Small Area Schätzungen vom Ausgabenanteil," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 79-93, October.
    16. Tzavidis, Nikos & Zhang, Li-Chun & Luna Hernandez, Angela & Schmid, Timo & Rojas-Perilla, Natalia, 2016. "From start to finish: A framework for the production of small area official statistics," Discussion Papers 2016/13, Free University Berlin, School of Business & Economics.
    17. Elżbieta Gołata, 2015. "Sae Education Challenges To Academics And Nsi," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 611-630, December.
    18. Chakraborty Adrijo & Datta Gauri Sankar & Mandal Abhyuday, 2016. "A Two-Component Normal Mixture Alternative to the Fay-Herriot Model," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 67-90, March.
    19. Malay Ghosh, 2020. "Small area estimation: its evolution in five decades," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 1-22, August.
    20. Gołata Elżbieta, 2015. "Sae Education Challenges to Academics and NSI," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 611-630, December.
    21. Serena Arima & Gauri S. Datta & Brunero Liseo, 2015. "Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 518-529, June.
    22. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.

    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:bes:jnlasa:v:106:i:493:y:2011:p:362-374. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    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.