IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v44y2003i4p555-579.html
   My bibliography  Save this article

Imputation by power transformation

Author

Listed:
  • Sarjinder Singh
  • Balbinder Deo

Abstract

No abstract is available for this item.

Suggested Citation

  • Sarjinder Singh & Balbinder Deo, 2003. "Imputation by power transformation," Statistical Papers, Springer, vol. 44(4), pages 555-579, October.
    • Handle: RePEc:spr:stpapr:v:44:y:2003:i:4:p:555-579
    DOI: 10.1007/BF02926010
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02926010
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02926010?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
    ---><---

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

    References listed on IDEAS

    as
    1. A. Cebrián & M. García, 1997. "Variance estimation using auxiliary information: An almost unbiased multivariate ratio estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 45(1), pages 171-178, January.
    2. Sarjinder Singh, 2001. "Generalized Calibration Approach for Estimating Variance in Survey Sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 404-417, June.
    3. M. Garcia & A. Cebrian, 1996. "Repeated substitution method: The ratio estimator for the population variance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 43(1), pages 101-105, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Shakti Prasad, 2018. "Product Exponential Method Of Imputation In Sample Surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 19(1), pages 159-166, March.
    2. Shashi Bhushan & Abhay Pratap Pandey, 2021. "Optimal imputation of the missing data using multi auxiliary information," Computational Statistics, Springer, vol. 36(1), pages 449-477, March.
    3. Prasad Shakti, 2018. "Product Exponential Method Of Imputation In Sample Surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 19(1), pages 159-166, March.

    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. M. Rueda & A. Arcos, 2004. "Improving ratio-type quantile estimates in a finite population," Statistical Papers, Springer, vol. 45(2), pages 231-248, April.
    2. Rajesh Tailor & Balkishan Sharma, 2012. "Modified estimators of population variance in presence of auxiliary information," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(1), pages 37-46, March.
    3. Housila Singh & Ramkrishna Solanki, 2013. "A new procedure for variance estimation in simple random sampling using auxiliary information," Statistical Papers, Springer, vol. 54(2), pages 479-497, May.
    4. Pandey Ranjita & Yadav Kalpana, 2017. "Population Variance Estimation Using Factor Type Imputation Method," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 375-392, September.
    5. Muhammad Riaz & Ronald Does, 2009. "A process variability control chart," Computational Statistics, Springer, vol. 24(2), pages 345-368, May.
    6. Kim, Jong-Min & Sungur, Engin A. & Heo, Tae-Young, 2007. "Calibration approach estimators in stratified sampling," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 99-103, January.
    7. Sarjinder Singh, 2006. "Survey statisticians celebrate golden jubilee year 2003 of the linear regression estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(1), pages 1-18, February.
    8. Stearns, Matthew & Singh, Sarjinder, 2008. "On the estimation of the general parameter," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4253-4271, May.
    9. A. Cebrián & M. García, 1997. "Variance estimation using auxiliary information: An almost unbiased multivariate ratio estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 45(1), pages 171-178, January.
    10. Antonio Arcos & José M. Contreras & María M. Rueda, 2014. "A Novel Calibration Estimator in Social Surveys," Sociological Methods & Research, , vol. 43(3), pages 465-489, August.
    11. Mariano Ruiz Espejo & Miguel Delgado Pineda & Saralees Nadarajah, 2013. "Optimal unbiased estimation of some population central moments," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 39-62, June.
    12. Muhammad Riaz, 2008. "Monitoring process variability using auxiliary information," Computational Statistics, Springer, vol. 23(2), pages 253-276, April.
    13. Ranjita Pandey & Kalpana Yadav, 2017. "Population Variance Estimation Using Factor Type Imputation Method," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 375-392, September.
    14. Housila Singh & Ritesh Tailor & Sarjinder Singh & Jong-Min Kim, 2011. "Estimation of population variance in successive sampling," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 477-494, April.
    15. Maria del Mar Rueda, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1077-1081, December.
    16. Rueda, M. & Martinez, S. & Martinez, H. & Arcos, A., 2006. "Mean estimation with calibration techniques in presence of missing data," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3263-3277, July.
    17. Martínez, S. & Rueda, M. & Arcos, A. & Martínez, H. & Sánchez-Borrego, I., 2011. "Post-stratified calibration method for estimating quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 838-851, January.

    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:spr:stpapr:v:44:y:2003:i:4:p:555-579. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.