IDEAS home Printed from https://ideas.repec.org/a/spt/stecon/v8y2019i1f8_1_3.html
   My bibliography  Save this article

Estimation of Nested Error Non-parametric Unit Level Model

Author

Listed:
  • Patrick Munyangabo
  • Anthony Waititu
  • Anthony Kibira Wanjoya

Abstract

In this paper, we proposed a nested error nonparametric unit level model, when the linearity assumptions have been violated. The model formulation and parameter estimation of mean function were examined and proposed two theorems for asymptotic properties of mean function of proposed model were done. The simulation study was performed and it has shown that the mean square errors (MSE) and bias of our estimated were close to zero.Keywords: Schwarzs inequality; Triangle inequality

Suggested Citation

  • Patrick Munyangabo & Anthony Waititu & Anthony Kibira Wanjoya, 2019. "Estimation of Nested Error Non-parametric Unit Level Model," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(1), pages 1-3.
  • Handle: RePEc:spt:stecon:v:8:y:2019:i:1:f:8_1_3
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JSEM%2fVol%208_1_3.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non‐parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286, February.
    2. Nikos Tzavidis & Nicola Salvati & Monica Pratesi & Ray Chambers, 2008. "M-quantile models with application to poverty mapping," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 393-411, July.
    3. María José Lombardía & Stefan Sperlich, 2008. "Semiparametric inference in generalized mixed effects models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 913-930, November.
    4. Monica Pratesi & M. Ranalli & Nicola Salvati, 2009. "Nonparametric -quantile regression using penalised splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(3), pages 287-304.
    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. Patrick Munyangabo & Anthony Waititu & Anthony Kibira Wanjoya, 2019. "Nested Error Non-parametric Unit Level Model performance in the context of empirical Bayes (EB) approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(3), pages 1-3.

    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. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    2. Salvati, Nicola & Chandra, Hukum & Giovanna Ranalli, M. & Chambers, Ray, 2010. "Small area estimation using a nonparametric model-based direct estimator," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2159-2171, September.
    3. Xuemei Hu & Weiming Yang, 2019. "Semi-parametric small area inference in generalized semi-varying coefficient mixed effects models," Statistical Papers, Springer, vol. 60(4), pages 1039-1058, August.
    4. Chandra, Hukum & Salvati, Nicola & Chambers, Ray, 2018. "Small area estimation under a spatially non-linear model," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 19-38.
    5. Stefan Sperlich, 2013. "Comments on: Model-free model-fitting and predictive distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 227-233, June.
    6. González Manteiga, Wenceslao & Lombardía, María José & Martínez Miranda, María Dolores & Sperlich, Stefan, 2013. "Kernel smoothers and bootstrapping for semiparametric mixed effects models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 288-302.
    7. Jianhong Wu & Lixing Zhu, 2012. "Estimation of and testing for random effects in dynamic panel data models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 477-497, September.
    8. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    9. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    10. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.
    11. Alex Fenton, 2013. "Small-area measures of income poverty," CASE Papers case173, Centre for Analysis of Social Exclusion, LSE.
    12. repec:cep:sticas:/173 is not listed on IDEAS
    13. Lei Liu & Zhihua Sun, 2017. "Kernel-based global MLE of partial linear random effects models for longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 615-635, July.
    14. María José Lombardía & Esther López‐Vizcaíno & Cristina Rueda, 2017. "Mixed generalized Akaike information criterion for small area models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1229-1252, October.
    15. Bhuiyan, M. Kamruj Jaman & Hossain, M. Jamal & Islam, Mohammad Amirul & Imam, M. Farouq & Quddus, Md. Abdul, 2020. "Small Area Estimation Of Nutritional Status Of Under-Five Children In Sylhet Division: An M-Quantile Approach," Bangladesh Journal of Agricultural Economics, Bangladesh Agricultural University, vol. 41(1), July.
    16. Fenton, Alex, 2013. "Small-area measures of income poverty," LSE Research Online Documents on Economics 51269, London School of Economics and Political Science, LSE Library.
    17. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    18. María José Lombardía & Esther López-Vizcaíno & Cristina Rueda, 2021. "Selection model for domains across time: application to labour force survey by economic activities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 228-254, March.
    19. J. N. K. Rao, 2015. "Inferential Issues In Model-Based Small Area Estimation: Some New Developments," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 491-510, December.
    20. Marco Alfò & Maria Francesca Marino & Maria Giovanna Ranalli & Nicola Salvati & Nikos Tzavidis, 2021. "M‐quantile regression for multivariate longitudinal data with an application to the Millennium Cohort Study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 122-146, January.
    21. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.

    More about this item

    Keywords

    schwarzs inequality; triangle inequality;

    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:spt:stecon:v:8:y:2019:i:1:f:8_1_3. 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.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.