IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v67y2004i2p97-110.html
   My bibliography  Save this article

On influence assessment for LAD regression

Author

Listed:
  • Sun, Rui-Bo
  • Wei, Bo-Cheng

Abstract

Least absolute deviations (LAD) regression, i.e. L1 regression, is more resistant to the outliers in the response variable than the least-squares regression, but is relatively sensitive to outlying observations in explanatory variables. However, some but few attentions have been contributed to the influence assessment for LAD regression, especially for LAD nonlinear regression. In this paper, we propose several diagnostic measures, which can be used for LAD regression models. The quasi-likelihood distance based on the L1 objective function, Cook distance based on the elliptical norm and some other diagnostic measures are introduced for LAD regression, and two examples are given to illustrate the use of these measures. The diagnostic models for LAD regression are also investigated. It is proved that the estimators of the case deletion model (CDM) and the mean shift outlier model (MSOM) are equal in linear and nonlinear LAD regression models.

Suggested Citation

  • Sun, Rui-Bo & Wei, Bo-Cheng, 2004. "On influence assessment for LAD regression," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 97-110, April.
    • Handle: RePEc:eee:stapro:v:67:y:2004:i:2:p:97-110
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(03)00295-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Wang, J. D., 1995. "Asymptotic Normality of L1-Estimators in Nonlinear Regression," Journal of Multivariate Analysis, Elsevier, vol. 54(2), pages 227-238, August.
    2. Dodge, Yadolah, 1997. "LAD Regression for Detecting Outliers in Response and Explanatory Variables," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 144-158, April.
    3. Bo‐Cheng Wei & Yue‐Qing Hu & Wing‐Kam Fung, 1998. "Generalized Leverage and its Applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 25-37, March.
    4. Tang, Nian-Sheng & Wei, Bo-Cheng & Wang, Xue-Ren, 2000. "Influence diagnostics in nonlinear reproductive dispersion models," Statistics & Probability Letters, Elsevier, vol. 46(1), pages 59-68, January.
    5. McKean, Joseph W. & Sievers, Gerald L., 1987. "Coefficients of determination for least absolute deviation analysis," Statistics & Probability Letters, Elsevier, vol. 5(1), pages 49-54, January.
    6. Bo-Cheng Wei & Jian-Qing Shih, 1994. "On statistical models for regression diagnostics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 267-278, June.
    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. Vanegas, Luis Hernando & Cysneiros, Francisco José A., 2010. "Assessment of diagnostic procedures in symmetrical nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1002-1016, April.
    2. Vanegas, Luis Hernando & Rondón, Luz Marina & Cysneiros, Francisco José A., 2012. "Diagnostic procedures in Birnbaum–Saunders nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1662-1680.

    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. Li, Ai-Ping & Xie, Feng-Chang, 2012. "Diagnostics for a class of survival regression models with heavy-tailed errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4204-4214.
    2. Giulio Bottazzi & Marco Grazzi, 2014. "Dynamics Of Productivity And Cost Of Labour In Italian Manufacturing Firms," Bulletin of Economic Research, Wiley Blackwell, vol. 66(S1), pages 55-73, December.
    3. Noh, Hohsuk & El Ghouch, Anouar & Van Keilegom, Ingrid, 2013. "Assessing model adequacy in possibly misspecified quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 558-569.
    4. R.A.B. Assumpção & M.A. Uribe-Opazo & M. Galea, 2014. "Analysis of local influence in geostatistics using Student's t -distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2323-2341, November.
    5. Osorio, Felipe & Paula, Gilberto A. & Galea, Manuel, 2007. "Assessment of local influence in elliptical linear models with longitudinal structure," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4354-4368, May.
    6. Andréa Rocha & Alexandre Simas, 2011. "Influence diagnostics in a general class of beta regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 95-119, May.
    7. Leiva, Victor & Barros, Michelli & Paula, Gilberto A. & Galea, Manuel, 2007. "Influence diagnostics in log-Birnbaum-Saunders regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5694-5707, August.
    8. Cheng, C.-L. & Shalabh, & Garg, G., 2014. "Coefficient of determination for multiple measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 137-152.
    9. Xiaofei Wu & Rongmei Liang & Hu Yang, 2022. "Penalized and constrained LAD estimation in fixed and high dimension," Statistical Papers, Springer, vol. 63(1), pages 53-95, February.
    10. Xia, Tian & Tang, Nian-Sheng & Wang, Xue-Ren, 2006. "Consistency and asymptotic normality of the maximum likelihood estimates in reproductive dispersion linear models," Statistics & Probability Letters, Elsevier, vol. 76(11), pages 1137-1146, June.
    11. Ibacache-Pulgar, Germán & Paula, Gilberto A., 2011. "Local influence for Student-t partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1462-1478, March.
    12. Andy Lee & John Yick, 1999. "Covariate Transformation Diagnostics for Generalized Linear Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(2), pages 383-398, June.
    13. Cysneiros, Francisco José A. & Paula, Gilberto A. & Galea, Manuel, 2007. "Heteroscedastic symmetrical linear models," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1084-1090, June.
    14. Rongrong Xu & Jinde Wang, 2008. "-estimation for spatial nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(6), pages 523-537.
    15. Alcantara, Izabel Cristina & Cysneiros, Francisco José A., 2013. "Linear regression models with slash-elliptical errors," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 153-164.
    16. J. M. Muñoz-Pichardo & J. L. Moreno-Rebollo & R. Pino-Mejías & M. D. Cubiles Vega, 2019. "Influence measures in beta regression models through distance between distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 163-185, June.
    17. Silva, Giovana Oliveira & Ortega, Edwin M.M. & Cancho, Vicente G. & Barreto, Mauricio Lima, 2008. "Log-Burr XII regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3820-3842, March.
    18. Vanegas, Luis Hernando & Rondón, Luz Marina & Cysneiros, Francisco José A., 2012. "Diagnostic procedures in Birnbaum–Saunders nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1662-1680.
    19. Li, Ai-Ping & Chen, Zhao-Xia & Xie, Feng-Chang, 2012. "Diagnostic analysis for heterogeneous log-Birnbaum–Saunders regression models," Statistics & Probability Letters, Elsevier, vol. 82(9), pages 1690-1698.
    20. Brice Ozenne & Patrick M. Fisher & Esben Budtz‐J⊘rgensen, 2020. "Small sample corrections for Wald tests in latent variable models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 841-861, August.

    More about this item

    Keywords

    Diagnostics Inferential measure LAD regression L1 objective function Nonlinear regression Quasi-likelihood distance;

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

    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:eee:stapro:v:67:y:2004:i:2:p:97-110. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.