IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v29y2002i8p1191-1204.html
   My bibliography  Save this article

Influence diagnostics for the structural errors-in-variables model under the Student-t distribution

Author

Listed:
  • Manuel Galea
  • Heleno Bolfarine
  • Filidor Vilcalabra

Abstract

The influence of observations on the parameter estimates for the simple structural errors-in-variables model with no equation error, under the Student-t distribution, is investigated using the local influence approach. The main conclusion is that the Student-t model with small degrees of freedom is able to incorporate possible outliers and influential observations in the data. The likelihood displacement approach is useful for outlier detection, especially when a masking phenomenon is present and the degrees of freedom parameter is large. The diagnostics are illustrated with two examples.

Suggested Citation

  • Manuel Galea & Heleno Bolfarine & Filidor Vilcalabra, 2002. "Influence diagnostics for the structural errors-in-variables model under the Student-t distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1191-1204.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1191-1204
    DOI: 10.1080/0266476022000011265
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011265
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0266476022000011265?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. Berkane, Maia & Kano, Yutaka & Bentler, Peter M., 1994. "Pseudo maximum likelihood estimation in elliptical theory: Effects of misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 18(2), pages 255-267, September.
    2. Myung Geun Kim, 2000. "Outliers and influential observations in the structural errors-in-variables model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(4), pages 451-460.
    3. Fernández, C. & Steel, M.F.J., 1997. "Multivariate Student -t Regression Models : Pitfalls and Inference," Discussion Paper 1997-08, Tilburg University, Center for Economic Research.
    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. Filidor Vilca-Labra & Reiko Aoki & Camila Zeller, 2011. "Hypotheses testing for structural calibration model," Statistical Papers, Springer, vol. 52(3), pages 553-565, August.
    2. Vidal, Ignacio & Arellano-Valle, Reinaldo B., 2010. "Bayesian inference for dependent elliptical measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2587-2597, November.
    3. 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.
    4. Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 723-740.
    5. Yonghui Liu & Ruochen Sang & Shuangzhe Liu, 2017. "Diagnostic analysis for a vector autoregressive model under Student-super-′s t-distributions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(2), pages 86-114, May.
    6. Vidal, Ignacio & Iglesias, Pilar, 2008. "Comparison between a measurement error model and a linear model without measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 92-102, September.

    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. Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 723-740.
    2. David Cademartori & Cecilia Romo & Ricardo Campos & Manuel Galea, 2003. "Robust estimation of systematic risk using the t distribution in the chilean stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 10(7), pages 447-453.
    3. Vidal, Ignacio & Iglesias, Pilar, 2008. "Comparison between a measurement error model and a linear model without measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 92-102, September.
    4. Shaobo Jin & Fan Yang-Wallentin, 2017. "Asymptotic Robustness Study of the Polychoric Correlation Estimation," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 67-85, March.
    5. Michelli Barros & Manuel Galea & Víctor Leiva & Manoel Santos-Neto, 2018. "Generalized Tobit models: diagnostics and application in econometrics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 145-167, January.
    6. Yongjae Kwon & Hamparsum Bozdogan & Halima Bensmail, 2009. "Performance of Model Selection Criteria in Bayesian Threshold VAR (TVAR) Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 83-101.
    7. Popp, Bastian & Woratschek, Herbert, 2017. "Consumers’ relationships with brands and brand communities – The multifaceted roles of identification and satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 46-56.
    8. Leiva, Victor & Riquelme, Marco & Balakrishnan, N. & Sanhueza, Antonio, 2008. "Lifetime analysis based on the generalized Birnbaum-Saunders distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2079-2097, January.
    9. Zeller, Camila B. & Labra, Filidor V. & Lachos, Victor H. & Balakrishnan, N., 2010. "Influence analyses of skew-normal/independent linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1266-1280, May.
    10. Antonio Sanhueza & Víctor Leiva & N. Balakrishnan, 2008. "A new class of inverse Gaussian type distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(1), pages 31-49, June.
    11. Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October.
    12. Vidal, Ignacio & Arellano-Valle, Reinaldo B., 2010. "Bayesian inference for dependent elliptical measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2587-2597, November.
    13. Felipe Osorio & Manuel Galea, 2006. "Detection of a change-point in student-t linear regression models," Statistical Papers, Springer, vol. 47(1), pages 31-48, January.
    14. V. Lachos & T. Angolini & C. Abanto-Valle, 2011. "On estimation and local influence analysis for measurement errors models under heavy-tailed distributions," Statistical Papers, Springer, vol. 52(3), pages 567-590, August.
    15. 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.
    16. Jose T.A.S. Ferreira & Mark F.J. Steel, 2004. "Bayesian Multivariate Regression Analysis with a New Class of Skewed Distributions," Econometrics 0403001, University Library of Munich, Germany.
    17. Victor Lachos & Vicente Cancho & Reiko Aoki, 2010. "Bayesian analysis of skew-t multivariate null intercept measurement error model," Statistical Papers, Springer, vol. 51(3), pages 531-545, September.

    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:taf:japsta:v:29:y:2002:i:8:p:1191-1204. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.