IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v50y2009i2p277-300.html
   My bibliography  Save this article

Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model

Author

Listed:
  • M. Menéndez
  • L. Pardo
  • M. Pardo

Abstract

No abstract is available for this item.

Suggested Citation

  • M. Menéndez & L. Pardo & M. Pardo, 2009. "Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model," Statistical Papers, Springer, vol. 50(2), pages 277-300, March.
  • Handle: RePEc:spr:stpapr:v:50:y:2009:i:2:p:277-300
    DOI: 10.1007/s00362-007-0078-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-007-0078-z
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00362-007-0078-z?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. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, 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. Abhik Ghosh & Ayanendranath Basu, 2017. "The minimum S-divergence estimator under continuous models: the Basu–Lindsay approach," Statistical Papers, Springer, vol. 58(2), pages 341-372, June.
    2. Qian Chen & David Giles, 2012. "Finite-sample properties of the maximum likelihood estimator for the binary logit model with random covariates," Statistical Papers, Springer, vol. 53(2), pages 409-426, May.

    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. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    2. Bambio, Yiriyibin & Bouayad Agha, Salima, 2018. "Land tenure security and investment: Does strength of land right really matter in rural Burkina Faso?," World Development, Elsevier, vol. 111(C), pages 130-147.
    3. Wei, Zheng & Kim, Daeyoung, 2021. "On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    4. Rasheed A. Adeyemi & Temesgen Zewotir & Shaun Ramroop, 2016. "Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria," IJERPH, MDPI, vol. 13(11), pages 1-22, November.
    5. M. Pardo, 2011. "Testing equality restrictions in generalized linear models for multinomial data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 231-253, March.
    6. Keunbaik Lee & Michael J. Daniels, 2007. "A Class of Markov Models for Longitudinal Ordinal Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1060-1067, December.
    7. Högberg, Hans & Svensson, Elisabeth, 2008. "An Overview of Methods in the Analysis of Dependent ordered catagorical Data: Assumptions and Implications," Working Papers 2008:7, Örebro University, School of Business.
    8. Maynou, Laia & Cairns, John, 2019. "What is driving HTA decision-making? Evidence from cancer drug reimbursement decisions from 6 European countries," Health Policy, Elsevier, vol. 123(2), pages 130-139.
    9. Pardo, L. & Pardo, M.C., 2008. "An extension of likelihood-ratio-test for testing linear hypotheses in the baseline-category logit model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1477-1489, January.
    10. Maynou, Laia & Cairns, John, 2018. "What is driving HTA decision-making? Evidence from cancer drug reimbursement decisions from 6 European countries," LSE Research Online Documents on Economics 90877, London School of Economics and Political Science, LSE Library.
    11. Xavier Bartoll & Joan Gil & Raul Ramos, 2018. "“Has the economic crisis worsened the work-related stress and mental health of temporary workers in Spain?”," AQR Working Papers 201808, University of Barcelona, Regional Quantitative Analysis Group, revised Oct 2018.
    12. Kędra, Arleta & Maleszyk, Piotr & Visvizi, Anna, 2023. "Engaging citizens in land use policy in the smart city context," Land Use Policy, Elsevier, vol. 129(C).
    13. Tatjana Miljkovic & Daniel Fernández, 2018. "On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio," Risks, MDPI, vol. 6(2), pages 1-18, May.
    14. Ivy Liu & Daniel Fernández, 2020. "Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow," Biometrics, The International Biometric Society, vol. 76(2), pages 564-568, June.
    15. Varin, Cristiano & Vidoni, Paolo, 2006. "Pairwise likelihood inference for ordinal categorical time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2365-2373, December.
    16. Jan Gertheiss & Gerhard Tutz, 2009. "Penalized Regression with Ordinal Predictors," International Statistical Review, International Statistical Institute, vol. 77(3), pages 345-365, December.
    17. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    18. Meltem Ucal & Simge Günay, 2022. "Household Happiness and Fuel Poverty: a Cross-Sectional Analysis on Turkey," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(1), pages 391-420, February.
    19. Fuks, Mauricio & Salazar, Esther, 2008. "Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil," Energy Economics, Elsevier, vol. 30(4), pages 1672-1692, July.
    20. Eleni Matechou & Ivy Liu & Daniel Fernández & Miguel Farias & Bergljot Gjelsvik, 2016. "Biclustering Models for Two-Mode Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 611-624, September.

    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:50:y:2009:i:2:p:277-300. 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.