IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v70y2004i4p285-298.html
   My bibliography  Save this article

Goodness-of-fit inference for the Cox-Aalen additive-multiplicative regression model

Author

Listed:
  • Kraus, David

Abstract

The Cox-Aalen additive-multiplicative intensity model of Scheike and Zhang (Scand. J. Statist. 29, 2002) is considered. We study goodness-of-fit tests based on the stratified martingale residual process. Asymptotic distribution of the process is derived and the Kolmogorov-Smirnov type test is constructed. Several ways of overcoming the problem of complexity of the limiting distribution are discussed. The results are accompanied by a small Monte Carlo study.

Suggested Citation

  • Kraus, David, 2004. "Goodness-of-fit inference for the Cox-Aalen additive-multiplicative regression model," Statistics & Probability Letters, Elsevier, vol. 70(4), pages 285-298, December.
    • Handle: RePEc:eee:stapro:v:70:y:2004:i:4:p:285-298
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(04)00277-9
    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. Torben Martinussen, 2002. "A flexible additive multiplicative hazard model," Biometrika, Biometrika Trust, vol. 89(2), pages 283-298, June.
    2. Thomas H. Scheike & Mei‐Jie Zhang, 2002. "An Additive–Multiplicative Cox–Aalen Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 75-88, March.
    Full references (including those not matched with items on IDEAS)

    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. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    2. Christa Brelsford & Caterina De Bacco, 2018. "Are `Water Smart Landscapes' Contagious? An epidemic approach on networks to study peer effects," Papers 1801.10516, arXiv.org.
    3. Chin-Tsang Chiang & Mei-Cheng Wang, 2009. "Varying-coefficient model for the occurrence rate function of recurrent events," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 197-213, March.
    4. Zhuowei Sun & Hongyuan Cao & Li Chen, 2022. "Regression analysis of additive hazards model with sparse longitudinal covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 263-281, April.
    5. Xi Ning & Yinghao Pan & Yanqing Sun & Peter B. Gilbert, 2023. "A semiparametric Cox–Aalen transformation model with censored data," Biometrics, The International Biometric Society, vol. 79(4), pages 3111-3125, December.
    6. Giada Adelfio & Marcello Chiodi, 2021. "Including covariates in a space-time point process with application to seismicity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 947-971, September.
    7. Hao, Meiling & Zhao, Xingqiu & Xu, Wei, 2020. "Competing risk modeling and testing for X-chromosome genetic association," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    8. Jun Yan & Jian Huang, 2012. "Model Selection for Cox Models with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 68(2), pages 419-428, June.
    9. Marschner, Ian C. & Gillett, Alexandra C. & O’Connell, Rachel L., 2012. "Stratified additive Poisson models: Computational methods and applications in clinical epidemiology," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1115-1130.
    10. Thomas H. Scheike & Mei-Jie Zhang, 2003. "Extensions and Applications of the Cox-Aalen Survival Model," Biometrics, The International Biometric Society, vol. 59(4), pages 1036-1045, December.
    11. Qu, Lianqiang & Song, Xinyuan & Sun, Liuquan, 2018. "Identification of local sparsity and variable selection for varying coefficient additive hazards models," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 119-135.
    12. Peng He & Frank Eriksson & Thomas H. Scheike & Mei-Jie Zhang, 2016. "A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 103-122, March.
    13. Ruixuan Liu, 2020. "A competing risks model with time‐varying heterogeneity and simultaneous failure," Quantitative Economics, Econometric Society, vol. 11(2), pages 535-577, May.
    14. Audrey Boruvka & Richard J. Cook, 2015. "A Cox-Aalen Model for Interval-censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 414-426, June.
    15. Chin-Tsang Chiang, 2011. "A more flexible joint latent model for longitudinal and survival time data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 151-170, March.
    16. Christa Brelsford & Caterina Bacco, 2018. "Are ‘Water Smart Landscapes’ Contagious? An Epidemic Approach on Networks to Study Peer Effects," Networks and Spatial Economics, Springer, vol. 18(3), pages 577-613, September.
    17. Osman, Muhtarjan & Ghosh, Sujit K., 2012. "Nonparametric regression models for right-censored data using Bernstein polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 559-573.
    18. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    19. Lee, Unkyung & Sun, Yanqing & Scheike, Thomas H. & Gilbert, Peter B., 2018. "Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 59-79.
    20. Nils Lid Hjort & Emil Aas Stoltenberg, 2023. "The partly parametric and partly nonparametric additive risk model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 372-402, April.

    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:70:y:2004:i:4:p:285-298. 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.