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A composite likelihood approach for spatially correlated survival data

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  • Paik, Jane
  • Ying, Zhiliang

Abstract

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Suggested Citation

  • Paik, Jane & Ying, Zhiliang, 2012. "A composite likelihood approach for spatially correlated survival data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 209-216, January.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:1:p:209-216
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    References listed on IDEAS

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    1. Kuk, Anthony Y. C. & Nott, David J., 2000. "A pairwise likelihood approach to analyzing correlated binary data," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 329-335, May.
    2. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    3. S. le Cessie & J. C. van Houwelingen, 1994. "Logistic Regression for Correlated Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 95-108, March.
    4. Eric Bradlow & Bart Bronnenberg & Gary Russell & Neeraj Arora & David Bell & Sri Duvvuri & Frankel Hofstede & Catarina Sismeiro & Raphael Thomadsen & Sha Yang, 2005. "Spatial Models in Marketing," Marketing Letters, Springer, vol. 16(3), pages 267-278, December.
    5. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    6. Yi Li & Louise Ryan, 2002. "Modeling Spatial Survival Data Using Semiparametric Frailty Models," Biometrics, The International Biometric Society, vol. 58(2), pages 287-297, June.
    7. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    8. E. T. Parner, 2001. "A Composite Likelihood Approach to Multivariate Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 295-302, June.
    9. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
    10. Anthony Y. C. Kuk, 2007. "A Hybrid Pairwise Likelihood Method," Biometrika, Biometrika Trust, vol. 94(4), pages 939-952.
    11. Li, Yi & Lin, Xihong, 2006. "Semiparametric Normal Transformation Models for Spatially Correlated Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 591-603, June.
    12. Henderson R. & Shimakura S. & Gorst D., 2002. "Modeling Spatial Variation in Leukemia Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 965-972, December.
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    2. Andréas Heinen & James B. Kau & Donald C. Keenan & Mi Lim Kim, 2021. "Spatial Dependence in Subprime Mortgage Defaults," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 1-24, January.

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