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A composite likelihood approach for spatially correlated survival data
1Department of Medicine, Stanford University, Stanford, CA 94305, United States.
Computational Statistics & Data Analysis
|November 14, 2013
Summary
This study introduces a composite likelihood method for analyzing spatially correlated survival data, particularly useful for understanding e-commerce purchasing behavior and spatial dependence. The approach models dependence using geographic and demographic factors, offering consistent and asymptotically normal estimators.
Area of Science:
- Statistics
- Spatial Analysis
- Survival Analysis
Background:
- Marketing research increasingly examines spatially clustered purchasing behavior.
- Assessing geographic distance as a metric for purchasing dependence is a key challenge.
- Existing methods may not fully capture spatial correlations in time-to-event data.
Purpose of the Study:
- To develop a composite likelihood approach for handling spatially correlated survival data.
- To model the dependence structure of time-to-event data influenced by spatial factors.
- To apply the methodology to e-commerce data for understanding purchasing behavior.
Main Methods:
- Utilized pairwise joint distributions for modeling spatial correlation.
- Employed the Farlie-Gumbel-Morgenstern (FGM) distribution to model dependence.
- Modeled the dependence parameter as a function of geographic and demographic pairwise distances.
- Developed pairwise composite likelihood equations for parameter estimation.
Main Results:
- The proposed composite likelihood approach effectively handles spatially correlated survival data.
- The FGM distribution and distance-based modeling provided insights into purchasing dependence.
- The derived estimators demonstrated consistency and asymptotic normality under spatial asymptotic theory.
Conclusions:
- The composite likelihood method offers a robust framework for analyzing spatially dependent survival data.
- This approach enhances the understanding of purchasing behavior in e-commerce contexts.
- The statistical properties of the estimators support the reliability of the methodology.
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