Semiparametric estimation with clustered right censored data via multivariate gaussian random fields

Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the independence case, estimators of models unknowns are shown to be consistent and asymptotically normally distributed under infill-type spatial statistics asymptotic. Detailed small sample numerical studies that are in agreement with the theoretical results are provided in the independence case. In the dependence case, the estimators are shown to be inefficiency when the dependence is ignored. The foregoing procedures are applied to Leukemia survival data in Northeast England--Abstract, page iv.

Name thesis:degree_name

Ph. D. in Mathematics

Grantor

Missouri University of Science and Technology

Author dc:creator

• Sainul Abdeen, Fathima Zahra

• Clustered failure times
• Composite likelihood
• Estimating function
• Gaussian random fields
• Infill asymptoticSpatial correlation
• Statistics and Probability

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3174

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4179