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ESTIMATION IN A SEMI-PARAMETRIC TWO-STAGE RENEWAL REGRESSION MODEL.

Dorota M Dabrowska¹

¹University of California, Los Angeles.

Statistica Sinica

|January 5, 2010

Summary

This study introduces a two-stage modulated renewal process for paired data analysis. It demonstrates the consistency and asymptotic normality of parameter estimates using U-process methods.

Area of Science:

Statistics
Biostatistics
Survival Analysis

Background:

Paired data analysis presents unique statistical challenges.
Renewal processes are fundamental in modeling event occurrences over time.
Modulated renewal processes offer a flexible framework for complex event data.

Purpose of the Study:

To develop and evaluate a two-stage modulated renewal process for paired data.
To estimate parameters in models with proportional hazard intensities.
To establish the statistical properties of the proposed estimation methods.

Main Methods:

Utilized a two-stage modulated renewal process framework.
Employed proportional hazard intensity models.
Applied U-process methods for theoretical analysis.

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Main Results:

Demonstrated consistency of parameter estimates.
Proved asymptotic normality of parameter estimates.
Validated the applicability of U-process methods in this context.

Conclusions:

The proposed two-stage modulated renewal process is a viable method for paired data.
U-process theory provides a robust foundation for the statistical inference.
The estimation procedure yields reliable and statistically sound results.