Scandinavian Journal of Statistics· 2026Q2
Nonparametric Survival Estimation With Contaminated and Adjudicated Events
- 0citations
- Q2SCImago
- 2026year
Short summary
A new conditional expert Kaplan-Meier estimator is developed to handle time-to-event data with both right-censoring and contamination from uncertain events, common in finance and insurance.
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Key points
- Developed a conditional expert Kaplan-Meier estimator for time-to-event data with right-censoring and contamination.
- Established asymptotic theory, including functional consistency and weak convergence, for the new estimator.
- Quantified the bias introduced by imperfect expert judgments, showing unbiased judgments ensure consistency.
- Demonstrated practical application using loan default data.
AI-generated from the title and abstract; the full text is not read.
Abstract
ABSTRACT We study the conditional expert Kaplan–Meier estimator, an extension of the classical Kaplan–Meier estimator designed for time‐to‐event data subject to both right‐censoring and contamination. Such contamination, where observed events may not reflect true outcomes, is common in applied settings, including insurance and credit risk, where expert opinion is often used to adjudicate uncertain events. Building on previous work, we develop a comprehensive asymptotic theory for the conditional version incorporating covariates through kernel smoothing. We establish functional consistency and weak convergence under suitable regularity conditions and quantify the bias induced by imperfect expert information. The results show that unbiased expert judgments ensure consistency, while systematic deviations lead to a deterministic asymptotic bias that can be explicitly characterized. We examine finite‐sample properties through simulation studies and illustrate the practical use of the estimator with an application to loan default data.
The authors' abstract, as published at the source. Scandinavian Journal of Statistics, 2026 · DOI ↗
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FinanceEconomics, Econometrics and Finance