Hazard Rate for Associated Data in Deconvolution Problems: Asymptotic Normality
In reliability theory and survival analysis, observed data are often weakly dependent and subject to additive measurement errors. Such contamination arises when the underlying data are neither independent nor strongly mixed but instead exhibit association. This paper focuses on estimating the hazard...
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| Format | Journal Article |
| Language | English |
| Published |
18.03.2025
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| Subjects | |
| Online Access | Get full text |
| DOI | 10.48550/arxiv.2503.14759 |
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| Summary: | In reliability theory and survival analysis, observed data are often weakly
dependent and subject to additive measurement errors. Such contamination arises
when the underlying data are neither independent nor strongly mixed but instead
exhibit association. This paper focuses on estimating the hazard rate by
deconvolving the density function and constructing an estimator of the
distribution function. We assume that the data originate from a strictly
stationary sequence satisfying association conditions. Under appropriate
smoothness assumptions on the error distribution, we establish the
quadratic-mean convergence and asymptotic normality of the proposed estimators.
The finite-sample performance of both the hazard rate and distribution function
estimators is evaluated through a simulation study. We conclude with a
discussion of open problems and potential future research directions. |
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| DOI: | 10.48550/arxiv.2503.14759 |