Non-parametric Kernel Estimation of Weighted Dynamic Cumulative Past Inaccuracy Measure Based on Censored Data
The inaccuracy measure has recently become a valuable tool for detecting errors in experimental data. This measure applies only when random variables have density functions. To circumvent this constraint, the cumulative inaccuracy measure is a commonly used alternative measure of inaccuracy in the l...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Bologna
2025-04-01
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| Series: | Statistica |
| Subjects: | |
| Online Access: | https://rivista-statistica.unibo.it/article/view/19565 |
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| Summary: | The inaccuracy measure has recently become a valuable tool for detecting errors in experimental data. This measure applies only when random variables have density functions. To circumvent this constraint, the cumulative inaccuracy measure is a commonly used alternative measure of inaccuracy in the literature. When the observations generated by a stochastic process are recorded using a weight function, weighted distributions are established. Based on right-censored dependent data, we provide a nonparametric estimate for the weighted dynamic cumulative past inaccuracy measure in this study. The proposed estimator’s asymptotic characteristics have been examined, and its performance demonstrated through simulated and real-world data sets. |
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| ISSN: | 0390-590X 1973-2201 |