Non-parametric Kernel Estimation of Weighted Dynamic Cumulative Past Inaccuracy Measure Based on Censored Data
DOI:
https://doi.org/10.6092/issn.1973-2201/19565Keywords:
Alpha-mixing, Information measures, Recursive kernel density estimator, Right-censored data, Weighted dynamic cumulative past inaccuracy measureAbstract
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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