Non-parametric Kernel Estimation of Weighted Dynamic Cumulative Past Inaccuracy Measure Based on Censored Data

2025/04/09 by Viswakala, K.V., Abdul Sathar, E.I.
#Alpha-mixing #Information measures #Recursive kernel density estimator #Right-censored data #Weighted dynamic cumulative past inaccuracy measure

paper · doi:10.6092/issn.1973-2201/19565

Abstract

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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