Autori: Abdul-Sathar, E. I., Viswakala, K.V.
Titolo: Non-parametric Kernel Estimation of Weighted Dynamic Cumulative Past Inaccuracy Measure Based on Censored Data
Periodico: Statistica
Anno: 2023 - Volume: 83 - Fascicolo: 4 - Pagina iniziale: 223 - Pagina finale: 245

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.




SICI: 0390-590X(2023)83:4<223:NKEOWD>2.0.ZU;2-S
Testo completo: https://rivista-statistica.unibo.it/article/view/19565/19491

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