TY - JOUR
T1 - On the asymptotic distribution of Matusita’s overlapping measure
AU - Taleb Suleiman Alodat, Moh'D
AU - Al Fayez, Moh’d
AU - Eidous, Omer
N1 - Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - In this paper, we study the asymptotic distribution of the plug-in kernel density estimator of the Matusita's overlapping measure. By utilizing the convergence of functional of stochastic processes, we show, under certain conditions, that the asymptotic distribution of the plug-in kernel density estimator (KDE) of Matusita's overlapping measure is normal distribution. Also, a small simulation study is conducted to support the theoretical finding of this paper. Furthermore, we apply our finding to a breast cancer data.
AB - In this paper, we study the asymptotic distribution of the plug-in kernel density estimator of the Matusita's overlapping measure. By utilizing the convergence of functional of stochastic processes, we show, under certain conditions, that the asymptotic distribution of the plug-in kernel density estimator (KDE) of Matusita's overlapping measure is normal distribution. Also, a small simulation study is conducted to support the theoretical finding of this paper. Furthermore, we apply our finding to a breast cancer data.
KW - Asymptotic distribution
KW - kernel density estimation
KW - limiting distribution
KW - Matusita's overlapping measure
KW - breast cancer
UR - https://doi.org/10.1080/03610926.2020.1869260
U2 - 10.1080/03610926.2020.1869260
DO - 10.1080/03610926.2020.1869260
M3 - Article
SN - 0361-0926
VL - 51
SP - 6963
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 20
M1 - 20
ER -