In some long term studies, a series of dependent and possibly
truncated lifetime data may be observed. Suppose that the lifetimes
have a common continuous distribution function F. A popular stochastic
measure of the distance between the density function f of the lifetimes
and its kernel estimate fn is the integrated square error (ISE). In this
paper, we derive a central limit theorem for the integrated square error
of the kernel density estimators in the left-truncation model. It is
assumed that the lifetime observations form a stationary strong mixing
sequence. A central limit theorem (CLT) for the ISE of the kernel hazard
rate estimators is also presented.
Fakoor,V , Jomhoori,S and Ganjeali,A . (2022). Density Estimators for Truncated Dependent Data. Journal of the Iranian Statistical Society, 10(1), 45-61.
MLA
Fakoor,V , , Jomhoori,S , and Ganjeali,A . "Density Estimators for Truncated Dependent Data", Journal of the Iranian Statistical Society, 10, 1, 2022, 45-61.
HARVARD
Fakoor V, Jomhoori S, Ganjeali A. (2022). 'Density Estimators for Truncated Dependent Data', Journal of the Iranian Statistical Society, 10(1), pp. 45-61.
CHICAGO
V Fakoor, S Jomhoori and A Ganjeali, "Density Estimators for Truncated Dependent Data," Journal of the Iranian Statistical Society, 10 1 (2022): 45-61,
VANCOUVER
Fakoor V, Jomhoori S, Ganjeali A. Density Estimators for Truncated Dependent Data. JIRSS. 2022;10(1):45-61.
Journal of the Iranian Statistical Society