Asymptotic Normality of the Nonparametric Kernel Estimation of the Conditional Hazard Function for Left-Truncated and Dependent Data

Annals of Applied Mathematics
Vol. 34 No. 4 (2018), pp. 395-406
Preview Buy Article Full text requires purchase or subscription
Author(s)
, ,
College of Math. and Computer Science, Fuzhou University, Fuzhou Fujian, PR China
Abstract

Under some mild conditions, we derive the asymptotic normality of the Nadaraya-Watson and local linear estimators of the conditional hazard function for left-truncated and dependent data. The estimators were proposed by Liang and Ould-Saïd [1]. The results confirm the guess in Liang and Ould-Saïd [1].

Keywords
asymptotic normality Nadaraya-Watson estimation local linear estimation conditional hazard function left-truncated data.
More from this issue
Some Problems of Nonlinear Partial Differential Equations in Field Theory
Guo et al.
pp. 331-344
Stability and Boundedness of Solutions for a Certain Fourth-Order Delay Differential Equation
Cai et al.
pp. 345-357
A Lotka-Volterra Prey-Predator System with Feedback Control Effect
Chen
pp. 358-363
Stability Analysis of a Lotka-Volterra Commensal Symbiosis Model Involving Allee Effect
Guan
pp. 364-375
Stay updated
Share
How to Cite
RIS BibTeX