RT Journal Article
T1 Weighted estimation of conditional mean function with truncated, censored and dependent data
A1 Liang, Han-Ying
A1 Iglesias Pérez, Maria Carmen
K1 1209 Estadística
AB By applying the empirical likelihood method, we construct a new weighted estimator of the conditional mean function for a left-truncated and right-censored model. Assuming that the observations form a stationary α-mixing sequence, we derive weak convergence with a certain rate and prove asymptotic normality of the weighted estimator. The asymptotic normality shows that the weighted estimator preserves the bias, variance, and, more importantly, automatic good boundary behavior of a local linear estimator of the conditional mean function. Also, a Berry-Esseen type bound for the weighted estimator is established. A simulation study is conducted to study the finite sample behavior of the new estimator and a real data application is provided.
PB Statistics
SN 02331888
YR 2018
FD 2018-08-09
LK http://hdl.handle.net/11093/1216
UL http://hdl.handle.net/11093/1216
LA eng
NO Statistics, 52(6): 1249-1269 (2018)
NO National Natural Science Foundation of China | Ref. 11671299
DS Investigo
RD 19-sep-2024