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Local likelihood of quantile difference under left-truncated, right-censored and dependent assumptions.
- Source :
- Statistics; Feb2023, Vol. 57 Issue 1, p71-93, 23p
- Publication Year :
- 2023
-
Abstract
- We, in this paper, focus on the inference of conditional quantile difference (CQD) for left-truncated and right-censored model. Based on local conditional likelihood function of the observed data, local likelihood ratio function and smoothed local log-likelihood ratio (log-SLL) of the CQD are constructed, and the maximum local likelihood estimator of the CQD is further defined from the log-SLL. When the observations are assumed to be a sequence of stationary α-mixing random variables, we establish asymptotic normality of the defined estimator, and prove the Wilks' theorem of adjusted log-SLL. Besides, we define another estimator of the CQD based on product-limit estimator of conditional distribution function and give its asymptotic normality. Also, simulation study and real data analysis are conducted to investigate the finite sample behaviour of the proposed methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02331888
- Volume :
- 57
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 161896248
- Full Text :
- https://doi.org/10.1080/02331888.2022.2161547