Confidence intervals for a distribution function in the presence of auxiliary information

Computational Statistics and Data Analysis

Volumn 21, Issue 3, 1996, Pages 327-342

  Zhang, Biao ^a  

^a UNIVERSITY OF TOLEDO   (United States)

Author keywords

Asymptotic normality; Empirical likelihood; Weak consistency

Indexed keywords

ASYMPTOTIC STABILITY; COMPUTER SIMULATION; FUNCTIONS; MATHEMATICAL MODELS; PROBABILITY; RANDOM PROCESSES;

CONFIDENCE INTERVALS; EMPIRICAL LIKELIHOOD; POPULATION DISTRIBUTION FUNCTION; WEAK CONSISTENCY;

STATISTICAL METHODS;

EID: 0030110582     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-9473(95)00019-4     Document Type: Article

References (10)

1. Chen, J. and J. Qin, Empirical likelihood estimation for finite populations and the effective usage of auxiliary information, Biometrika, 80 (1993) 107-116.
2. DiCiccio, T. J., P. Hall and J.P. Romano, Empirical likelihood is Bartlett-correctable, Ann. Statist., 19 (1991) 1053-1061.
3. Haberman, S.J, Adjustment by minimum discriminant information, Ann. Statist., 12 (1984) 971-988.
4. Hall, P., Pseudo-likelihood theory for empirical likelihood, Ann. Statist., 18 (1990) 121-140.
5. Owen, A.B., Empirical likelihood ratio confidence intervals for a single functional, Biometrika, 75 (1988) 237-249.
6. Owen, A.B., Empirical likelihood confidence regions, Ann. Statist., 18 (1990) 90-120.
7. Owen, A.B., Empirical likelihood for linear models, Ann. Statist., 19 (1991) 1725-1747.
8. Qin, J. and J. Lawless, Empirical likelihood and general estimating equations, Ann. Statist., 22 (1994) 300-325.
9. Sheehy, A., Kullback-Leibler constrained estimation of probability measures, Technical Report 137 (Department of Statistics, University of Washington, 1988).
10. Zhang, B., M-estimation and quantile estimation in the presence of auxiliary information, J. Statist. Plann. Inference, 44 (1995) 77-94.