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EMPIRICAL LIKELIHOOD CONFIDENCE INTERVALS FOR DEPENDENT DURATION DATA

Published online by Cambridge University Press:  30 April 2010

Anouar El Ghouch
Affiliation:
Université catholique de Louvain
Ingrid Van Keilegom*
Affiliation:
Université catholique de Louvain
Ian W. McKeague
Affiliation:
Columbia University
*
*Address correspondence to Ingrid Van Keilegom, Institute of Statistics, Université catholique de Louvain, Voie du Roman Pays 20, 1348 Louvain-la-Neuve, Belgium; e-mail: Ingrid.Vankeilegom@uclouvain.be.

Abstract

Three types of confidence intervals are developed for a general class of functionals of a survival distribution based on censored dependent data. The confidence intervals are constructed via asymptotic normality (Wald’s method), the empirical likelihood (EL) method, and the blockwise EL method in which sample means over blocks of observations are used in place of the original data. Asymptotic results are derived to accurately calibrate the various procedures, and their performance is evaluated in a simulation study. The problem of the choice of the block size is also discussed.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Arcones, M. (1995) On the central limit theorem for U-statistics under absolute regularity. Statistics and Probability Letters 24, 245249.CrossRefGoogle Scholar
Arcones, M. (1998) The law of large numbers for U-statistics under absolute regularity. Electronic Communications in Probability 3, 1319.CrossRefGoogle Scholar
Bijwaard, G. (2004) Dynamic economic aspects of migration. Medium Econometrische Toepassingen, 2630.Google Scholar
Bougerol, P. & Picard, N. (1992) Strict stationarity of generalized autoregressive processes. Annals of Probability 20, 17141730.CrossRefGoogle Scholar
Bradley, R. (1986) Basic properties of strong mixing conditions. In Eberlein, E. & Taqqu, M.S. (eds.), Dependence in Probability and Statistics: A Survey of Recent Results, pp. 165192. Birkhäuser.CrossRefGoogle Scholar
Brooks, R., Faff, R., & Fry, T. (2001) Garch modelling of individual stock data: The impact of censoring, firm size and trading volume, institutions and money. Journal of International Financial Markets, Institutions and Money 11, 215222.CrossRefGoogle Scholar
Cai, Z. (2001) Estimating a distribution function for censored time series data. Journal of Multivariate Analysis 78, 299318.CrossRefGoogle Scholar
Cai, Z. & Roussas, G. (1992) Uniform strong estimation under α-mixing, with rates. Statistics and Probability Letters 15, 4755.CrossRefGoogle Scholar
Chen, S., Dahl, G., & Khan, S. (2005) Nonparametric identification and estimation of a censored location-scale regression model. Journal of the American Statistical Association 100, 212221.CrossRefGoogle Scholar
Doukhan, P. (1994) Mixing: Properties and Examples. Lecture Notes in Statistics. Springer-Verlag.CrossRefGoogle Scholar
Eriksson, B. & Adell, R. (1994) On the analysis of life tables for dependent observations. Statistics in Medicine 13, 4351.CrossRefGoogle ScholarPubMed
Franses, P. & Paap, R. (2002) Censored latent effects autoregression, with an application to US unemployment. Journal of Applied Econometrics 17, 347366.CrossRefGoogle Scholar
Gijbels, I. & Veraverbeke, N. (1991) Almost sure asymptotic representation for a class of functionals of the Kaplan-Meier estimator. Annals of Statistics 19, 14571470.CrossRefGoogle Scholar
Hall, P. & La Scala, B. (1990) Methodology and algorithms of empirical likelihood. International Statistical Review 58, 109127.CrossRefGoogle Scholar
Hoel, P., Port, S., & Stone, C. (1971) Introduction to Probability Theory. Houghton Mifflin.Google Scholar
Kitamura, Y. (1997) Empirical likelihood methods with weakly dependent processes. Annals of Statistics 25, 20842102.CrossRefGoogle Scholar
Künsch, H. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17, 12171241.CrossRefGoogle Scholar
Liu, R. & Singh, K. (1992) Moving blocks jackknife and bootstrap capture weak dependence. In Lepage, R. & Billard, L. (eds.), Exploring the Limits of Bootstrap, pp. 225248. Wiley.Google Scholar
Newey, W. & West, K. (1987) A simple positive definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703705.CrossRefGoogle Scholar
Owen, A. (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75, 237249.CrossRefGoogle Scholar
Owen, A. (2001) Empirical Likelihood. Chapman and Hall/CRC.Google Scholar
Pham, T. & Tran, L. (1985) Some mixing properties of time series models. Stochastic Processes and their Applications 19, 297303.CrossRefGoogle Scholar
Politis, D., Romano, J., & Wolf, M. (1997) Subsampling for heteroskedastic time series. Journal of Econometrics 81, 281317.CrossRefGoogle Scholar
Politis, D. & White, H. (2004) Automatic block-length selection for the dependent bootstrap. Econometric Reviews 23, 5370.CrossRefGoogle Scholar
Radulović, D. (1996) The bootstrap of the mean for strong mixing sequences under minimal conditions. Statistics and Probability Letters 28, 6572.CrossRefGoogle Scholar
Rio, E. (2000) Théorie Asymptotique des Processus Aléatoires Faiblement Dépendants. Springer-Verlag.Google Scholar
Shorack, G. & Wellner, J. (1986) Empirical Processes with Applications to Statistics. Wiley.Google Scholar
Stute, W. (1995) The central limit theorem under random censorship. Annals of Statistics 23, 422439.CrossRefGoogle Scholar
Stute, W. (1996) The jackknife estimate of variance of a Kaplan-Meier integral. Annals of Statistics 24, 26792704.CrossRefGoogle Scholar
Stute, W. & Wang, J.-L. (1993) The strong law under random censorship. Annals of Statistics 21, 15911607.CrossRefGoogle Scholar
Thomas, D. & Grunkemeier, G. (1975) Confidence interval estimation of survival probabilities for censored data. Journal of the American Statistical Association 70, 865871.CrossRefGoogle Scholar
Wang, Q. & Jing, B. (2001) Empirical likelihood for a class of functionals of survival distribution with censored data. Annals of the Institute of Statistical Mathematics 53, 517527.CrossRefGoogle Scholar
Ying, Z. & Wei, L. (1994) The Kaplan-Meier estimate for dependent failure time observations. Journal of Multivariate Analysis 50, 1729.CrossRefGoogle Scholar
Zvingelis, J. (2001) On bootstrap coverage probability with dependent data. In Giles, D.E.A. (ed.), Computer-Aided Econometrics. Marcel Dekker.Google Scholar