AUTHORS: N. Glenn Griesinger, Andrea J. Shelton, Kiran Chilakamarri, Demetrios Kazakos

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ABSTRACT: Complex sample survey data are obtained through multistage sampling designs that involve clustering, stratification, and non–responce adjustments. Standard statistical methods such as empirical likelihood are typically not applicable to complex samples because independent, identically distributed observations seldom result from such data. Hence, we derive pseudo empirical likelihood confidence intervals for stratified single–stage and stratified multistage sampling designs. Use of such designs include national health data sets.

KEYWORDS: Complex sample, survey data, empirical likelihood.

REFERENCES:

[1] G. Casella & R. L. Berger, (2002), Statistical Inference, Duxbury.

[2] J. Chen, & R. R. Sitter (1999), A pseudo empirical likelihood approach to the effective use of auxiliary information in complex surveys, Statistica Sinica, 9, 385 – 406.

[3] H. O. Hartley and J. N. K. Rao (1968), A new estimation theory for sample survey, Biometrika, 55(3), 547 – 557.

[4] J. R. Landis, J. M. Lepkowski, S. A. Eklund, S. A. Stehouwer, (1982), a statistical methodology for analyzing data from a complex survey, the First National Health and Nutrition Examination Survey, National Center for Health Statistics, Vital Health Statistics, 2(92).

[5] D. J. Nordman, (2008), A blockwise empirical likelihood for spatial lattice data, Satistica Sinica 18: 1111 – 1129.

[6] A. Owen (1988), Empirical likelihood ratio confidence intervals for a single functional, Biometrika, 75, 237 – 49.

[7] A. Owen (2001), Empirical Likelihood, Chapman and Hall / CRC Press.

[8] C. Wu and J. N. K. Rao (2006), Pseudo– empirical likelihood ratio confidence intervals for complex surveys, The Canadian Journal of Statistics, 34(3), 359 – 375.