期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 43, 期 10, 页码 2432-2443出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2012.752839
关键词
Correlated data; Efficiency; Empirical covariance; Generalized estimating equations; Optimal estimating equations
The quadratic inference function (QIF) method is increasingly popular for the marginal analysis of correlated data due to its advantages over generalized estimating equations. Asymptotic theory is used to derive analytical results from the QIF, and we, therefore, study three asymptotically equivalent weighting matrices in terms of finite-sample parameter estimation. Furthermore, to improve small-sample estimation, we study modifications to the estimation procedure. Examples are presented via simulations and application. Results show that although theoretical weighting matrices work best, the proposed estimation procedure, in which initial estimates are held constant within the matrix of estimated empirical covariances, is preferable in practice.
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