@article{lu_chen_gan_2002, title={Semi-parametric modelling and likelihood estimation with estimating equations}, volume={44}, ISSN={["1369-1473"]}, DOI={10.1111/1467-842X.00222}, abstractNote={This paper proposes a semi‐parametric modelling and estimating method for analysing censored survival data. The proposed method uses the empirical likelihood function to describe the information in data, and formulates estimating equations to incorporate knowledge of the underlying distribution and regression structure. The method is more flexible than the traditional methods such as the parametric maximum likelihood estimation (MLE), Cox’s (1972) proportional hazards model, accelerated life test model, quasi‐likelihood (Wedderburn, 1974) and generalized estimating equations (Liang & Zeger, 1986). This paper shows the existence and uniqueness of the proposed semi‐parametric maximum likelihood estimates (SMLE) with estimating equations. The method is validated with known cases studied in the literature. Several finite sample simulation and large sample efficiency studies indicate that when the sample size is larger than 100 the SMLE is compatible with the parametric MLE; and in all case studies, the SMLE is about 15% better than the parametric MLE with a mis‐specified underlying distribution.}, number={2}, journal={AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS}, author={Lu, JC and Chen, D and Gan, NC}, year={2002}, month={Jun}, pages={193–212} }
 @article{thomas_gan_1998, title={Generating multiple imputations for matrix sampling data analyzed with item response models}, volume={22}, DOI={10.3102/10769986022004425}, abstractNote={ Sample survey designs in which each participant is administered a subset of the items contained in a complete survey instrument are becoming an increasingly popular method of reducing respondent burden ( Mislevy, Beaton, Kaplan, Sheehan 1992 ; Raghunathan & Grizzle, 1995 ; Wacholder, Carroll, Pee, Gail 1994 ). Data from these survey designs can be analyzed using multiple imputation methodology that generates several imputed values for the missing data and thus yields several complete data sets. These data sets are then analyzed using complete data estimators and their standard errors ( Rubin, 1987b ). Generating the imputed data sets, however, can be very difficult. We describe improvements to the methods currently used to generate the imputed data sets for item response models summarizing educational data collected by the National Assessment of Educational Progress (NAEP), an ongoing collection of samples of 4th, 8th, and 12th grade students in the United States. The improved approximations produce small to moderate changes in commonly reported estimates, with the larger changes associated with an increasing amount of missing data. The improved approximations produce larger standard errors. }, number={4}, journal={Journal of Educational and Behavioral Statistics}, author={Thomas, N. and Gan, N.}, year={1998}, pages={425–445} }