Two-stage orthogonality based estimation for semiparametric varying-coefficient models and its applications in analyzing AIDS data.

Semiparametric smoothing methods are usually used to model longitudinal data, and the interest is to improve efficiency for regression coefficients. This paper is concerned with the estimation in semiparametric varying-coefficient models (SVCMs) for longitudinal data. By the orthogonal projection method, local linear technique, quasi-score estimation, and quasi-maximum likelihood estimation, we propose a two-stage orthogonality-based method to estimate parameter vector, coefficient function vector, and covariance function. The developed procedures can be implemented separately and the resulting estimators do not affect each other. Under some mild conditions, asymptotic properties of the resulting estimators are established explicitly. In particular, the asymptotic behavior of the estimator of coefficient function vector at the boundaries is examined. Further, the finite sample performance of the proposed procedures is assessed by Monte Carlo simulation experiments. Finally, the proposed methodology is illustrated with an analysis of an acquired immune deficiency syndrome (AIDS) dataset.

Characterization of 11 new microsatellite loci in taro (Colocasia esculenta).

Eleven new microsatellite markers were isolated from taro, Colocasia esculenta (L.) Schott, a root crop widely distributed all over the world. Forty-eight primer pairs were designed from a microsatellite-enriched genomic library, of which 11 primer pairs have polymorphisms in 30 individuals tested from a population in China, which revealed two to six alleles per locus with the observed and expected heterozygosity levels ranging from 0 to 0.733 and from 0.381 to 0.731, respectively. These new genetic markers will be useful for the study of taro germplasm management and population evolution in the future.