The purpose of the paper is two-fold for nonlinear mixed models(NLMM)without any distribution assumptions about random effects and random errors except for moment conditions.
Integer-valued generalized autoregressive conditional heteroscedasticity(GARCH)models have played an important role in time series analysis of count data.
Interval-censored failure time data occur in many fields such as demography,economics,medical research and reliability and many inference procedures on them have been developed(Chen et al.,2012; Sun,2
In this paper,we study the estimations of single index models with longitudinal data.Specifically,based on the Cholesky decomposition,we construct a single index mean-covariance model for longitudinal