The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate parameter estimation in discretely observed stochastic differential equations, where the observations are corrupted by additive white noise. The new class of estimating functions has most of the nice properties of martingale estimating functions. However, they may be applied when no obvious or e...

this article is devoted to study of the autoconvolution equations and generalized mittag-leffler functions. these types of equations are given in terms of the laplace transform convolution of a function with itself. we state new classes of the autoconvolution equations of the first kind and show that the generalized mittag-leffler functions are solutions of these types of equations. in view of ...

This article proposes an estimation approach for panel models with mixed continuous and ordered categorical outcomes based on generalized estimating equations for the mean and pseudo-score equations for the covariance parameters. A numerical study suggests that efficiency can be gained as concerns the mean parameter estimators by using individual covariance matrices in the estimating equations ...

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they redu...