Godambe (1960, 1985) and Hansen (1982) proposed the method of estimating function which makes a bridge between least squares estimator and maximum likelihood estimator. In this paper we apply the estimating function approach to CHARN models which include many well-known nonlinear time series models as special cases. The innovation density is permitted to be skew-symmetric. Since the estimation ...

The reputation of the maximum pseudolikelihood estimator (MPLE) for Exponential Random Graph Models (ERGM) has undergone a drastic change over past 30 years. While first receiving broad support, mainly due to its computational feasibility and lack alternatives, general opinions started with introduction approximate likelihood (MLE) methods that became practicable increasing computing power MCMC...

Based on the concept of a Lévy copula to describe the dependence structure of a multivariate Lévy process we present a new estimation procedure. We consider a parametric model for the marginal Lévy processes as well as for the Lévy copula and estimate the parameters by a two-step procedure. We first estimate the parameters of the marginal processes, and then estimate in a second step only the d...

Gaussian processes are a common analysis tool in statistics and uncertainty quantification. The covariance function of the process is generally unknown and often assumed to fall into some parameteric class. One of the scalability bottlenecks for the largescale usage of these processes is the computation of the maximum likelihood estimates of the parameters of the covariance matrix. In a classic...

In this paper, we first discuss the origin, developments and various thoughts by several researchers on the generalized linear regression estimator (GREG) due to Deville and Särndal [Deville, J.C., Särndal, C.E., 1992. Calibration estimators in survey sampling. J. Amer. Statist. Assoc. 87, 376–382]. Then, the problem of estimation of the general parameter of interest considered by Rao [Rao, J.N...