The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strenght of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite samp...

in this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate ar(1) is investigated. a closed formula for typical elements of the godambe information(sandwich information) is presented. some efficiency calculations are also given to discuss the feasibility andcomputational advantages of the weighted pairwise likelihood app...

In this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate AR(1) is investigated. A closed formula for typical elements of the Godambe information (sandwich information) is presented. Some efficiency calculations are also given to discuss the feasibility and computational advantages of the weighted pairwise likelihood a...

We consider the effective sample size, based on Godambe information, for block likelihood inference which is an attractive and computationally feasible alternative to full large correlated datasets. With reference a Gaussian random field having constant mean, we explore how choice of blocks impacts this size. This done by introducing column-wise blocking method spreads out spatial points within...

This paper explains why Godambe-Durbin \estimating functions" (EFs) from 1960 are worthy of attention in econometrics. Godambe and Kale (1991) show the failures of Gauss-Markov and least squares and prove the small-sample superiority of EFs. There are many areas of Econometrics including unit root estimation, generalized method of moments (GMM), panel data models, etc., which can use some simpl...

In this paper, generalized quasi-likelihood estimation for a Poisson Process model with a dependent structure is developed incorporating knowledge of skewness and kurtosis. This is a generalization of a similar idea proposed for independent observations by Godambe and Thompson (1989). The generalized quasi-score function is constructed on the basis of non-orthogonal estimating function invoking...

The Takacs–Fiksel method is a general approach to estimate the parameters of a spatial Gibbs point process. This method embraces standard procedures such as the pseudolikelihood and is defined via weight functions. In this paper we propose a general procedure to find weight functions which reduce the Godambe information and thus outperform pseudolikelihood in certain situations. The new procedu...

This paper extends a result of Godambe on parametric estimation for discrete time stochastic processes to nonparametric estimation for the continuous time case. Following Hasminskii and Ibragimov (1980), the nonparametric problem is formulated as a parametric one but with infinite dimensional parameter. Let { P } be a family of probability measures such that (D,F,P) is complete , (Ft,t~O) is a ...

We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the deleted jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic rep...