A generalised quasi-likelihood estimation

William, Martin L.; Durairajan, T.M.

doi:10.1016/s0378-3758(98)00227-4

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…This notation is used throughout the paper. For different derivations of the same set of EF we refer to C rowder (1987), L in and H eyde (1997), and W illiam and D urairajan (1999). In the latter citation, the EF in are achieved assuming that V t , γ 1, t and γ 2, t are non‐stochastic functions of θ , σ 2 and c .…”

Section: Assumptions and Notation For Optimal Estimating Functionsmentioning

confidence: 99%

The estimation of a state space model by estimating functions with an application

Papanastassiou

Ioannides

2004

Statistica Neerlandica

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Conventionally the parameters of a linear state space model are estimated by maximizing a Gaussian likelihood function, even when the input errors are not Gaussian. In this paper we propose estimation by estimating functions fulfilling Godambe's optimality criterion. We discuss the issue of an unknown starting state vector, and we also develop recursive relations for the third-and fourth-order moments of the state predictors required for the calculations. We conclude with a simulation study demonstrating the proposed procedure on the estimation of the stochastic volatility model. The results suggest that the new estimators outperform the Gaussian likelihood.

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Section: Assumptions and Notation For Optimal Estimating Functionsmentioning

confidence: 99%

The estimation of a state space model by estimating functions with an application

Papanastassiou

Ioannides

2004

Statistica Neerlandica

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Optimal estimating function for estimation and prediction in semi-parametric models

Durairajan¹,

William²

2008

Journal of Statistical Planning and Inference

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Schwarz Methods for Quasi-Likelihood in Generalized Linear Models

Guo¹

2008

Communications in Statistics - Simulation and Computation

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The topic is maximum likelihood estimation in generalized linear models by an approximation using a sequence of sub-models, referred to here as blocks. More broadly, the topic is the solution of a non-linear system of equations H = S of the type that arises in quasi-likelihood estimation, to be solved for ∈ R m . The Schwarz methods use a sequence of sub-models, each sub-model corresponding to a subset of the components of , the sub-models being patched together to yield the solution for the full model. The technique might be useful for model comparison, where the fitted values from a sub-model are used as starting values for a larger model. And we explore the use of the multiplicative Schwarz method and the damped additive Schwarz method for generalized linear model with large sample sizes. It is shown that the convergence of the proposed iterative procedures is guaranteed for canonical link functions and uncanonical link functions.

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A generalised quasi-likelihood estimation

Cited by 3 publications

References 5 publications

The estimation of a state space model by estimating functions with an application

The estimation of a state space model by estimating functions with an application

Optimal estimating function for estimation and prediction in semi-parametric models

Schwarz Methods for Quasi-Likelihood in Generalized Linear Models

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