Titlebook: Quasi-Likelihood And Its Application; A General Approach t Christopher C. Heyde Book 1997 Springer Science+Business Media New York 1997 Est

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An Alternative Approach: E-Sufficiency,ntial approach to parameter inference in cases where they are available. To adapt these basic concepts to the context of estimating functions, Small and McLeish developed the expectation based concepts of E-sufficiency, E-ancillarity, local E-sufficiency and local E-ancillarity. The basic idea is th

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Asymptotic Quasi-Likelihood,erion holds for either fixed . or for each . as . → ∞. Here we address the situation where the criteria for optimality are not satisfied exactly but hold in a certain asymptotic sense to be made precise below. These considerations give rise to an equivalence class of asymptotic quasi-likelihood esti

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Projected Quasi-Likelihood,ditioned on an ancillary statistic have received much attention. This discussion requires full specification of distributions and is consequently not directly available in quasi-likelihood context. However, conducting inference conditionally on a statistic . is equivalent to projecting onto the subs

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Infinite Dimensional Problems, in the neuronal membrane potential model . discussed earlier in Section 2.6 with parameters held constant, if changes in θ(t) over the recording interval 0 ≤ . ≤ . are important and the function needs to be estimated. Both the situation where . replica trajectories of {., 0 ≤ . ≤ . are observed and

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Miscellaneous Applications,godic theorem and involves no significant structural assumptions. Nevertheless, in various circumstances, such as for estimating the mean of a stationary process, it turns out that the method of moments (which estimates . = . by . produces an estimator that has the same asymptotic variance as the be

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Complements and Strategies for Application,pecial circumstances. The focus of the theory has usually been on polynomial functions of the data and these will not always provide ideal families of estimating functions. It must be remembered that the general theory requires estimating functions which are at least square integrable and if the und