Results 241 to 250 of about 57,825 (288)

An extended quasi-likelihood function

Biometrika, 1987
The introduction by \textit{R. W. M. Wedderburn} [Biometrika 61, 439-447 (1974; Zbl 0292.62050)] of quasi-likelihood for general linear models greatly widened their scope by allowing the full distributional assumption about the random component in the models to be replaced by a much weaker assumption in which only the first and second moments were ...
Nelder, J. A., Pregibon, D.
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Quasi-Likelihood Methods

2017
In this chapter, quasi-likelihood methods are shown. If the random component of a GLM is specified then the likelihood function can be used and the role of maximum likelihood method for estimating parameters of a model is well established. In GLM, the response or outcome variable follows a specific probability distribution under the family of ...
M. Ataharul Islam, Rafiqul I. Chowdhury
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Generalized quasi-likelihood

Statistical Papers, 2004
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Quasi-likelihood Estimation in Semiparametric Models

Journal of the American Statistical Association, 1994
Abstract Suppose the expected value of a response variable Y may be written h(Xβ +γ(T)) where X and T are covariates, each of which may be vector-valued, β is an unknown parameter vector, γ is an unknown smooth function, and h is a known function. In this article, we outline a method for estimating the parameter β, γ of this type of semiparametric ...
Thomas A. Severini, Joan G. Staniswalis
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Quasi-likelihood or extended quasi-likelihood? An information-geometric approach

Annals of the Institute of Statistical Mathematics, 1995
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Quasi-likelihood for Multiplicative Random Effects

Biometrika, 1991
SUMMARY A quasi-likelihood approach similar to that described by Morton (1987) is found to be particularly simple in the case of measurements subject to two or more sources of multiplicative error. Still further simplification occurs when the effects of interest also are multiplicative.
D. FIRTH, I. R. HARRIS
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Asymptotic Properties of the Maximum Quasi-Likelihood Estimator in Quasi-Likelihood Nonlinear Models

Communications in Statistics - Theory and Methods, 2008
Quasi-likelihood nonlinear models (QLNM) are a further extension of generalized linear models by only specifying the expectation and variance functions of the response variable. In this article, some mild regularity conditions are proposed. These regularity conditions, respectively, assure the existence, strong consistency, and the asymptotic normality
Tian Xia, Fanchao Kong, Shunfang Wang, Xueren Wang   +3 more
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Boosting local quasi-likelihood estimators

Annals of the Institute of Statistical Mathematics, 2008
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Ueki, Masao, Fueda, Kaoru
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Likelihood and Quasi-Likelihood

2009
In this section the local maximum likelihood approach is introduced as a generalization of the linear LPA. It provides universal tools for designing methods and algorithms for a variety of stochastic models that are different from the standard Gaussian one.
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Quasi-Likelihood and GLIM

1985
Quasi-likelihood allows GLMs to be specified by use of the link function and variance function only, or equivalently by specifying the first two moments of the error distribution. Wedderburn’s original definition is extended to allow the comparison of different variance functions, and several uses of quasi-likelihood in extending the range of GLMs are ...
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