Results 251 to 260 of about 1,338,681 (301)

A generalised quasi-likelihood estimation

Journal of Statistical Planning and Inference, 1999
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William, Martin L., Durairajan, T. M.
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On constrained quasi-likelihood estimation

Biometrika, 1993
Summary: For maximum likelihood or least squares parameter estimation subject to a constrained parameter, the standard approach is to use the method of Lagrange multipliers. In this paper it is shown that the same formal procedure applies very generally for constrained quasi-likelihood estimation even though there is ordinarily no objective function to
Heyde, C. C., Morton, R.
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On the efficiency of quasi-likelihood estimation

Biometrika, 1987
The introduction of quasi-likelihood for general linear models by \textit{R. W. M. Wedderburn} [Biometrika 61, 439-447 (1974; Zbl 0292.62050)] greatly widened their scope by replacing the full distributional assumption about the random component by the existence of the first and second moments.
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Profile quasi-likelihood

Statistics & Probability Letters, 2002
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Lin, Lu, Zhang, Runchu
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COMPARISON OF QUASI‐LIKELIHOOD MODELS FOR OVERDISPERSION

Australian Journal of Statistics, 1995
SummaryMethods for modelling overdispersed data are compared. These methods are considered to be of two kinds: a likelihood based approach and a method‐of‐moments based approach. The likelihood method facilitates computation of maximum likelihood estimates which can be obtained through the same algorithm as that of weighted least squares.
Yanez, N. David III, Wilson, Jeffrey R.
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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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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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