A - Papers appearing in refereed journals
Pan, J. and Thompson, R. 2003. Gauss-Hermite quadrature approximation for estimation in generalised linear mixed models. Computational Statistics. 18 (1), pp. 57-78.
|Authors||Pan, J. and Thompson, R.|
This paper provides a unified algorithm to explicitly calculate the maximum likelihood estimates of parameters in a general setting of generalised linear mixed models (GLMMs) in terms of Gauss-Hermite quadration approximation. The score function and observed information matrix are expressed explicitly as analytically closed forms so that Newton-Raphson algorithm can be applied straightforwardly. Compared with some existing methods, this approach can produce more accurate estimates of the fixed effects and variance components in GLMMs, and can serve as a basis of assessing existing approximations in GLMMs. A simulation study and practical example analysis are provided to illustrate this point.
|Keywords||Gauss-Hermite quadrature; Generalised linear mixed models; Maximum likelihood estimates; Newton-Raphson algorithm; Random effects|
|Year of Publication||2003|
|Journal citation||18 (1), pp. 57-78|
|Digital Object Identifier (DOI)||doi:10.1007/s001800300132|
|Open access||Published as non-open access|
|Funder project or code||445|
|Research in statistics relevant to biological processes|
|01 Mar 2003|
|Copyright license||Publisher copyright|
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