Add to ORKGIn Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum likelihood estimator is removed by adjusting the score vector, and that in canonical-link generalized linear models the method is equivalent to maximizing a penalized likelihood that is easily implemented via iterative adjustment of the data. Here a more general family of bias-reducing adjustments is developed for a broad class of univariate and multivariate generalized nonlinear models. The resulting formulae for the adjusted score vector are computationally convenient, and in univariate models they directly suggest implementation through an iterative scheme of data adjustment. For generalized linear models a necessary and sufficient conditi...
The adjustment of the binomial data by small constants is a common practice in statistical modellin...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...
Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not con...
In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum ...
In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum ...
This paper presents an integrated framework for estimation and inference from generalized linear mod...
In this paper, we derive general formulae for second-order biases of maximum likelihood estimates wh...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Under suitable regularity conditions, an appropriate improved score statistic was derived recently b...
We derive analytic expressions for the biases, to O(n-1), of the maximum likelihood estimators of th...
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates ...
We propose a likelihood function endowed with a penalisation that reduces the bias of the maximum li...
For the parameters of a multinomial logistic regression, it is shown how to obtain the bias-reducing...
© 2009 Australian Statistical Publishing Association Inc. Published by Blackwell Publishing Asia Pty...
The adjustment of the binomial data by small constants is a common practice in statistical modellin...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...
Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not con...
In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum ...
In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum ...
This paper presents an integrated framework for estimation and inference from generalized linear mod...
In this paper, we derive general formulae for second-order biases of maximum likelihood estimates wh...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Under suitable regularity conditions, an appropriate improved score statistic was derived recently b...
We derive analytic expressions for the biases, to O(n-1), of the maximum likelihood estimators of th...
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates ...
We propose a likelihood function endowed with a penalisation that reduces the bias of the maximum li...
For the parameters of a multinomial logistic regression, it is shown how to obtain the bias-reducing...
© 2009 Australian Statistical Publishing Association Inc. Published by Blackwell Publishing Asia Pty...
The adjustment of the binomial data by small constants is a common practice in statistical modellin...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...
Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not con...