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非线性回归估计量的不对称性AI 解读

The least-squares estimators of nonlinear regression parameters are partitioned into three terms. Only the first is symmetrically distributed. Measures of asymmetry are derived from the other two which are valid for any sample size in that, if they are both zero for a given parameter, then its estimator is exactly symmetrically distributed. Asymptotic results are derived and we find that all odd central moments have a common factor when evaluated to first order. This factor…

具有嵌套层级超泊松变异的广义线性模型AI 解读

We consider a scaled Poisson generalized linear model with random multiplicative errors associated with each stratum of a nested block structure. The normal equations are obtained. The quasi-likelihood is shown to be the product of a sequence of negative binomial quasi-likelihoods using weighted totals and correcting for margins. This is achieved by extending the definition of the quasi-likelihood which now includes a vector of weights. The usual asymptotic results still…

非线性回归估计量的非对称性AI 解读

The least-squares estimators of nonlinear regression parameters are partitioned into three terms. Only the first is symmetrically distributed. Measures of asymmetry are derived from the other two which are valid for any sample size in that, if they are both zero for a given parameter, then its estimator is exactly symmetrically distributed. Asymptotic results are derived and we find that all odd central moments have a common factor when evaluated to first order. This factor…

具有嵌套层级额外泊松变异的广义线性模型AI 解读

We consider a scaled Poisson generalized linear model with random multiplicative errors associated with each stratum of a nested block structure. The normal equations are obtained. The quasi-likelihood is shown to be the product of a sequence of negative binomial quasi-likelihoods using weighted totals and correcting for margins. This is achieved by extending the definition of the quasi-likelihood which now includes a vector of weights. The usual asymptotic results still…