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广义混合模型中的估计AI 解读

SUMMARY Regression models containing fixed and random effects may have a response variable which is not normally distributed. The generalized mixed model includes both discrete and continuous response variables and is developed here for problems in which the regression variables enter linearly into the model. Best linear unbiased predictor methods are extended to maximum likelihood and residual maximum likelihood estimation procedures. Applications in modelling discrete…

一般线性模型的误差诊断：重复测量与田间试验AI 解读

SUMMARY The large sample distribution of the recursive residual maximum likelihood residuals from the general linear model with dependent errors is derived. Further results are obtained from this distribution to aid the identification of an appropriate error model in the general linear model.

具有相关误差的一般线性模型的递归估计AI 解读

Summary The techniques for recursive estimation of the general linear model are extended to the case of dependent errors with known second-order properties. Recurrence relations for computation and a sequence of independent recursive residuals, useful for testing the constancy of the regression relation over time, are derived and studied. Computational savings are available when the error process is stationary.

从概率递推关系构造离散分布估计量AI 解读

A method is proposed for deriving an estimation procedure from the recurrence relationship between probabilities in a discrete distribution. It is limited to cases in which only one parameter occurs in the recurrence relationship. To find a confidence interval for the parameter we consider the estimating function obtained and this may be shown to be dependent on differences between the cumulative probability function and its estimated value.

从概率递推关系构造离散分布估计量AI 解读

A method is proposed for deriving an estimation procedure from the recurrence relationship between probabilities in a discrete distribution. It is limited to cases in which only one parameter occurs in the recurrence relationship. To find a confidence interval for the parameter we consider the estimating function obtained and this may be shown to be dependent on differences between the cumulative probability function and its estimated value.

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