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用于多项数据额外变异建模的有限混合分布AI 解读

We propose a new distribution to model categorical data exhibiting overdispersion when the overdispersion is believed to be caused by clumped sampling. The proposed distribution is a finite mixture of multinomial random variables. The finite mixture representation facilitates computation of the maximum likelihood estimator. For large cluster sizes, we derive an approximation for the Fisher information matrix of the model, and an explicit expression for the joint asymptotic…

用于多项分布额外变异建模的有限混合分布AI 解读

We propose a new distribution to model categorical data exhibiting overdispersion when the overdispersion is believed to be caused by clumped sampling. The proposed distribution is a finite mixture of multinomial random variables. The finite mixture representation facilitates computation of the maximum likelihood estimator. For large cluster sizes, we derive an approximation for the Fisher information matrix of the model, and an explicit expression for the joint asymptotic…

非线性约束下线性时间序列模型的参数估计AI 解读

Least squares estimators of the parameters of a linear time series model, where the parameters are constrained by a set of nonlinear restrictions, are studied. The model may contain lags of the dependent variable as regressors and the sums of squares of the explanatory variables may grow at different rates as the sample size increases. The estimation procedures can be applied to a regression model with an error process that satisfies either a stationary or a nonstationary…