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平移幂变换模型的分组似然AI 解读

SUMMARY Estimation of parameters is considered in the shifted power transformation family of models. The ordinary likelihood function is shown to behave poorly, often having no local maximum. It is argued that a grouped likelihood approach avoids these difficulties, and the practical implementation of this approach is considered. Bayesian inference is also considered, though more briefly, and it is argued that there are again difficulties with an ungrouped likelihood…

将残差用作伴随协变量AI 解读

Suppose that there is correlation between the yields of successive or adjacent experimental units. An estimator of treatment effects using the residuals of adjacent plots as a concomitant variable is investigated. It is shown to be very close to the maximum likelihood estimator when the errors form a first-order autoregressive series.

将残差用作伴随变量AI 解读

Suppose that there is correlation between the yields of successive or adjacent experimental units. An estimator of treatment effects using the residuals of adjacent plots as a concomitant variable is investigated. It is shown to be very close to the maximum likelihood estimator when the errors form a first-order autoregressive series.