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生存数据混合回归模型中的参数正交性AI 解读

Summary The implications of parameter orthogonality for the robustness of survival regression models are considered. The question of which of the proportional hazards or the accelerated life families of models would be more appropriate for analysis is usually ignored, and the proportional hazards family is applied, particularly in medicine, for convenience. Accelerated life models have conventionally been used in reliability applications. We propose a one-parameter family…

非线性方差分量模型AI 解读

General aspects of nonlinearity in the context of component of variance models are discussed, and two special topics are examined in detail. Firstly, simple procedures, both formal and informal, are proposed for describing departures from normal-theory linear models. Transformation models are shown to be a special case of a more general formulation, and data on blood pressure are analyzed in illustration. Secondly, an approximate likelihood is proposed and its accurate…

大量小样本中的序列相关检验AI 解读

Methods are outlined for testing for serial correlation in large numbers of small samples, emphasis being placed on samples of size three. Some data on pulse rates are analysed in illustration.

大量小样本中的序列相关检验AI 解读

Methods are outlined for testing for serial correlation in large numbers of small samples, emphasis being placed on samples of size three. Some data on pulse rates are analysed in illustration.

方差与协方差分量模型的变量变换AI 解读

Variance component models make various independence assumptions and typically assume effects are normally distributed. Here we assume that the data, possibly after suitable transformation, satisfy such a model. An analysis of transformations for balanced variance component models is presented, components of covariance being incorporated in the multivariate case. An analysis of some blood pressure data is discussed.

方差与协方差分量模型中的变换AI 解读

Variance component models make various independence assumptions and typically assume effects are normally distributed. Here we assume that the data, possibly after suitable transformation, satisfy such a model. An analysis of transformations for balanced variance component models is presented, components of covariance being incorporated in the multivariate case. An analysis of some blood pressure data is discussed.