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广义线性混合模型对假设敏感性的一个注记
A simple case of Poisson regression is used to study the potential gain in efficiency from using a mixed model representation. Possible systematic errors arising from misspecification of the random terms in the model are examined. It is shown in particular that for a special but realistic problem, appreciable bias may arise from misspecification of a random component.
检验多个总体斜率的似然比统计量及 Bartlett 校正
Inference about the slopes of two or more populations when the explanatory variables are measured with error is studied. We concentrate on the construction of a likelihood based test statistic and its Bartlett correction factor. The improvement of the chi-squared approximation is appreciable in the unknown variance case.
检验多个斜率时似然比统计量的Bartlett调整
Inference about the slopes of two or more populations when the explanatory variables are measured with error is studied. We concentrate on the construction of a likelihood based test statistic and its Bartlett correction factor. The improvement of the chi-squared approximation is appreciable in the unknown variance case.
两个变量均有测量误差时简单线性回归的似然估计
Likelihood based methods for obtaining approximate confidence intervals for the slope in a simple linear regression when both variables are measured with error are discussed and compared. We also derive a Bartlett correction factor for the likelihood ratio statistic. The improvement of the chi-squared approximation is appreciable in the unknown variance case. Further, the distribution of the maximum likelihood slope estimator is obtained from the joint distribution of sample…