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单元格含多个计数的双因素计数数据分析AI 解读

Multiple counts may occur in each cell of an a × b two-way layout (balanced or unbalanced) of two fixed factors A and B. Standard log-linear model analysis based on a Poisson distribution assumption of the cell counts is not applicable here, because of the unbalanced nature of the table or because the Poisson distribution assumption is not valid. We develop C(α) tests for interaction and main effects assuming data to be Poisson distributed and also assuming that data within…

每格含多个计数的双因素布局分析AI 解读

Multiple counts may occur in each cell of an a × b two-way layout (balanced or unbalanced) of two fixed factors A and B. Standard log-linear model analysis based on a Poisson distribution assumption of the cell counts is not applicable here, because of the unbalanced nature of the table or because the Poisson distribution assumption is not valid. We develop C(α) tests for interaction and main effects assuming data to be Poisson distributed and also assuming that data within…

家庭数据中组内相关的最大似然估计：估计方程方法AI 解读

We introduce a generalized model for estimating intraclass correlation in the analysis of familial data. This model incorporates the models of Donner & Koval (1980), Donner & Bull (1983), Rosner (1984) and Münoz, Rosner & Carey (1986) as special cases. We then obtain the maximum likelihood estimates of the parameters of the model. In particular we obtain a single estimating equation, to be solved iteratively, for the estimation of the intraclass correlation p and discuss all…

家族数据组内相关的极大似然估计：估计方程方法AI 解读

We introduce a generalized model for estimating intraclass correlation in the analysis of familial data. This model incorporates the models of Donner & Koval (1980), Donner & Bull (1983), Rosner (1984) and Münoz, Rosner & Carey (1986) as special cases. We then obtain the maximum likelihood estimates of the parameters of the model. In particular we obtain a single estimating equation, to be solved iteratively, for the estimation of the intraclass correlation p and discuss all…

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负二项变异下计数数据的单因素组间分析AI 解读

Two C(α) statistics (Neyman, 1959) for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter are derived. The performance of the two C(α) statistics, the likelihood ratio statistic and two more statistics based on transformed data (Anscombe, 1948) is then compared in terms of size and power by using Monte Carlo simulations. The C(α) statistics are recommended. The C(α) statistics for testing the equality of the…

具有负二项变异的计数数据单因素分析AI 解读

Two C(α) statistics (Neyman, 1959) for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter are derived. The performance of the two C(α) statistics, the likelihood ratio statistic and two more statistics based on transformed data (Anscombe, 1948) is then compared in terms of size and power by using Monte Carlo simulations. The C(α) statistics are recommended. The C(α) statistics for testing the equality of the…

泊松混合对统计推断的敏感性AI 解读

Data presented as contingency tables and classified by qualitative or quantitative methods are usually analysed on the basis of a Poisson log linear model. We examine the effect of Poisson mixtures, especially as represented by the negative binomial distribution, on statistical inference. The main finding is that probabilities of rejection are increased, sometimes considerably. In the case of heterogeneous binary data, attention is also given to the problems of analysis…

泊松混合下的推断敏感性AI 解读

Data presented as contingency tables and classified by qualitative or quantitative methods are usually analysed on the basis of a Poisson log linear model. We examine the effect of Poisson mixtures, especially as represented by the negative binomial distribution, on statistical inference. The main finding is that probabilities of rejection are increased, sometimes considerably. In the case of heterogeneous binary data, attention is also given to the problems of analysis…