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基于非对称极大似然的泊松过度离散估计AI 解读

A common difficulty in regression problems with Poisson (or binomial or other exponential family) response variables is over-dispersion: the scatter around the fitted regression is too large by the standards of Poisson variability. This article concerns the description, estimation, and testing of various patterns of overdispersion, with particular emphasis on the Poisson case. Asymmetric maximum likelihood (AML) is a method of fitting regressions for the conditional…

基于非对称最大似然的 Poisson 过度离散估计AI 解读

A common difficulty in regression problems with Poisson (or binomial or other exponential family) response variables is over-dispersion: the scatter around the fitted regression is too large by the standards of Poisson variability. This article concerns the description, estimation, and testing of various patterns of overdispersion, with particular emphasis on the Poisson case. Asymmetric maximum likelihood (AML) is a method of fitting regressions for the conditional…

零一数据的回归与方差分析：残差变异的度量AI 解读

We consider regression situations for which the response variable is dichotomous. The most common analysis fits successively richer linear logistic models and measures the residual variation from the model by minus twice the maximized log likelihood. General measures of residual variation are considered here, including ordinary squared error and prediction error as well as the log likelihood. All of these are shown to be satisfactory in a certain primitive sense, unlike…

Logistic 回归与正态判别分析的效率比较AI 解读

A random vector x arises from one of two multivariate normal distributions differing in mean but not covariance. A training set x 1, x 2, ··· x n of previous cases, along with their correct assignments, is known. These can be used to estimate Fisher's discriminant by maximum likelihood and then to assign x on the basis of the estimated discriminant, a method known as the normal discrimination procedure. Logistic regression does the same thing but with the estimation of…

使序贯实验保持分组平衡AI 解读

Subjects arrive sequentially at an experimental site and must be assigned immediately to treatment or control groups. In order to avoid biasing the results of the experiment it is customary to make the assignments by independent flips of a fair coin, but in small-sized experiments this may result in a severe imbalance between the numbers of treatments and controls. This paper discusses a new method of assigning the subjects which tends to balance the experiment, but at the…

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