论文检索

单一离散分量广义线性混合模型的偏差校正AI 解读

General expressions are derived for the asymptotic biases in three approximate estimators of regression coefficients and variance component, for small values of the variance component, in generalised linear mixed models with canonical link function and a single source of extraneous variation. The estimators involve first and second order Laplace expansions of the integrated likelihood and a related procedure known as penalised quasi-likelihood. Numerical studies of a series…

广义线性混合模型中的近似推断AI 解读

Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior…

摘要暂缺

摘要暂缺

两阶段病例对照数据的 Logistic 回归AI 解读

Samples of diseased cases and nondiseased controls are drawn at random from the population at risk. After classification according to the exposure of interest, subsamples of cases and controls are selected for purposes of covariable ascertainment. A modification of the usual logistic regression analysis yields consistent estimates of covariable adjusted relative risks and their standard errors. By balancing the numbers of exposed and nonexposed for whom covariable…