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两阶段结果依赖抽样下逻辑回归参数的最大似然估计AI 解读

Summary Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case—control studies in epidemiology and choice–based sampling in econometrics. Two-phase or double sampling is a standard technique for drawing efficient stratified samples. We develop maximum likelihood estimation of logistic regression coefficients for a hybrid two-phase, outcome–dependent sampling design. An algorithm is given for determining the estimates by repeated…

多离散度分量广义线性混合模型的偏差校正AI 解读

General formulas are derived for the asymptotic bias in regression coefficients and variance components estimated by penalized quasi-likelihood (PQL) in generalized linear mixed models with canonical link function and multiple sets of independent random effects. Easily computed correction matrices result in variance component estimates that have satisfactory asymptotic behavior for small values of the variance components and significantly reduce bias for larger values. Both…

单一离散度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…