Random-covariances and mixed-effects models for imputing multivariate multilevel continuous data [0.03%]
随机协方差和混合效应模型在多变量多层次连续数据插补中的应用
Recai M Yucel
Recai M Yucel
Principled techniques for incomplete-data problems are increasingly part of mainstream statistical practice. Among many proposed techniques so far, inference by multiple imputation (MI) has emerged as one of the most popular. While many str...
A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use [0.03%]
一种用于门诊精神卫生服务使用的重复测量零膨胀计数数据的贝叶斯模型及其应用
Brian H Neelon,A James O&#x;Malley,Sharon-Lise T Normand
Brian H Neelon
In applications involving count data, it is common to encounter an excess number of zeros. In the study of outpatient service utilization, for example, the number of utilization days will take on integer values, with many subjects having no...
Latent Regression Analysis [0.03%]
潜在回归分析
Thaddeus Tarpey,Eva Petkova
Thaddeus Tarpey
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a ...
Modelling spatially correlated survival data for individuals with multiple cancers [0.03%]
多重癌症个体的空间相关生存数据建模
Ulysses Diva,Sudipto Banerjee,Dipak K Dey
Ulysses Diva
Epidemiologists and biostatisticians investigating spatial variation in diseases are often interested in estimating spatial effects in survival data, where patients are monitored until their time to failure (for example, death, relapse). Sp...
Thaddeus Tarpey,Dong Yun,Eva Petkova
Thaddeus Tarpey
A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indisti...