
doi: 10.2307/3109764
pmid: 9629645
The linear mixed model has become an important tool in modelling, partially due to the introduction of the SAS procedure MIXED, which made the method widely available to practising statisticians. Its growing popularity calls for data-analytic methods to check the underlying assumptions and robustness. Here, the problem of detecting influential subjects in the context of longitudinal data is considered, following the approach of local influence proposed by Cook.
Male, longitudinal data, Likelihood Functions, Biometry, Models, Statistical, Computational problems in statistics, Age Factors, Prostatic Hyperplasia, Prostatic Neoplasms, Prostate-Specific Antigen, Applications of statistics to biology and medical sciences; meta analysis, influential subjects, Diagnosis, Differential, Cross-Sectional Studies, diagnostics, Humans, Longitudinal Studies, linear mixed model
Male, longitudinal data, Likelihood Functions, Biometry, Models, Statistical, Computational problems in statistics, Age Factors, Prostatic Hyperplasia, Prostatic Neoplasms, Prostate-Specific Antigen, Applications of statistics to biology and medical sciences; meta analysis, influential subjects, Diagnosis, Differential, Cross-Sectional Studies, diagnostics, Humans, Longitudinal Studies, linear mixed model
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 206 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
