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In the past decade, mixed-effects modelling has
received a great deal of attention in applied and
theoretical statistical literature. Mixed models are
very flexible tools in analysing repeated measures
and hierarchical data. This monograph is divided
into three parts, the first presenting a "workshop"
of advanced analysis of some real data sets in the
context of the mixed models, which allows to favor
the conceptualization of the statistical modelling
in the theoretical - practical context. This part
provide an excellent starting point to analyze
longitudinal data. The second part was devoted to a
simulation study to assess the robustness of the
linearization and the two-stage methods under non-
normality of random effects and/or residuals. A
complementary simulation study was devoted to the
impact of incorrect assumptions on the true
structure of the random effects covariance matrix
and the true correlation pattern of residuals, over
the performance of the linearization approach. In
the last part, different bootstrap techniques were
suggested, to perform statistical inference based on
the repeated measures and on the NLME context.
received a great deal of attention in applied and
theoretical statistical literature. Mixed models are
very flexible tools in analysing repeated measures
and hierarchical data. This monograph is divided
into three parts, the first presenting a "workshop"
of advanced analysis of some real data sets in the
context of the mixed models, which allows to favor
the conceptualization of the statistical modelling
in the theoretical - practical context. This part
provide an excellent starting point to analyze
longitudinal data. The second part was devoted to a
simulation study to assess the robustness of the
linearization and the two-stage methods under non-
normality of random effects and/or residuals. A
complementary simulation study was devoted to the
impact of incorrect assumptions on the true
structure of the random effects covariance matrix
and the true correlation pattern of residuals, over
the performance of the linearization approach. In
the last part, different bootstrap techniques were
suggested, to perform statistical inference based on
the repeated measures and on the NLME context.