Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning

The cave of shadows: Addressing the human factor with generalized additive mixed models

“…Would researchers come to 'wrong' conclusions if they analyze data simply using maximal linear models, without taking the human factor into account, without paying attention to whether the model is overfitting the data with mathematically uninterpretable parameters (Lele et al, 2012;Bates et al, 2015), and accepting less than nominal power (Matuschek et al, 2016)? The problem here is that low-hanging fruit is easily plucked, often by simple linear models without any random effects.…”

“…This confirmatory modeling strategy has the advantage that models that overfit the data with meaningless parameters are avoided. As pointed out by Lele et al (2012), "Whenever mixed models are used, estimability of the parameters should be checked before drawing scientific inferences or making management decisions". Since meaningless parameters can arise even under convergence (see Bates et al, 2015), the analyst may want to minimize the risk of running into this situation specifically when significance is assessed in a confirmatory context.…”

“…Since the proposed standard of Barr et al is overly conservative with an unnecessary loss of power (Matuschek et al, 2016), and since it comes with the risk of basing conclusions on mathematically ill-defined models that overfit the data (Lele et al, 2012;Bates et al, 2015), Barr et al's proposal has the unfortunate side-effect of locking analytical practice in a methodological cage of shadows from which the true structure of experimental data, rich and fertile in perhaps unexpected ways, cannot be fully appreciated.…”

Spatial Disease Cluster Detection: An Application to Childhood Asthma in Manitoba, Canada

“…Lele et al [19] also described an approach to compute prediction and prediction intervals for the random effects. The DC approach, thus, is well suited to address the issues in spatial focused cluster detection using the frequentist paradigm.…”

Logit-normal mixed model for Indian monsoon precipitation

“…One may then use MCMC to generate a dependent sample from the posterior distribution from which estimates can be derived based on strong laws. Lele et al (2010) derived a method called data cloning to be used in conjunction with MCMC. The algorithm can be summarized in the following three steps.…”