to such analyses requires choosing a covariance structure. Information criteria, such as AIC and BIC, are often used by … KR adjustment method, incorporating the selection of the covariance structure using AIC and BIC. Performance of the AIC … analyze each simulated data set. Type I error rates from the best AIC and BIC models were always higher than target values …

Information Criterion (AIC), Bayesian Information Criterion (BIC) , or Deviance Information Criterion (DIC) which corrects for … bias induced by sample size and number of random effects. AIC, BIC, and DIC are information criteria that measure the … school counties nested in states. These are considered hierarchical, nested, or multilevel because students are within the …

using the spline method and estimate the associated parameters using the maximum likelihood approach. A simulation study and …

used in making inferences and predictions. Missing data is a very common occurrence in most research settings and can …

with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from … literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the “best model …” according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates …

This paper derives the exact distribution of the maximum likelihood estimator of a first-order linear autoregression … estimator is ρT-consistent. Further, the likelihood ratio test statistic for a simple hypothesis on the autoregressive parameter …). 'Likelihood analysis of a first-order autoregressive model with exponential innovations', Journal of Time Series Analysis, 24 …

exactly describe the Maximum Likelihood Estimator (MLE) in terms of convolution under a certain model for ideal images formed …

uncertainty is represented by a probability function, in our theory, uncertainty is given in the form of a likelihood function …This paper presents a new axiomatic decision theory for choice under uncertainty. Unlike Bayesian decision theory where … extracted from statistical evidence. The likelihood principle in statistics stipulates that likelihood functions encode all …