Linear mixed-effects models are very popular and powerful tools in many scientific fields such as zoology, biology, and education. Estimators of fixed effects do not only depend on the variances of error terms but they also depend on random terms in mixed-effect models. When the distributions of random effects are unknown or enough sample size cannot be obtained, standard methods may fail. This study aims to determine a promising confidence interval method among existing methods in terms of coverage probability of true value of parameter. Standard and parametric bootstrap-based confidence interval methods for nested error regression model were compared in the simulation study under small samples. It is observed that parametric bootstrap-based method provides better coverage rates for small intra-correlation and profile likelihood method usually provides better results for moderate and strong correlation.
The objective of this work was to evaluate the adjustment of data of average wind velocity to Campo Grande, State of Mato Grosso do Sul, to the distributions of probability of: Weibull (W2), Ralyeigh (RAY), Log-Logistic (LL), Inverse Gaussian (IG), Normal (N), Gamma (G), Generalized extreme value (GEV), Extreme value (EV), Lognormal (LN), Logistic (L), Burr (BR) and Rician (R). Four statistical criteria, coefficient of determination (R2), mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are considered as judgment criteria to evaluate the adequacy of probability density functions. As a result, Weibull, Rayleigh, generalized extreme value, extreme value, and Rician distributions accurately perform data. These distributions can be used as an alternative distribution that adequately describes wind speed data in Campo Grande. The weaker settings are obtained by the normal, Burr, logistic, log-logistic, and inverse Gaussian distributions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.