On Bayesian estimation of a latent trait model defined by a rank-based likelihood.

Maximum likelihood estimation (frequentist) and Bayesian estimation are two common parameter estimation methods. However, maximum likelihood estimation faces limitations, including the effect of outliers, computational complexity, and issues with ordinal categorical data, leading to biased estimates and inaccurate coverage probabilities. To address these limitations, this study employed a latent trait model with a Bayesian marginal likelihood of rank-based estimation for parameter estimation.

Bayesian rank likelihood-based estimation: An application to low birth weight in Ethiopia.

Background: Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability.

Methods: This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk factors in Ethiopia.

Influence of temperature on virulence of fungal isolates of Metarhizium anisopliae and Beauveria bassiana to the two-spotted spider mite Tetranychus urticae.

Twenty-three isolates of Metarhizium anisopliae (Metschnikoff) Sokorin and three isolates of Beauveria bassiana (Balsamo) Vuillemin (Ascomycota: Hypocreales: Clavicipitaceae) were assessed for their virulence against the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae). Based on the screening results, nine isolates of M. anisopliae and two isolates of B.