The problem of estimating the variance parameter robustly in a heteroscedatic linear model is considered. The situation where the variance is a function of the explanatory variables is treated. To estimate the variance robustly in this case, it is necessary to guard against the influence of outliers in the design as well as outliers in the response. By analogy with the homoscedastic regression ...

The assessment of the influence of individual observations on the outcome of the analysis by perturbation has received a lot of attention for situations in which the observations are independent and identically distributed. However, no methods based on minor perturbations for carrying out such assessments are available in the context of multinomial models. A simultaneous perturbation scheme for...

Over the last 20 to 30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson-Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this stud...

a number of 5558 lambing records of 2245 mehraban ewes, collected during years 1995 to 2010 were utilized to estimate the variance components and heritability for the number of lambs per lambing,  making use of b-spline random regression models. the fixed factors affecting the studied trait were identified using general linear model analysis as well as sas software, where flock-year-season and ...

this study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ann) in broiler chicken. artificial neural networks (anns) are powerful tools for modeling systems in a wide range of applications. the ann model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...