In this research genetic and phenotypic parameters were estimated using linear and threshold models, for reproductive traits, data from 6 large industrial dairy herd of East Azerbaijan province collected by Agriculture Jihad Organization during 10 years (2001-2010). Best linear unbiased predictions of traits breeding values were estimated using Restricted Maximum Likelihood method by WOMBAT sof...

We consider model selection in a hierarchical Bayes formulation of the sparse normal linear model in which individual variables have, independently, an unknown prior probability of being included in the model. The focus is on orthogonal designs, which are of particular importance in nonparametric regression via wavelet shrinkage. Empirical Bayes estimates of hyperparameters are easily obtained ...

The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural ne...

purpose: this study aimed to evaluate risk factors associated with threshold retinopathy of prematurity (rop) in premature infants referred to farabi eye hospital during 2008-2009. since blindness and sever visual impairments will occur in a high percentage of infants stricken with threshold retinopathy, we intended to examine relationship between demographic factors and threshold retinopathy i...

The first-hitting-time based model conceptualizes a random process for subjects’ latent health status. time-to-event outcome is modeled as the first hitting time of to pre-specified threshold. Threshold regression with linear predictors has numerous benefits in causal survival analysis, such estimators’ collapsibility. We propose neural network extension threshold model. With flexibility networ...