gibbs sampling

Profitability in dairy herds depend on animal survival in the herd, which is provided by reducing involuntary culling and increasing the voluntary culling in the herd. The aim of this study was to investigate the non-genetic factors affecting longevity and to estimate the genetic parameters of longevity and somatic cell score in Iranian Holstein cows. For this purpose, the data of 277715 cattle that were collected by Animal Breeding Center of Iran during 2001 to 2018, were used. Longevity was defined as length of life (LL), lifetime number of days in milk (LDIM), lifetime milk yield (LMY), lifetime fat yield (LFY), and lifetime protein yield (LPY), and the effect of environmental factors on traits and risk of culling were determined using Survival kit and cmprsk statistical packages. Variance components of longevity traits were estimated based on exponential distribution and censored data along with somatic cell score trait using two-trait analysis by Gibbs sampling method. The results showed that the effect of herd, year and season of calving and calving age of cows was significant on all longevity traits (P <0.001). Cows with a high degree of dystocia had a higher culling risk than other cows and the culling risk was higher for cows born in the spring than in other seasons (except to the LL). The range of heritability for different longevity traits varied from 0.076 to 0.186 and the heritability of the somatic cell score was estimated to be 0.31. The results showed that genetic selection for somatic cell score and some longevity traits such as LPY can be effective in improving these traits, although due to the importance of environmental factors on longevity traits, these factors should also be considered. Genetic and residual correlation between somatic cell score with longevity traits were negative and the amount of genetic correlation between traits were higher than their residual correlation. Therefore, genetic selection for somatic cell score and considering it as a selection criterion in breeding programs can indirectly improve the longevity traits in Iranian Holstein cows. Increasing and improvement of longevity is not only economically important but also improves animal welfare.

The aim of this study was to analyze survival and its relationship with body weight and average daily gain traits in Japanese quail. For this purpose, Data base with 1854 Japanese quail survival records were used which collected during 4 generations from 1396 to 1398, by Khorasan Razavi Agricultural and Natural Resources Research and Education Center. The Survival and cmprsk statistical packages were employed to determine the non-genetic effects on survival and culling risk at different times, and variance components estimates of survival trait was performed by MCMCglmm package. Genetic parameters of traits were estimated using single and two-trait analyses via Gibbs sampling. The mean mortality of breeding period (0.206) and the average survival rate (0.793) were calculated. The estimated average heritabilities for survival in single and two-trait analyses were 0.216 and 0.153, respectively. The range of heritability of body weight in single trait analysis was estimated between 0.135 to 0.307) with a mean of 0.219 by two- trait analysis. In two-trait analysis, heritability of body weight trait ranged from 0.155 to 0.014. The highest genetic correlation was -0.3113 (between body weight and average daily gain) and the lowest genetic correlation was -0.0277 (between survival trait and average daily gain). The results showed that optimal management of environmental factors is effective in reducing the culling risk and genetic selection for survival trait can improve the genetic potential of survival.

The nonparametric and semiparametric regression models have been improved extensively in the field of cross-sectional study and independent data, but their improvement in the field of longitudinal data is restricted to the recent years or decade. Since the common methods for correlated data have a much lower ability rather than for the independent data, we should use the models which consider the correlation among the data. The mixed and marginal models consider the correlation factor among the data, and so obtain a better fit for that. Furthermore, the semiparametric regression has more flexibility compared to the parametric and nonparametric regression. Consequently, based on the properties of the longitudinal data, the marginal longitudinal semiparametric regression with the penalized spline estimations, is a suitable choice for the analysis of the longitudinal data. In this article, the semiparametric regression with different coefficients which specifies the relationship between a response variable and an explanatory variable based on another explanatory variable is assessed. In addition, Bayesian inference on the nonparametric model for a simulated data and the marginal longitudinal semiparametric model for a real data have been done by standard software; and the results have good performance.

Statistical prediction analysis plays an important role in a wide range of fields. Examples include engineering systems, design of experiments, etc. In this paper, based on progressively Type-II right censored data, Bayesian two-sample point and interval predictors are developed under both informative and non-informative priors. By assuming a generalized exponential model, prediction bounds as well as Bayes point predictors are obtained under the squared error loss (SEL) and the Linear-Exponential (LINEX) loss functions for the order statistic in a future progressively Type-II censored sample with an arbitrary progressive censoring scheme. The derived results may be used for prediction of total time on test in lifetime experiments. %in reliability analyses In addition to numerical method, Gibbs sampling procedure (as Markov Chain Monte Carlo method) are used to assess approximate prediction bounds and Bayes point predictors under the SEL and LINEX loss functions. The performance of the proposed prediction procedures are also demonstrated via a Monte Carlo simulation study and an illustrative example, for each method.

In many medical studies، in order to describe the course of illness and treatment effects، longitudinal studies are used. In longitudinal studies، responses are measured frequently over time، but sometimes these responses are discrete and with two-state. Recently Binary quantile regression methods to analyze this kind of data have been taken into consideration. In this paper، quantile regression model with Lasso and adaptive Lasso penalty for longitudinal data with dichotomous responses is provided. Since in both methods posteriori distributions of the parameters are not in explicit form، thus the full conditional posteriori distributions of parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed methods with the conventional methods، a simulation study was conducted and at the end، applications to a real data set are illustrated.

In fitting random-intercept models, it is commonly assumed that the random effects and the error terms follow the normal distribution. In many empirical applications, the true distribution of data obeys non-normality and thus the main concern of most recent studies is the use of alternative distributions. In this paper, we propose a new class of random-intercept models using the Skew-Laplace distribution. The new regression model is flexible in the analysis of correlated data and simple in the implementation of Markov Chain Monte Carlo methods, such as the Gibbs sampling approach. Using the stochastic representation of the Skew-Laplace distribution we derive the full conditional posteriors distributions in order to present the Bayesian inference of model parameters. A real data analysis is illustrated from the economic contexts to show the usefulness of the proposed model.

In regression analysis, it is usually assumed that the error terms are independent, but in practice we occasionally deal with many cases such as spatial data that the error terms in regression models are correlated and their correlation structure is a function of the observation locations. This type of models, namely spatial regression, are used for surface determination in geology, archaeology, epidemiology and image processing. In this paper, the Bayesian approach is used for spatial regression analysis with first order spatially autocorrelated errors. Because of computation difficulties of posterior distribution, MCMC methods are used for estimation of the posterior parameters. Then the efficiency of introduced method is considered in a simulation study for different sample and lattice sizes