BackgroundThe distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student's-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student's-t (BSt) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models.MethodsIn the simulation study, bivariate residuals were generated using Student's-t distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student's-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student's-t distributions with unknown degrees of freedom.ResultsPosterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student's-t error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student's-t model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models.ConclusionsReliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.
, çeşitli endüstri ve faaliyetlere özgü olarak tıp, psikoloji, eğitim, toksikoloji, ergonomi, fizik, kimya, ekonomi, hukuk, teknoloji gibi bilimsel alanlarla ilgili konulara temas eden geniş kapsamlı ve çok disiplinli bir kavramdır. İSG, bu çok çeşitli alanlarla ilgili ve ilişkili olmasının yanı sıra, bazı temel ilkelere sahiptir. Bu ilkelerden en önemlisi, her çalışanın İSG haklarına sahip olması ve bu hakların güvence altına alınması gerekliliğidir. Diğer önemli ilke ise işyerinde sağlık ve güvenliğin tesisi için çalışanın, işverenin ve devletin birtakım sorumlulukları ve yükümlülüklerinin olmasıdır. Hak ve sorumlulukların tam olarak yerine getirilmesi için öncelikle bu konuda bilgi sahibi olmak, dolayısıyla eğitim ve öğretim almak gerekmektedir. Bu araştırma çalışmasında, çalışanların İSG eğitimi konusundaki bilinç düzeylerini belirlemek amacıyla betimsel bir alan çalışması yapılmıştır. Çalışmada nicel araştırma tekniklerinden kaynak tarama ve anket yöntemleri kullanılmıştır. Anket, Çanakkale Onsekiz Mart Üniversitesi'nin farklı birimlerinde görev yapan akademik ve idari personel ile yüz yüze görüşülerek gerçekleştirilmiştir. Katılımcıların anket formlarına verdikleri cevaplar Cronbach Alpha testi kullanılarak güven analizine tabi tutulmuştur. Çalışma sonucunda, çalışanların farklı demografik (cinsiyet, bölüm, medeni durum, unvan gibi) özelliklerine göre İSG konusundaki bilgi düzeyleri ve farkındalıkları tablolar halinde gösterilmiştir.
Urban forests are confronted with high using pressure because of the increasing demand for recreation and accessibility of these settings. For that purpose, defining and managing recreational carrying capacity is considered as significant in ensuring ecological value's and recreational satisfaction's continuity. The purpose of this paper is to investigate the carrying capacity of Erzurum Urban Forest with respect to Level of Service (LOS) as a new management technique that focuses on service quality and visitor satisfaction. The data were obtained by self-administered questionnaire conducted with 166 visitors on weekends and holidays during summer season of the year 2014. Data were analyzed by dimensions/indicators of recreational satisfaction and socio-demographic characteristics with intent to identify tolerance range of visitors. The contribution and relative importance of each of the indicators to the overall satisfaction were analyzed by using Ordinal Logistic Model (OLM). The results indicated that the four indicators were at the greatest degree; "distance from picnic spot to toilets" and "quantity of children's playground facilities" were decease of overall satisfaction while "distance from picnic spot to parking" and "level of shade at picnic spot" had a positive contribution to the overall satisfaction. The outputs from these analyses were used to calculate LOS. It was revealed that the level of service (0.40) in Erzurum Urban Forest was below the moderate level. Planning and managing strategies for optimizing the LOS were developed and discussed by considering these results.
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