Analysis of quantal models is a particular aspect of the general problem of investigating multimodality. The distinction is that the spacings between modes are integral multiples of some unspecified fundamental unit and that the number of modes is not defined. Such semi-structured models arise in a wide variety of contexts such as biology, cosmology, archaeology and molecular physics. This paper presents a brief review of their historical development in such areas as an aid to their recognition in other contexts as well as giving guidance to their analysis from the statistical viewpoint. The available methodology for their analysis is collated into a coherent and self-contained account, establishing various optimality properties under particular parametric distributional assumptions. An illustrative power study shows how dependence on sample size and failure of assumptions such as underlying distribution, origin of measurements and independence affect the power of various analyses. These aspects are illustrated by an example from developmental biology.cosine quantogram, megalithic yard, quantal model, multimodality, power,

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This paper investigates bayesian treatment of regression modelling with Ramsay-Novick (RN) distribution speci…cally developed for robust inferential procedures. It falls into the category of the so-called heavy-tailed distributions generally accepted as outlier resistant densities. RN is obtained by coverting the usual form of a non-robust density to a robust likelihood through the modi…cation of its unbounded in ‡uence function. The resulting distributional form is quite complicated which is the reason for its limited applications in bayesian analyses of real problems. With the help of innovative Markov Chain Monte Carlo (MCMC) methods and softwares currently available, here we …rst suggested a random number generator for RN distribution. Then, we developed a robust bayesian modelling with RN distributed errors and Student-t prior. The prior with heavy-tailed properties is here chosen to provide a built-in protection against the misspeci…cation of con ‡icting expert knowledge (i.e. prior robustness). This is particularly useful to avoid accusations of too much subjective bias in the prior speci…cation. A simulation study conducted for performance assessment and a real-data application on the famously known "stack loss" data demonstrated that robust bayesian estimates with RN likelihood and heavy-tailed prior are robust against outliers in all directions and inaccurately speci…ed priors.

This study aimed to assess the health status of grey mullet Mugil cephalus by means of relative condition factor under the coexisting parasite groups within seasons. Fish were captured monthly in the Lower Kızılırmak Delta in Samsun, Turkey, from December 2011 to November 2012. A total of five taxonomic groups containing 13 parasite species were constituted: Trichodina (Trichodina puytoraci, T. lepsii), Monogenea (Ligophorus cephali, L. mediterraneus, Gyrodactylus sp., Microcotyle mugilis), Digenea (Ascocotyle (Phagicola) longa, Diplostomum spathaceum, Tylodelphys clavata, Posthodiplostomum sp., Haplosplanchnus pachysomus), Acanthocephala (Neoechinorhynchus agilis) and Copepoda (Ergasilus lizae). Only one mullet was not parasitized and 13.8%, 36.3%, 29.2%, 16.2% and 4.5% were infected by one, two, three, four and five parasite groups respectively. Health assessment of mullets was performed by means of relative condition factor based on weight–length relationship estimated by robust regression methods. General linear modelling was employed to investigate any effect of season and infection load on this condition measure. Potential impacts of multiple parasitism on the community, which have been largely overlooked in the literature, were first visually revealed by relating the levels of co‐infections (in terms of prevalence and intensity) to the seasons. Then, a correlation analysis was carried out between condition estimates and the log‐ratios of relative abundances of parasite groups. The results showed that mullets suffering from Monogenea–Digenea–Acanthocephala (M–D–A) and also along with Copepoda (M–D–A–C) co‐infection had weights less than expected (Kn < 1) under winter conditions. However, Trichodina involved co‐infections appeared to prefer large sized hosts (Kn > 1) particularly in autumn.

Amaç: Diyetisyenlerin iş doyumunu etkileyen faktörlerin iyileştirilmesi, iş doyumunun yükseltilmesi, bireylerin/toplumun beslenme ve sağlık durumunun iyileştirilmesine katkıda bulunabilir. İzmir'de çalışan diyetisyenlerin bireysel beslenme danışmanlığı ile ilişkili faktörler hakkındaki düşüncelerinin incelenmesi, iş doyumu ile iş doyumunu etkileyen faktörlerin saptanması amaçlanmıştır. Yöntem: Tanımlayıcı tipteki bu araştırma, İzmir'de çalışan diyetisyenlerle (n=183) yürütülmüştür. Verilerin elde edilmesinde literatür taramasıyla oluşturulan anket formu ve İş Doyumu Ölçeği (Job Satisfaction Survey-JSS) kullanılmıştır. Ki-kare, bağımsız gruplarda t testi, one-way ANOVA, tukey testi, lineer regresyon analizi (enter modeli) kullanılmış, p<0.05 istatistiksel olarak önemli kabul edilmiştir. Bulgular: Diyetisyenlerin yaş ortalaması 31.9±8.84 yıl olup, %89.1'i kadındır. Diyetisyenlerin %34.4'ü kamuda, %38.8'i özel sektörde, %26.8'i kendi iş yerinde çalışmaktadır. Diyetisyenlerin JSS puanı 127.72±34.47 olup, iş doyumu düşüktür. Yüksek lisans mezunlarının, özel sektörde ve klinik alanda çalışanların, gelirini yeterli bulan, mesleğini çevresine öneren diyetisyenlerin ortalama iş doyumu puanı daha yüksektir (p<0.05). Lineer regresyon analizine göre diyetisyenlerin çalışma sektörü; özelden kamuya, kamudan iş yeri sahibine doğru ilerledikçe iş doyumu azalmaktadır. Diyetisyenler beslenme danışmanlığında sosyal medyanın etkili olduğunu, sosyal medyanın insanları beslenme açısından kısmen doğru yönlendirdiğini düşünmekte, medyada beslenme bilgisi verilmesini doğru bulmamakta, internet üzerinden verilen beslenme danışmanlığını yeterli bulmamaktadır. Diyetisyenlere göre danışan motivasyonunda en önemli etmenler; diyetisyenin yüz ifadesi, beden dili, kararlı tutumu, gerçekçi hedefler belirlemesi, görünüşü ve özgüvenidir. Sonuç: Beslenme danışmanlığı ve iş doyumuna ilişkin bu veriler, mesleki kuruluşlar ve eğitim planlayıcıları için yol gösterici olabilir. İş doyumunu etkileyen etmenlerin daha derinlemesine incelenmesi için nitel verilerle desteklenen daha fazla nicel çalışmaya ve iş doyumunu arttıracak faktörlere odaklanan çalışmalara ihtiyaç vardır.

Citation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to the fact that distribution of citation counts involves a great number of zeros, this study serves for an additional aim that is to model the counts with advanced discrete regression models for a more precise prediction [2]. Data collected for this study consist of the citation counts of all scientific papers produced by 261 Statisticians in between 2005-2017. Discrete models varying from Poisson to Zero-Inflated or Hurdle were constructed by possible influential factors, such as the publication age, the number of references, the journal category etc. Predictive performances of alternative discrete models were compared via AIC and Vuong test [3]. Results suggested that Zero Inflated Negative Binomial and Hurdle Negative Binomial mixture models are the best forms to predict the zero inflation of citation counts [4]. In addition, the influential factors of the final model were interpreted to make some suggestions to Statisticians to increase the citation counts of their work.

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This paper considers parameter estimation of the linear regression model with Ramsay-Novick (RN) distributed errors, focusing on its use to aid robustness. Positioning within the class of heavy-tailed distributions, RN distribution can be defined as the modification of unbounded influence function of a non-robust density so that it has more resistance to outliers. Potential use of this robust density has been assessed in Bayesian settings on real data examples and there is a lack of performance assessment for finite samples in the classical approach. Therefore, this study explores its robustness properties when used as error distribution compared to normal and other alternating heavy-tailed distributions like Laplace and Studentt. An extensive simulation study was conducted for this purpose under different settings of sample size, model parameters and outlier percentages. An efficient data generation of RN distribution through random-walk Metropolis algorithm is here also suggested. The results were supported by a real world application on famously known as Brownlee's stack loss plant data.