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.

Determination of the input/output variables is an important issue in Data Envelopment Analysis (DEA). Researchers often refer to expert opinions in defining these variables. The purpose of this paper is to propose a new approach to determine the input/output variables, it is important to keep in mind that especially when there is no any priori information about variable selection. This new proposed technique is based on a theoretical method which is called “Copula”. Copula functions are used for modeling the dependency structure of the variables with each other. Also we use the local dependence function which analyzes the point dependency of variables of copulas to define the input/output variables. To illustrate the usefulness of the proposed approach, we conduct two applications using simulated and real data and compare the efficiencies in DEA. Our results show that new approach gives values close to perfection.

In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1) and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB), Hurdle-Poisson (HP) and Hurdle-Negative Binomial (HNB) amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.). The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions. ÖZEkolojik çalışmaların çoğunda, ya örneklemlerdeki belirleme hataları ya da türlerin varlıkları için elverişsiz yaşam koşulları sebebiyle, türlerin yoklukları kaçınılmazdır ve bunun sonucu olarak çok fazla miktarda sıfır sayısı ya da bolluğu ortaya çıkar. Bu tipteki veriler için genellikle log(bolluk+1) regresyon modellemesi yapılır ve oluşturulan modelinin kestirim için yetersiz olduğu bilinen bir gerçektir. Sıfır sayılı bollukları içeren, aralarında sıfır ağırlıklı regresyon (ZIP ve ZINB), engelli-poisson (HP) ve engelli negatif binom (HNB) bulunan yeni kesikli modeller klasik regresyon modellerine göre daha çok tercih edilmektedir. Türkiye için ekonomik açıdan en önemli deniz ürünlerinden biri olması nedeniyle, bu çalışmada Akdeniz midyelerinin (Mytilus galloprovincialis L.) varoluşlarının tehditi Nematopsis legeri paraziti için önemli biyotik ve abiyotik faktörlerin belirlenmesinde bu dört modelin performanslarının kıyaslanması amaçlanmıştır. Türkiye'nin Sinop şehrinin üç kıyı bölgesinden toplanan parazitlerin sayılarının ortalamada %50'den fazlasının sıfır değerli olduğu görülmüş ve oluşturulan modellerin kıyaslaması bilgi 379 kriterleri ile yapılmıştır. Sonuçlar bu parazitlerin oluşma olasılıklarını en iyi ZINB ve HNB modelleriyle ifade edildiğini göstermiştir ve modellerde etkili faktörlerin bölgelerin çevresel farklılıklarıyla ilişkili olduğu bulunmuştur.Anahtar Kelimeler: Akdeniz midyesi parazitleri, Poisson regresyon, Negatif binom regresyon, Sıfır ağırlıklı regresyon, Engelli model

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