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Dalgacık eşiklendirme ile regresyon analizi
No full textTez (Yüksek Lisans) -- Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü, 2019.[Abstract Not Available
Performance of Information Complexity Criteria in Structural Equation Models with Applications
No full textA common problem in structural equation modeling is that of model selection. Many researchers have addressed this problem, but many methods have provided mixed benefits until recently. Akaike's well-known criteria, AIC, has been applied in the context of structural equation modeling, but the effectiveness of many other information criteria have not been studied in a convincing manner. In this paper, we compare the SEM model selection prowess of several AIC-type and ICOMP-type criteria. We also introduce two new large sample consistent forms of Bozdogan's ICOMP criteria - one of which is robust to model misspecification. To study the empirical performance of the information criteria, we use a well-known SEM simulation protocol, and demonstrate that most of the information-theoretic criteria select the pseudo true model with very high frequencies. We also demonstrate, however, that the performance of AIC is inversely related to the sample size. Finally, we apply the new criteria to select an analytical model for a real dataset from a retail marketing study of consumer behavior. Our results show the versatility of the new proposed method where both the goodness-of fit and the complexity of the model is taken into account in one criterion function.Scientific and Technological Research Council of Turkey (TUBITAK); Department of Statistics, Operations, and Management Science at the University of TennesseeThis research was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) for the first author at the Department of Statistics, Operations, and Management Science at the University of Tennessee as a Visiting Scholar under the supervision of Professor Bozdogan. The first author extends her gratitude and thanks to Professor Bozdogan for the hospitality and conducive research atmosphere provided
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Kategorik ve karma veri setlerinin yapısal eşitlik modellemesinde (YEM) Gifi yaklaşımı kullanımı ve bilgi karmaşıklığı kriteri (ICOMP)
No full textBu çalışmada Yapısal Eşitlik Modelleri’nde (YEM) kategorik, ikili veya karma veri setlerinin analizine ilişkin var olan problemleri çözmek için özgün bir alternatif yaklaşım olarak Gifi yöntemi önerilmiştir. Gifi yönteminde, kategorik değişkenleri nicel hale dönüştürmek için optimal ölçekleme yöntemi kullanılır. Nicelleştirme sürecinde gözlenen değişkendeki bilgi, dönüştürülmüş değişkende aynen korunur. Yani Gifi yöntemi, kategorik değişkenlerin ölçek özelliklerini bozmadan kategorik veriyi sürekli veriye dönüştürür ve bu dönüştürme işleminde herhangi bir bilgi kaybı söz konusu olmaz. Ölçek özellikleri, dönüştürülmüş doğrusal olmayan sürekli Gifi veri uzayında saklanır. Bu nedenle dönüştürme işleminden geriye dönüş mümkündür. Bu işlem, literatürde halen uygulanmakta olan rasgele belirlenmiş başlangıç değerlerini göz ardı eden Gifi sisteme özgün bir özelliktir. Gifi dönüşümünden sonra, çoklu normal dağılım varsayımına dayalı YEM kullanılarak dönüştürülmüş veri seti analiz edilmiştir. Böyle bir yaklaşım YEM’de, kategorik veriler için göz ardı edilen çok değişkenli normal dağılım varsayımını sağlamaktadır. Akaike’nin [1] Akaike Bilgi Kriteri (AIC), Bozdoğan’ın [2] Tutarlı Akaike Bilgi Kriteri (CAIC) ve Bozdoğan’ın [3-7] Bilgi Karmaşıklığı Kriteri (ICOMP) gibi bilgiye dayalı model seçim kriterleri YEM’de uyumun bir ölçümü olarak uygulanmaktadır. Minimum kriter değerini veren model, rakip modeller arasında veriye en iyi uyumlu model olarak seçilir. Bu çalışmada yaşam kalitesinin ölçüldüğü gerçek bir kategorik veri seti kullanılmıştır. Bu veri setine Gifi dönüşüm uygulayarak önerilen yaklaşımın çok yönlülüğü ve esnekliği gösterilmiştir. Ayrıca dönüştürülmüş veri seti üzerinden farklı YEM için model seçim kriter değerleri elde edilmiş ve minimum kriter değerini veren en iyi model belirlenmiştir.This paper introduces and develops a novel and computationally feasible alternative approach to the analysis of categorical, dichotomous, and mixed data sets in structural equation models (SEMs) to overcome currently existing problems. Our approach is based on the Gifi system. The Gifi system uses the optimal scaling methodology to quantify the observed categorical variables. In the quantification process, information in the observed variable is retained in the quantified variable. That is, the Gifi system transforms categorical data to continuous data without destroying the scale properties of the categorical variables. The scaling is thus preserved in the transformed nonlinear continuous Gifi data space. Hence the transformation is invertible. This is one of the unique characteristics of the Gifi system which avoids the arbitrary thresholding specification that is currently practiced and used in the literature. After the Gifi transformation, we analyze the transformed data set using SEM based on the multinormal distributional assumption. Such an approach legitimizes the distributional assumption of multivariate normality in SEM. Information-theoretic model selection criteria such as Akaike's [1] AIC, Bozdogan's [2] Consistent AIC, called CAIC, and the information-theoretic measure of complexity ICOMP criterion of Bozdogan [3-7] are introduced and develop as measures of fit in SEMs. The model with the minimum values of the criteria is selected as the best fitting model among a portfolio of candidate models. We provide a real benchmark numerical example using SEM on a categorical data set which measures the quality of life (QOL) to illustrate the versatility and flexibility of our approach using the Gifi transformations on this data set and fit five alternative SEM models by scoring the model selection criteria
Performance of information complexity criteria in structural equation models with applications
No full textA common problem in structural equation modeling is that of model selection. Many researchers have addressed this problem, but many methods have provided mixed benefits until recently. Akaike's well-known criteria, AIC, has been applied in the context of structural equation modeling, but the effectiveness of many other information criteria have not been studied in a convincing manner. In this paper, we compare the SEM model selection prowess of several AIC-type and ICOMP-type criteria. We also introduce two new large sample consistent forms of Bozdogan's ICOMP criteria - one of which is robust to model misspecification. To study the empirical performance of the information criteria, we use a well-known SEM simulation protocol, and demonstrate that most of the information-theoretic criteria select the "pseudo true" model with very high frequencies. We also demonstrate, however, that the performance of AIC is inversely related to the sample size. Finally, we apply the new criteria to select an analytical model for a real dataset from a retail marketing study of consumer behavior. Our results show the versatility of the new proposed method where both the goodness-of fit and the complexity of the model is taken into account in one criterion function
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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