1,720,986 research outputs found
Ortalama ötelemeli sapan değer modelinde M-tahmin yöntemi ve konik programlama ile parametre tahmini
Full text linkBu tez çalışması, sapan değerlerle bozulmuş bir veri kümesi üzerinde, öncelikle sapan değerlerin teşhis edilmesi, ardından sapan değerin sahip olduğu bilgiyi göz ardı etmemek için Ortalama Ötelemeli Sapan Değer (OÖSD) modelin oluşturulmasını amaçlamaktadır. Oluşturulan modelin parametreleri, çok önemli ve direkt olmayan sağlam bir sapan değer yöntemi olan M-tahmin yöntemi kullanılarak tahmin edildi. Doğrusal regresyon modelindeki sapan değer probleminin üstesinden gelmek için, M-tahmin edicilerin sağlamlığını Tikhonov Düzenleme ve En Küçük Mutlak Küçültme Ve Operatör Seçimi (LASSO)'nun kararlılığı ile birleştiren iki yöntem geliştirildi. Bunun için öncelikle Huber tipi fonksiyon ile M-tahmin yöntemine dayanarak Tikhonov düzenleme ve LASSO problemi OÖSD modeline uyduruldu. Daha sonra bu problemin çözümü için iç noktalar yöntemini kullanan konik karesel programlama (CQP) yöntemi önerildi. Burada amaç, modeli sapan değerlerin olumsuz etkilerinden korumaktır. Ayrıca, verilen problem için en uygun ayar sabitinin nasıl hesaplanacağı üzerine öneri getirildi. Daha sonra oluşturulan modeller amonyağı nitrik aside oksitleyen bir düzeneğin 21 günlük çalışması sonucu elde edilen veri grubuna uygulandı. Bu uygulamada MATLAB ve MOSEK paket programları kullanılmıştır. Anahtar Kelimeler: Sapan değerler, Tikhonov Düzenleme, LASSO, M-tahmin, Sürekli En iyilemeThis thesis aims the constitution of Mean Shift Outlier Model (MSOM) on a data set contaminated with outliers, after detection of outliers to not disregard the information possessed by the outliers. The parameters of this model were estimated by using M-estimation method which is a very important and indirect robust outlier detection method. Robustness of M-estimators were combined with the efficiency of Tikhonov Regularization and Least Absolute Shrinkage and Selection Operator (LASSO) to overcome the problem of outliers in linear regression model. Therefore, firstly Tikhonov Regularization and LASSO problems were applied to the Mean Shift Outlier Model (MSOM) based on Huber type M-estimation method. Then, the conic quadratic programming method that uses the interior point method was proposed for solving this problem. Here, The aim is to protect the model from the negative effects of outliers. Moreover, a proposal was introduced on how the calculation of the optimal tuning constant for the given problem. Then, the established models were applied to the data set which was obtained by 21-day operation of a plant for the oxidation of ammonia to nitric acid. For that application MATLAB and MOSEK software packages were used. Keywords: Outliers, Tikhonov Regularization, LASSO, M-estimation, Contunious Optimizatio
A comparison of shrinkage methods in linear regression
No full textİstatistiksel araştırmalar ve analizlerde veri kümesine dayalı olarak elde edilen regresyon modelleri çok önemli rol oynar. Bu modellerden biride çoklu doğrusal regresyon modelidir. Ancak çoklu doğrusal regresyon analizinde, iç ilişki problemi ortaya çıktığında en küçük kareler tahmin edicileri yansız olmasına rağmen büyük varyanslı olarak elde edilirler. Bu durum yapılan istatistiksel analizin doğruluğunu olumsuz yönde etkilemektedir. İstatistiksel araştırmalarda küçültme yöntemlerinin birinin kullanılması ile tahmin edicilerin varyansı küçültülerek iç ilişki problem ortadan kalkabilmektedir. Bu tez çalışmasında iç ilişki problemi ele alınarak, iç ilişkinin giderilmesi için Ridge Regresyon, LASSO, Elastik Net ve Liu gibi küçültme yöntemleri incelenmiştir.Uygulama çalışması olarak da, iç ilişkinin saptandığı veri üzerinden adı geçen küçültme yöntemlerinin hata kareler toplamı, hata kareler ortalamaları ve belirleme katsayıları karşılaştırılmıştır. Bu karşılaştırmalarda yukarıdaki küçültme yöntemlerinin tamamı en küçük karelere göre daha iyi sonuçlar vermekle birlikte, LASSO hem en küçük karelere göre hem de ele alınan bütün küçültme yöntemlerine göre daha iyi sonuçlar vermiştir. Sonuç olarak iç ilişki problemin varlığı durumunda en küçük kareler yöntemi yerine küçültme yöntemlerinin, özelliklede LASSO nun kullanılmasının daha doğru sonuçlar verdiği kararına varılmıştır.Regression models based on data set play a very important role in statistical research and analysis. One of these models is multiple linear regression model. However, in multiple linear regression analysis, when multicolinearity problem arises, the least squares estimators are obtained with large variance although they are unbiased. This situation negatively affects the accuracy of statistical analysis. In statistical research, by using one of the shrinkage methods, the variance of the estimators can be reduced and the multicolinearity problem can be eliminated. In this thesis, colinearity problem is investigated and Ridge Regression, LASSO, Elastic Net and Liu shrinkage methods are examined to eliminate multicolinearity problem. As an application study, sum of squares errors, mean squares erros mean and determination coefficients of shrikage methods were compared over the data in which colinearity was determined. In these comparisons, all of the above shrinkage methods yielded better results than the least squares, while the LASSO yielded better results than both the least squares and all the shrinkage methods discussed for this data set. As a result, it is concluded that the shrinkage methods instead of least squares method, especially LASSO, give more accurate statistical results in case of multicolinearity problem
Estimation in the partially nonlinear model by continuous optimization
No full textA useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model
On curvature measurements of the nonlinear errors in variable models by application study
No full textRelative curvature measurements are of great importance from a practical point of view since it determines the validity of the linearized approximation used in estimation problems for nonlinear regression models. But, these measurements can be negatively affected when an explanatory variable contains a measurement error as well as response variables and can prevent accurate inferences. In our study, we considered the curvature measurement of nonlinear errors in variable models to investigate adequacy of the linear approximation in case the explanatory variables are subjected to measurement error and how the parameter estimation problem is affected by this error, using the geometric concepts such as parameter-effects and intrinsic curvatures of the model function. Then, for the two cases of the explanatory variable, curvature calculations and statistical inferences were made on the chemical model called Michaelis-Menten, in which the rate of reaction against a substrate concentration is measured, by using different data sets
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No full textKumaşlara uygulanan fiziksel test ölçüm yöntemlerinin karşılaştırmalı olarak incelenmesi
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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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