Aykırı Değer Varlığında Cox Regresyon Analizi için Yeni Bir Yaklaşım

Aykırı değerin varlığı Cox regresyonun en önemli varsayımlarından olan orantılı hazard varsayımının ihlal olmasına ve doğru olmayan tahminlerin ortaya çıkmasına neden olur. Çünkü aykırı değerler, modelin parametrelerinin tahminleri üzerinde güçlü bir etkiye sahiptirler. Bu nedenle veri kümesinde aykırı değerlerin olması araştırmacılar için bir problemdir. Bu çalışmada, aykırı değerlerden dolayı orantılı hazard varsayımının ihlal edilmesi sonucu ortaya çıkan problemin çözümü farklı bir bakış açısıyla ele alınmıştır. Buna göre aykırı değer problemi bir kayıp değer problemi gibi düşünülüp çoklu değer atama yöntemi kullanılarak çözülmüştür. Sonuç olarak Cox regresyon analizinin orantılı hazard varsayımı tehlike altında ise kayıp veri problemlerinde üstün bir performans gösteren çoklu değer atama yöntemi ile elde edilen tahminler kullanılarak problemin çözülmesi önerilmektedir

Robust Principal Component Analysis Based on Modified Minimum Covariance Determinant in the Presence of Outliers

Principal component analysis (PCA) is not resistant to outliers existing in multivariate data sets. The results which are obtained by using classical PCA are far from real values in the presence of outliers. Therefore, using robust versions of PCA is favorable. The easiest way to obtain robust principal components is to replace classical estimates of the location and scale parameters with their robust versions. Robust estimations of location and scale parameters can be found with minimum covariance determinant (MCD) providing high breakdown point. In this study, algorithm of MCD is modified using Jackknife resampling approach and results of this modification are examined. Proposed robust principal component analysis (RPCA) based on modified MCD (MMCD) method that is modified using Jaccknife resampling are evaluated over two real data with different outlier ratios. In the light of obtained results, it can be said that RPCA based on MMCD is better than RPCA based on MCD in the presence of outliers

Robust Principal Component Analysis based on Fuzzy Coded Data

In the presence of outliers in the dataset, the principal component analysis method, like many of the classical statistical methods, is severely affected. For this reason, if there are outliers in dataset, researchers tend to use alternative methods. Use of fuzzy and robust approaches is the leading choice among these methods. In this study, a new approach to robust fuzzy principal component analysis is proposed. This approach combines the power of both robust and fuzzy methods at the same time and collects these two approaches under the framework of principal component analysis. The performance of proposed approach called robust principal component analysis based on fuzzy coded data is examined through a set of artificial dataset that are generated by considering three different scenarios and a real dataset to observe how it is affected by the increase in sample size and changes in the rate of outliers. In light of the study's findings, it is seen that the proposed approach gives better results than the ones in the classical and robust principal component analysis in the presence of outliers in dataset

Visualizing Diagnostic of Multicollinearity: Tableplot and Biplot Methods

In this study we tried to bring out the importance of two methods used for visualizing multicollinearity diagnostics. The first method is called Tableplot (see Friendly and Kwan, 2009). Through this technique, multicollinearity diagnostics ideas are combined with a visualizing approach. The second method is called Biplot (see Gabriel, 1971; Gower and Hand, 1996). The biplot technique is used for demonstrating significant properties of multivariate data structure. Both of these approaches have been examined by one real and three artificial data. Results indicate that Biplot method is preferred to instead of Tableplot for too large condition indexes

Cox Oransal Hazard Modelinde Kayıp Veri Analizi Yöntemlerinin Karşılaştırılması

Data with missing value are common in clinical studies. This study investigated to assess the effects of different missing data analysis techniques on the performance of Cox proportional hazard model. Material and Methods: In order to see how sample size and missing rate effect the missing data analysis techniques, we derived the survival data with 25, 50 and 100 sample sizes. Some elements of the survival data with different sample size were deleted in different rates under MAR (Missing at Random) assumption to generate incomplete data sets which had 5%, 10%, 20% and 40% missing value for each data. Data sets with missing values were completed by five missing data analysis techniques (complete case Analysis-CCA, mean imputation, regression imputation-REG, expectation maximization-EM algorithm, multiple imputation-MI). The new completed data sets were analyzed by Cox proportional hazard model and their results were compared with results of original data. Results: The difference between the techniques grew for increasing missing rate and while the sample size increased the methods were similar to each other. CCA was the most affected from sample size. The estimates from the methods REG, EM and MI were very similar to each other and real value. Conclusion: Multiple imputation method as impute more than one value for each missing value should be preferred instead of single imputation methods as impute only one value for each missing value

Measuring inequalities in the distribution of health workers by bi-plot approach: The case of Turkey

Optimal planning of the health workers is of vital importance for a country. Distribution of health workers among provinces in emerging markets is an important development criterion. In this study, biplot graphical approach is used to determine the distribution of health workers. The results of biplot analysis point out that the distribution of the healthcare staff in Turkey is unbalanced. The number of health workers should be planned and considered according to the desire, need, population, target and workload criteria. The new employment opportunities should be created and the workers should be encouraged to serve in low income regions by providing better conditions

Türkiye'de Yeni Tanımlı Sanayi Üretim Endeksi ve Alt Sanayi Gruplarındaki Büyümenin Biplot Yöntemi ile İncelenmesi

Bu çalışmada yeni tanımlı Sanayi Üretim Endeksi (2005=100)’nin ana sanayi grupları aralarındaki ilişki biplot tekniği ve yakınsama analizleriyle incelenmektedir. Kovaryans biplot grafiği ve yakınsama testi sonuçlarına göre imalat, toplam, dayanıksız, ara, dayanıklı ve sermaye malları üretimi endekslerindeki büyümeler arasında pozitif yönde bir ilişki olduğu tespit edilmiştir. Ancak ana sanayi grupları büyümeleri arasındaki korelasyonlar heterojen bir yapı göstermektedir