Bimodal mixture Weibull distribution being a special case of mixture Weibull distribution has been used recently as a suitable model for heterogeneous data sets in many practical applications. The bimodal mixture Weibull term represents a mixture of two Weibull distributions. Although many estimation methods have been proposed for the bimodal mixture Weibull distribution, there is not a comprehensive comparison. This paper presents a detailed comparison of five kinds of numerical methods, such as maximum likelihood estimation, least-squares method, method of moments, method of logarithmic moments and percentile method (PM) in terms of several criteria by simulation study. Also parameter estimation methods are applied to real data.

The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error. Choosing the optimal value of the biasing parameter k is vital in ridge regression in terms of bias‐variance trade off. Since the theoretical comparisons among the ridge estimators are not possible, it is general practice to carry out a Monte Carlo study to compare them. When the Monte Carlo designs on the existing ridge estimators are examined, it is seen that the performances of the ridge estimators are only considered for the same level of relationship between the independent variables. However, it is more likely to encounter different levels of relationships between the independent variables in real data sets. In this study, a new type iterative ridge estimator is proposed based on a modified form of the estimated mean square error function. Furthermore, a novel search algorithm is provided to achieve the estimations. The performance of the proposed estimator is compared with that of the ordinary least squares estimator and existing 18 ridge estimators through an extensive Monte Carlo design. In the design of the Monte Carlo, both data generation techniques were taken into account, based on the constant and varying correlation levels between the independent variables. Two illustrative real data examples are presented. The proposed estimator outperforms the existing estimators in the sense of the mean squared error for both data generating types. Moreover, it is also superior with respect to the k‐fold cross‐validation method in the real data examples.

Karar vericiler için özellikle iki kategorili(pozitif/negatif) sınıflandırma problemlerinde alternatif testler arasından hangisinin kullanılacağının belirlenmesi önemli bir problemdir. Tıpta hastalık tanısı konulması amacıyla kullanılan tanı testleri başta olmak üzere bir sınıflandırıcının iki seçenek arasındaki seçim performansının değerlendirilmesi araştırmacılar için önemli bir sorundur. ROC (Receiver Operating Characteristic) analizi bu testlerin performanslarını detaylı biçimde haritalayabilen ve karar vermede önemli olan kesme değerinin bulunmasına yarayan bir istatistiksel yöntemdir. Literatürde mevcut kesme değeri hesabı duyarlılık ve özgüllük değerlerini kullanmaktadır. Duyarlılık ve özgüllük değerleri kadar doğruluk oranı da kesme değeri hesaplanmasında dikkate alınması gereken bir diğer ölçüttür. Bu çalışmada doğruluk oranı baz alınarak alternatif kesme değerlerinin hesaplanması üzerinde duruldu. Literatürde mevcut olan Youden indeksi, köşe uzaklığı indeksi, uyum olasılığı indeksi ve birlik indeksi yöntemleri doğruluk oranı ile uyarlanarak yeni yöntemler önerildi. Pozitif ve negatif olguların simetrik dağılıma sahip olduğu durumlar için eşit ağırlıklı olarak mevcut ve uyarlanmış indeksler karşılaştırılmıştır. Toplam 8 ayrı kesme değerinin performansları R programında Monte-Carlo simülasyonu ile 20000 deneme üzerinden değerlendirilmiştir. Elde edilen simülasyon sonuçlarına göre önerilen uyarlanmış yöntemlerin en az mevcut olan yöntemler kadar doğruluk oranına sahip olduğu gösterilmiştir.

In this research, it is aimed to develop a scale for the use of cloud technologies in education. The sample group of the study consists of 415 preservice teachers who are studying at universities in Konya. For the validity and reliability analyses of the scale, the sample group consisting of 415 units was randomly allocated ( =208 and =207) sub-samples, the first sample was used for explanatory factor analysis and the second sample was used for confirmatory factor analysis. As a result of the explanatory factor analysis of the data obtained from the first group, 6-item scale consists of motivation and interaction sub-dimensions. Interaction dimension of total variability alone explains 35.89% and motivation dimension explains 33.56%. Factor loads for the sub-dimensions ranged between 0.74 and 0.83. The internal consistency coefficient was 0.83 for Cronbach alpha, 0.77 for motivation subscale and 0.79 for interaction subscale. For the second sample, it was found that the model formed by the two-factor structure of the scale was appropriate according to the fit indices obtained from the confirmatory factor analysis results. As a result, Cloud Technologies Usage scale was found to be a valid and reliable measurement tool.