ÖzBu çalışmada Weibull dağılımına sahip ilerleyen tür tip 2 sansürlü örneklemlerde parametre tahmini probleminde Newton yöntemine alternatif bir çözüm önerilmiştir. Newton yöntemi en çok olabilirlik tahmininde sıklıkla kullanılmaktadır. Newton yöntemi popüler olmasına rağmen en büyük dezavantajı en az iki kez türevlenebilir fonksiyonlar için kullanılabilmesidir. Olabilirlik fonksiyonu sansürlü örneklemlerde tam örneklemlere göre fonksiyonel olarak daha kompleks bir yapıda olduğundan, türev ve diğer hesaplamalar nispeten daha karışıktır. Bu çalışmada en çok olabilirlik yönteminde elde edilen denklem sisteminin çözümü için Newton metodunun kullanımındaki kısıtlamalara bir alternatif olarak Genetik Algoritma önerilmiştir. Detaylı bir simülasyon çalışması yardımıyla yan ve hata kareler ortalaması ile iki yöntemin performansları değerlendirilmiştir. Simülasyon sonuçlarına göre önerilen yöntemin karşılaştırılan tüm durumlar için ölçek parametresi için daha iyi sonuçlar verdiği, şekil parametresi için ise yanlar açısından sonuçların benzer olduğu ancak hata kareler ortalamasına göre bazı sansür şemaları için Newton yönteminin iyi sonuç verdiği bulunmuştur.
AbstractIn this study we suggested an alternative solution to the parameter estimation problem of the Weibull distribution based on progressively Type-II censored samples with Newton method. Newton is one of the widely used methods for solving the system of equations especially in maximum likelihood estimation. Even though it is popular, the biggest disadvantage of the Newton method is that it is a valid method for only functions that derivativable at least two times. Since the likelihood functions are in more complex form for censored samples than in full samples, calculations of derivatives and related processes are more complicated. We proposed to use the Genetic Algorithm an alternative to the limitations of the Newton method in solution of system of equations in maximum likelihood estimation. Performance of these methods are evaluated by the simulated bias and mean square error criteria by an intensive simulation study. Simulation results of the study showed that the suggested method give better results than Newton method for scale parameter for all conditions. Also shape parameter results for simulated biases are similar for GA and Newton method but Newton has better mean squared error values for some censoring schemes.
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.
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