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الاتساق الذاتي وتحليل المكونات الرئيسة == The Self - Consistency And Principal Component'S Analysis
Author name:
سحر طارق محمود الرحيم
Supervisor name:
رفعت لازم مشعل الخميسي
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T289 - p.pdf
Abstract:
تعني عبارة " الاتساق - الذاتي " اختبارا لمحور المكونات الرئيسة عندما يرتكز التوزيع على المحور الذي يعطي تقريبا بسيطا للتوزيع الاصلي متعدد المتغيرات.جرت عملية الاختبار بفحص نقص المطابقة Lack of Fit Test في ضوء طريقة البوتستراب (Bootstrap ) اي ( تكرار ا | The term " Self - Consistency " means ; examine the Self - consistency of a principal components axis : when a distribution is centered on a principal component axis, which provides a simple straight line approximation to a multivariate distribution.A principal component axis of a random vector X; is self - consistent if each point on the axis corresponds to the mean of X given that X projects orthogonally on to that point.this examination is done by " Bootstrap - Lack of fit " test applied on to two kinds of data set (real and simulation) with different samples (small, medium, large),depending on the probability values for getting levels of significance (to assess the self - consistency of principal component axis) which is based on the frame work of the data set, whether it will be true (towards to the elliptical form for the multivariate normal distribution) or generated data set using the restrictive simulation ( towards to the asymptotic spherical form for multivariate normal distribution ) that's for estimating the self - consistency by the hypothesis ;Hk : Yk is self - consistent for Xprincipal component analysis for factor analysis is use with the aim of analyzing the large number of variables and their effective factors by the factor rotation procedure which is assumes also the multivariate normal distribution, with proposed technique, based on the smallest column on X for different sizes of samples ( small " 5 - 20 " , medium " 25 - 50 ", and large " 60 - 100 ").High level of fit, is the result of " Bootstrap - Lack of fit" test in all sample size for all research variable's; with little difference and the best for male group of the results for female group on the size ( 5 - 35 ) , conversely for another size's.and also we saw highest level of self - consistency in the small sample size ( 5 - 20 ) for true data set using direct oblimin rotation method for male group, in the medium sample size ( 25 - 50) using the same method with direct method( None) for female group.In generated data set by normal restricted simulation technique, also high level of fit is the result of " Bootstrap - Lack of fit " test in all sample size's and for all sample Size's (small, medium, large) and for tow group's (male, female), which indicate to the usefulness of using the restricted simulation technique to take pure data set, and the model with high level of stationary.also we saw highest level of self - consistency in the different sample size's, using the varimax rotation method for tow Group's (male, female), the two method's ( none, direct oblimin rotation) in some of sample size's which refers to corresponding the normal stat with varimax rotation method ,that is well known of its object for simplified explaining the factor matrix by maximization the loading variance
