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مقارنة بعض الطرائق اللبية في تقدير نماذج الانحدار اللامعلمي بوجود بيانات تامة وغير تامة == A Comparison of Some Estimation Methods For Kernel Models In Complete And Incomplete Data
اسم المؤلف:
سعد كاظم حمزة
اسم المشرف:
ظافر حسين رشيد النجار | مناف يوسف
الموضوع العام:
الادارة والاقتصاد
السنة:
2009
الموضوع الدقيق:
الاحصاء
الدرجة:
ماجستير
الجامعة:
جامعة بغداد
اللغة:
العربية
مكان الجامعة:
بغداد
الصفحات الاولى:
📎 07T3729 - p.pdf
المستخلص:
ان استخدام النماذج المعلمية يتطلب العديد من الشروط الاولية التي يجب توافرها لكي تكون قراءة هذه النماذج قراءة صحيحة كما تتطلب وجود بيانات من النوع الكمي الامر الذي دفع الباحثون الى البحث عن نماذج اقل صرامة من النماذج المعلمية وتمثلت هذه النماذج بالنماذج ال | Using of parametric models require a number of preliminary conditions that should be available to make the reading of these models right as well as the presence of quantitative data which motivated the researchers to search for models with lesser terms than the parametric models represented by nonparametric models. One of nonparametric methods important for estimating nonparametric regression function known as (kernel estimation) to be used for estimating any statistical function which is (smooth estimator) free of disorders modifying the observations and approximating estimated regression function to real nonparametric regression function. Hence, the researcher showed some kernel methods to estimate the nonparametric regression function in both cases complete data (FNW, FLLS, VNW, VLLS) and incomplete data (FSNW, VSNW, FINW, VINW, FSLLS, VSLLS, FILLS, VILLS) and compared them through the simulation method as well as using different models and different sizes of samples and variants. From noticing the simulation results, it was shown that the best smoother was (FLLS) by using the first model, while by using the second model, it was shown that the best smoother was (VLLS) and in the third model was (VNW). In case of incomplete data, the best smoother was (VSLLS) by using the first and second models while by using the third model, the best smoother was (VSNW)
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