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الرئيسية > Discriminant Analysis for Corr...>الوصف

Discriminant Analysis for Correlated Data

المصدر: مجلة البحوث المالية والتجارية
الناشر: جامعة بورسعيد - كلية التجارة
المؤلف الرئيسي: El-Sayed, Ahmed Mohamed Mohamed (Author)
المجلد/العدد: ع3
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2019
الصفحات: 280 - 298
DOI: 10.21608/JSST.2019.61404
ISSN: 2090-5327
رقم MD: 1028797
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
الإحصاء | تحليل التمييز الخطي | الانحدار اللوجستي
كلمات المؤلف المفتاحية:
Linear Discriminant Analysis LDA | Quadratic Discriminant Analysis QDA | Canonical Discriminant Analysis CDA | Logistic Regression LR | Missclassification | Prior Probabilities | Apparent Errors
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المستخلص: The correlated data are of great importance in the practical life, for example we may want to track the case of a patient after taking a treatment for consecutive periods of time. Also, we may want to track a disease with the members of a certain family. Finally, we want to know the development of a certain disease with a patient for different periods of time, …, etc. the binary data, (for example a person smokes = 1, a person does not smoke = 0), (female = 1, male = 0), (the effect of treatment is active =1, placebo = 0), is a type of the correlated data. In this paper, we are applying the classification and discriminant methods on the correlated data (some of them are binary data and the other are not). Usually we are using a one dependent variable and then we classify it into two or more classes. Contrary to what is traditionally followed, in this paper we will deal with two dependent correlated categorical variables each one is divided into two classes. The methods of discriminant analysis are investigated in this paper when we are dealing with the independent and dependent variables both of them is correlated variables. Three methods of discriminant analysis are presented for these correlated data. Finally, different packages of R program are used to classify and discriminant the practical data. The data are named respiratory disorder data, these data are containing binary and continuous correlated variables. We have applied different methods of classification and discriminant analysis on these data.
ISSN: 2090-5327

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