Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error
المؤلفون المشاركون
Jäntschi, Lorentz
Bálint, Donatella
Bolboacă, Sorana D.
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-12-07
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest.
Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations.
A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors.
The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors).
The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error.
Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jäntschi, Lorentz& Bálint, Donatella& Bolboacă, Sorana D.. 2016. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100212
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jäntschi, Lorentz…[et al.]. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100212
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jäntschi, Lorentz& Bálint, Donatella& Bolboacă, Sorana D.. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100212
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1100212
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر