دورية أكاديمية
Modelling Right-Censored Data with Partially Linear Model and Feed Forward Neural Networks: A Methodological Study ; Sağdan-Sansürlü Verilerin Kısmi Doğrusal Model ve İleri Beslemeli Yapay Sinir Ağları ile Modellenmesi: Metodolojik Bir Çalışma
| العنوان: | Modelling Right-Censored Data with Partially Linear Model and Feed Forward Neural Networks: A Methodological Study ; Sağdan-Sansürlü Verilerin Kısmi Doğrusal Model ve İleri Beslemeli Yapay Sinir Ağları ile Modellenmesi: Metodolojik Bir Çalışma |
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| المؤلفون: | Bal, Çağatay, Yılmaz, Ersin |
| المساهمون: | MÜ, Fen Fakültesi, İstatistik Bölümü, orcid:0000-0002-7823-2712, orcid:0000-0002-9871-4700, Bal, Çağatay, Yılmaz, Ersin |
| بيانات النشر: | Türkiye klinikleri |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Feed forward neural networks, Smoothing splines, Partially linear models, Right-censored data, İleri beslemeli sinir ağları, Splayn düzleştirme, Kısmi doğrusal modeller, Sağdan-sansürlü veri |
| الوصف: | Objective: Modeling right-censored data becomes a challenging task in survival analysis, due to having an incomplete data structure. When the response variable is rightcensored, classical estimation methods cannot be used directly. Therefore, the censorship problem should be solved before the modeling process. The purpose of this study is to solve the censorship problem with synthetic data transformation and to make a comparison between the partial linear model (PLM) and feed forward neural networks (FFNN), two popular modeling procedures in recent years, in terms of model residuals. Thus, it is to study the behavior of methods. Material and Methods: This paper aims to estimate the effects of explanatory variables on a right-censored response variable whose distribution is unknown by two different methods, PLM and FFNN based on the spline smoothing method. The spline smoothing method is a mathematical approximation method used in PLM estimation. FFNN is a machine learning method that has become very popular recently and produces satisfactory models. To overcome the censorship problem, the right-censored response variable for the two mentioned methods has been replaced with synthetic data. Synthetic data transformation is a widely used censorship resolution method that allows censorship presence to be included in the prediction process. Results: To achieve the aim of the study, both simulation and real data studies were carried out and the results were presented. Conclusion: Kidney weakness data is used as an example of real data. When the results are examined, it is seen that FFNN is superior to PLM in both numerical studies. ; Amaç: Eksik bir veri yapısına sahip olması nedeniyle sağdansansürlenmiş verilerin modellenmesi, sağkalım analizinde zor bir işlemdir. Yanıt değişkeni sağdan sansürlendiğinde, klasik tahmin yöntemleri doğrudan kullanılamaz. Bu nedenle modelleme sürecinden önce sansür problemi çözülmelidir. Bu çalışmanın amacı, sansür problemini sentetik veri dönüşüm ile çözerek literatürde son yıllarda ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Türkiye Klinikleri Biyoistatistik Dergisi; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; BAL C, YILMAZ E (2022). Modelling Right-Censored Data with Partially Linear Model and Feed Forward Neural Networks: A Methodological Study. Türkiye Klinikleri Biyoistatistik Dergisi, 14(2), 90 - 102. 10.5336/biostatic.2022-89354; 1308-7894 / 2146-8877; file:///C:/Users/Aidata/Downloads/document-39.pdf; https://hdl.handle.net/20.500.12809/10526Test; 14; 90; 102 |
| الإتاحة: | https://doi.org/20.500.12809/10526Test https://hdl.handle.net/20.500.12809/10526Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.41AA9DF5 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |