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Journals / İstatistik Araştırma Dergisi / 2007 / Cilt: 5 Sayı: 2

Introduction to Partial Least Squares Algorithms NIPALS and SIMPLS and an Application

Kısmi En Küçük Kareler Algoritmalarından NIPALS ile SIMPLS'ın Tanıtımı ve Bir Uygulama

Elif Bulut(DOKUZ EYLÜL ÜNİVERSİTESİ, FEN FAKÜLTESİ, İSTATİSTİK BÖLÜMÜ)
Aylin Alın(DOKUZ EYLÜL ÜNİVERSİTESİ, FEN FAKÜLTESİ, İSTATİSTİK BÖLÜMÜ)
Pages
52–68
Publication Date
2007-12-14
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, NIPALS which is the first and basic algorithm of PLS and SIMPLS developed by De Jong in 1993 will be described and illustrated on an health data set.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Partial least squares (PLS) is used for obtaining the smallest number of orthogonal latent variables (component) that explain the great amount of variation in the original independent variables which have multicollinearity.
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Key Findings & Data:Since it is applicaple to various disciplines different algorithms have been developed for PLS.
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Conclusion & Impact:In this study, NIPALS which is the first and basic algorithm of PLS and SIMPLS developed by De Jong in 1993 will be described and illustrated on an health data set.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Çoklu Doğrusal Bağlantı · Kısmi En Küçük Kareler · NIPALS

Abstract

Partial least squares (PLS) is used for obtaining the smallest number of orthogonal latent variables (component) that explain the great amount of variation in the original independent variables which have multicollinearity. Since it is applicaple to various disciplines different algorithms have been developed for PLS. In this study, NIPALS which is the first and basic algorithm of PLS and SIMPLS developed by De Jong in 1993 will be described and illustrated on an health data set.

Özet

Kısmi en küçük kareler (KEKK) yöntemi, aralarında çoklu doğrusal bağlantı bulunan açıklayıcı değişkenler içerisinden, bu değişkenlerdeki değişimin büyük kısmını açıklayacak, dik ve daha az sayıda gizli değişkeni (bileşen) bulmakla ilgilenir. Farklı alanlarda farklı amaçlar doğrultusunda kullanımından dolayı, KEKK için farklı algoritmalar ortaya çıkarılmıştır. Bu çalışmada KEKK için en temel algoritma olan NIPALS, 1993 yılında De Jong tarafından geliştirilen ve yaygın olarak kullanılan SIMPLS algoritmalarına değinilmiş, bir örnek üzerinde uygulama gösterilmiştir.

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Cite This Article

Bulut, E., Alın, A. (2007). Introduction to Partial Least Squares Algorithms NIPALS and SIMPLS and an Application. İstatistik Araştırma Dergisi, Cilt: 5 Sayı: 2, 52–68. https://ajindex.com/articles/1617896

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References (8)

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