P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a range of standard and non-standard applications with code in R for professionals and researchers looking for a simple, flexible and powerful smoothing tool.
P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a range of standard and non-standard applications with code in R for professionals and researchers looking for a simple, flexible and powerful smoothing tool.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Paul H. C. Eilers is Professor Emeritus of Genetical Statistics at the Erasmus University Medical Center Rotterdam. He received his Ph.D. in biostatistics. His research interests include high-throughput genomic data analysis, chemometrics, smoothing, longitudinal data analysis, survival analysis, and statistical computing. He has published extensively on these subjects.
Inhaltsangabe
1. Introduction 2. Bases, penalties, and likelihoods 3. Optimal smoothing in action 4. Multidimensional smoothing 5. Smoothing of scale and shape 6. Complex counts and composite links 7. Signal regression 8. Special subjects A. P-splines for the impatient B. P-splines and competitors C. Computational details D. Array algorithms E. Mixed model equations F. Standard errors in detail G. The website.
1. Introduction 2. Bases, penalties, and likelihoods 3. Optimal smoothing in action 4. Multidimensional smoothing 5. Smoothing of scale and shape 6. Complex counts and composite links 7. Signal regression 8. Special subjects A. P-splines for the impatient B. P-splines and competitors C. Computational details D. Array algorithms E. Mixed model equations F. Standard errors in detail G. The website.
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