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Statistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective" by Richard A. Berk, in its 2nd edition, provides a comprehensive exploration of advanced statistical learning techniques with a focus on regression. The book covers fundamental concepts and practical applications, emphasizing the integration of theoretical insights with real-world data analysis. Topics include various regression models such as linear, logistic, and generalized linear models, alongside methods for model evaluation and variable selection. It introduces regularization techniques like ridge and lasso regression to enhance model robustness and predictive accuracy. The book also addresses the challenges of balancing prediction accuracy and model interpretability, crucial for effective decision-making in diverse fields including social sciences. Through detailed examples and case studies, this edition equips readers with the knowledge and skills necessary to apply sophisticated statistical learning methods effectively in research and practical applications.

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Informasi Detail
Judul Seri
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No. Panggil
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Penerbit
Switzerland : Springer International Publishing.,
Deskripsi Fisik
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Bahasa
English
ISBN/ISSN
978-3-319-44047-7
Klasifikasi
NONE
Tipe Isi
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Tipe Media
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Tipe Pembawa
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Edisi
2
Subjek
Regression analysis
Supervised Learning
Model Evaluation
Variable Selection
Regularization Methods
Prediction and Inference
Applications in Social Sciences
Info Detail Spesifik
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Pernyataan Tanggungjawab
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