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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Linear Models Theory for Data Science

Linear Models Theory for Data Science

Hong, Yili
Morgan, John P.
Deng, Xinwei

Hardcover

Series: Springer Texts in Statistics

ApplicationsGeneral ComputersProbability & Statistics

PREORDER - Expected ship date December 3, 2026

ISBN10: 3032385180
ISBN13: 9783032385185
Publisher: Springer
Published: Dec 3 2026
Pages: 488
Language: English

This book provides a holistic view of linear models as the foundation for many statistical and machine learning methods. It covers the theory of the standard linear model, then explores extensions of that model and the use of linear predictors in a broad range of more computationally demanding modeling techniques. Optimization procedures that underlie this evolutionary tree of models are also covered, while highlighting the central role of the linear model in statistics, machine learning, and data science. The early chapters cover linear model formulation, matrix algebra, distribution theory, and estimation and inference for the standard linear model. Following an overview of optimization techniques, the later chapters cover models for correlated responses, regularization, models for non-normal responses, predictive models, and models for inherently nonlinear relationships. Throughout, the emphasis is on the interrelated ideas connecting these many methods that collectively address a wide range of modeling situations.

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