A Non-Least Squares Approach to Linear Models 1st Edition

Grab your attention with a fresh take on regression: A Non-Least Squares Approach to Linear Models, 1st Edition by Mike Jacroux challenges conventional methods and invites readers to rethink how linear models are built, interpreted, and applied across disciplines.

Dive into a clear, rigorous exploration of alternatives to ordinary least squares—robust estimation, weighted procedures, and adaptive strategies—explained with practical intuition and mathematically sound reasoning. Jacroux balances theory and application, demonstrating when non-least-squares techniques outperform traditional regression, how to diagnose model weaknesses, and how to implement solutions that improve inference and prediction in real-world datasets.

Perfect for statisticians, data scientists, econometricians, engineers, and graduate students across the US, UK, Europe, and beyond, this book equips readers to tackle heteroscedasticity, outliers, and model misspecification with confidence. Case studies and worked examples illuminate complex ideas without sacrificing rigor, making advanced concepts accessible to practitioners and researchers seeking robust alternatives.

If you want a resource that expands your toolkit beyond ordinary least squares and enhances model reliability in applied settings, this book delivers. Its focused approach helps you understand when and why to choose non-standard estimation methods and how to implement them effectively in modern statistical workflows.

Enhance your modeling practice—order your copy of A Non-Least Squares Approach to Linear Models, 1st Edition by Mike Jacroux today and start building more resilient, insightful linear models for research and industry alike.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.