Statistical Learning from a Regression Perspective
«“It could readily be a textbook for an applications-focused course at the graduate level as each chapter comes with exercises … . Examples with accompanying code also appear throughout the chapters which provide a scaffold for getting started … . Berk’s pragmatic advice will serve a wide audience from practitioners to educators to students.” (Sara Stoudt, MAA Reviews, December 12, 2021)»
This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Les mer
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The third edition considers significant advances in recent years, among which are:
the development of overarching, conceptual frameworks for statistical learning;
the impact of "big data" on statistical learning;
the nature and consequences of post-model selection statistical inference;
deep learning in various forms;
the special challenges to statistical inference posed by statistical learning;
the fundamental connections between data collection and data analysis;
interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy.
This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
Detaljer
- Forlag
- Springer Nature Switzerland AG
- Innbinding
- Paperback
- Språk
- Engelsk
- Sider
- 433
- ISBN
- 9783030429232
- Utgave
- 3. utg.
- Utgivelsesår
- 2021
- Format
- 24 x 16 cm
Anmeldelser
«“It could readily be a textbook for an applications-focused course at the graduate level as each chapter comes with exercises … . Examples with accompanying code also appear throughout the chapters which provide a scaffold for getting started … . Berk’s pragmatic advice will serve a wide audience from practitioners to educators to students.” (Sara Stoudt, MAA Reviews, December 12, 2021)»