Statistical inference
by Paul H. Garthwaite
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Adopting a broad view of statistical inference, this text concentrates on what various techniques do, with mathematical proofs kept to a minimum. The approach is rigorous, but will be accessible to final year undergraduates. Classical approaches to point estimation, hypothesis testing and interval estimation are all covered thoroughly, with recent developments outlined. Separate chapters are devoted to Bayesian inference, to decision theory and to non-parametric and robust inference. The increasingly important topics of computationally intensive methods and generalised linear models are also included. In this edition, the material on recent developments has been updated, and additional exercises are included in most chapters.
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Includes bibliographical references (p. [308]-318) and index.
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- Created September 30, 2008
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| June 27, 2025 | Edited by MARC Bot | import existing book |
| August 18, 2024 | Edited by MARC Bot | import existing book |
| December 19, 2023 | Edited by ImportBot | import existing book |
| November 15, 2023 | Edited by MARC Bot | import existing book |
| September 30, 2008 | Created by ImportBot | Imported from Oregon Libraries MARC record |