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Applied Statistical Inference Likelihood and Bayes /
This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2014.
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| Edición: | 1st ed. 2014. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Held, Leonhard. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Applied Statistical Inference |h [electronic resource] : |b Likelihood and Bayes / |c by Leonhard Held, Daniel Sabanés Bové. |
| 250 | |a 1st ed. 2014. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2014. | |
| 300 | |a XIII, 376 p. 71 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Biometry. | |
| 650 | 0 | |a Mathematical statistics-Data processing. | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Biostatistics. |
| 650 | 2 | 4 | |a Statistics and Computing. |
| 700 | 1 | |a Sabanés Bové, Daniel. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642378881 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642378867 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-37887-4 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
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| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||