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|a (OCoLC)1127850237
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|a MAIN
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| 050 |
|
4 |
|a QA278.2
|b .F54 2019
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| 245 |
0 |
0 |
|a Flexible Bayesian regression modelling /
|c edited by Yanan Fan, David Nott, Mike S. Smith, Jean-Luc Dortet-Bernadet.
|
| 264 |
|
1 |
|a Amsterdam :
|b Academic Press,
|c 2019.
|
| 300 |
|
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|a 1 online resource
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| 336 |
|
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|a text
|2 rdacontent
|0 http://id.loc.gov/vocabulary/contentTypes/txt
|
| 337 |
|
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|a computer
|2 rdamedia
|0 http://id.loc.gov/vocabulary/mediaTypes/c
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| 338 |
|
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|a online resource
|2 rdacarrier
|0 http://id.loc.gov/vocabulary/carriers/cr
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| 500 |
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|a 1. Bayesian Quantile Regression with the Asymmetric Laplace Distribution 2. A Vignette on Model-Based Quantile Regression: Analyzing Excess-Zero Response 3. Bayesian Nonparametric Density Regression for Ordinal Responses 4. Non-standard Flexible Regression via Variational Bayes 5. Bayesian Mixed Binary-Continuous Copula Regression with an Application to Childhood Undernutrition 6. Bayesian Nonparametric Methods for Financial and Microeconomic Time Series Analysis 7. Bayesian Spectral Analysis Regression 8. Flexible Regression Modelling Under Shape Constraints 9. Scalable Bayesian Variable Selection for aNegative Binomial Regression Models.
|
| 588 |
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|a Description based on CIP data; resource not viewed.
|
| 520 |
|
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|a Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.
|
| 650 |
|
0 |
|a Regression analysis
|x Mathematical models.
|0 http://id.loc.gov/authorities/subjects/sh2009006876
|
| 650 |
|
0 |
|a Bayesian statistical decision theory.
|0 http://id.loc.gov/authorities/subjects/sh85012506
|
| 650 |
|
7 |
|a Bayesian statistical decision theory.
|2 fast
|0 (OCoLC)fst00829019
|
| 650 |
|
7 |
|a Regression analysis
|x Mathematical models.
|2 fast
|0 (OCoLC)fst01093277
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Fan, Y.
|q (Yanan),
|e editor.
|0 http://id.loc.gov/authorities/names/n2018025530
|
| 700 |
1 |
|
|a Nott, David,
|e editor.
|0 http://id.loc.gov/authorities/names/n86830970
|
| 700 |
1 |
|
|a Smith, Mike S.,
|e editor.
|
| 700 |
1 |
|
|a Dortet-Bernadet, Jean-Luc,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|z 9780128158623
|
| 903 |
|
|
|a HeVa
|
| 929 |
|
|
|a oclccm
|
| 999 |
f |
f |
|i 609a47fd-653c-5f6c-94c9-fe28687812bb
|s 95fa311b-65ec-5796-8048-641e68950b34
|
| 928 |
|
|
|t Library of Congress classification
|a QA278.2 .F54 2019
|l Online
|c UC-FullText
|u https://www.sciencedirect.com/science/book/9780128158623
|z Elsevier
|g ebooks
|i 11587872
|
