Arbeitspapier
Locally Adaptive Function Estimation for Binary Regression Models
In this paper we present a nonparametric Bayesian approach for fitting unsmooth or highly oscillating functions in regression models with binary responses. The approach extends previous work by Lang et al. (2002) for Gaussian responses. Nonlinear functions are modelled by first or second order random walk priors with locally varying variances or smoothing parameters. Estimation is fully Bayesian and uses latent utility representations of binary regression models for efficient block sampling from the full conditionals of nonlinear functions.
- Language
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Englisch
- Bibliographic citation
-
Series: Discussion Paper ; No. 310
- Subject
-
adaptive smoothing
forest health data
highly oscillating functions
MCMC
random walk priors
unsmooth functions
variable smoothing parameter
- Event
-
Geistige Schöpfung
- (who)
-
Jerak, Alexander
Lang, Stefan
- Event
-
Veröffentlichung
- (who)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (where)
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München
- (when)
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2003
- DOI
-
doi:10.5282/ubm/epub.1691
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1691-7
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Jerak, Alexander
- Lang, Stefan
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Time of origin
- 2003
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