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Bayesian inference in based-kernel regression: comparison of count data of condition factor of fish in pond systems
- Source :
- Journal of Applied Statistics, Journal of Applied Statistics, Taylor & Francis (Routledge), In press, pp.1-18. ⟨10.1080/02664763.2020.1830953⟩, Journal Of Applied Statistics (0266-4763) (Taylor & Francis Ltd), 2022-02, Vol. 49, N. 3, P. 676-693, J Appl Stat
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; The discrete kernel-based regression approach generally provides pointwise estimates of count data that do not account for uncertainty about both parameters and resulting estimates. This work aims to provide probabilistic kernel estimates of count regression function by using Bayesian approach and then allows for a readily quantification of uncertainty. Bayesian approach enables to incorporate prior knowledge of parameters used in discrete kernel-based regression. An application was proposed on count data of condition factor of fish (K) provided from an experimental project that analyzed various pond management strategies. The probabilistic distribution of estimates were contrasted by discrete kernels, as a support to theoretical results on the performance of kernels. More practically, Bayesian credibility intervals of K-estimates were evaluated to compare pond management strategies. Thus, similarities were found between performances of semi-intensive and coupled fishponds, with formulated feed, in comparison with extensive fishponds, without formulated feed. In particular, the fish development was less predictable in extensive fishpond, dependent on natural resources, than in the two other fishponds, supplied in formulated feed.
- Subjects :
- Statistics and Probability
Pointwise
021103 operations research
discrete kernel
prior and posterior distributions
0211 other engineering and technologies
02 engineering and technology
Articles
Aquaculture
Bayesian inference
01 natural sciences
Regression
Condition factor
010104 statistics & probability
fishpond
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Kernel (statistics)
Statistics
Kernel regression
0101 mathematics
Statistics, Probability and Uncertainty
uncertainty analysis
Uncertainty analysis
Mathematics
Count data
Subjects
Details
- Language :
- English
- ISSN :
- 02664763 and 13600532
- Database :
- OpenAIRE
- Journal :
- Journal of Applied Statistics, Journal of Applied Statistics, Taylor & Francis (Routledge), In press, pp.1-18. ⟨10.1080/02664763.2020.1830953⟩, Journal Of Applied Statistics (0266-4763) (Taylor & Francis Ltd), 2022-02, Vol. 49, N. 3, P. 676-693, J Appl Stat
- Accession number :
- edsair.doi.dedup.....ee8b2e00e9dc5f78e94da13f6836a59d