Bayesian empirical likelihood estimation for kink regression with unknown threshold
© Springer International Publishing AG 2018. Bayesian inference provides a flexible way of combining data with prior information from our knowledge. However, Bayesian estimation is very sensitive to the likelihood. We need to evaluate the likelihood density, which is difficult to evaluate, in order...
Saved in:
Main Authors: | Woraphon Yamaka, Pathairat Pastpipatkul, Songsak Sriboonchitta |
---|---|
Format: | Book Series |
Published: |
2018
|
Subjects: | |
Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037863242&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/58520 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Chiang Mai University |
Similar Items
-
Bayesian empirical likelihood estimation for kink regression with unknown threshold
by: Woraphon Yamaka, et al.
Published: (2018) -
Expectile and quantile kink regressions with unknown threshold
by: Varith Pipitpojanakarn, et al.
Published: (2018) -
Bayesian empirical likelihood estimation of smooth kink regression
by: Woraphon Yamaka, et al.
Published: (2019) -
Expectile and quantile kink regressions with unknown threshold
by: Varith Pipitpojanakarn, et al.
Published: (2018) -
Predictive recursion maximum likelihood of threshold autoregressive model
by: Pathairat Pastpipatkul, et al.
Published: (2018)