Fast and locally adaptive Bayesian quantile smoothing using calibrated variational approximations-Reference-Cited by-同舟云学术

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Fast and locally adaptive Bayesian quantile smoothing using calibrated variational approximations

Published:2023-10-30 Issue:1 Volume:34 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Onizuka Takahiro,Hashimoto Shintaro,Sugasawa Shonosuke

Funder

JST, the establishment of university fellowships towards the creation of science technology innovation

Japan Society for the Promotion of Science

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10327-y.pdf

Reference35 articles.

1. Balke, N.S.: Detecting level shifts in time series. J. Bus. Econ. Stat. 11(1), 81–92 (1993)

2. Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)

3. Brantley, H.L., Guinness, J., Chi, E.C.: Baseline drift estimation for air quality data using quantile trend filtering. Ann. Appl. Stat. 14(2), 585–604 (2020)

4. Carvalho, C.M., Polson, N.G., Scott, J.G.: The horseshoe estimator for sparse signals. Biometrika 97(2), 465–480 (2010)

5. Cobb, G.W.: The problem of the Nile: conditional solution to a changepoint problem. Biometrika 65(2), 243–251 (1978)

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