Abstract
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In this study, we propose a Bayesian quantile trend filtering method to estimate the non-stationary trend of quantiles. We introduce general shrinkage priors to induce locally adaptive Bayesian inference on trends and mixture representation of the asymmetric Laplace likelihood. To quickly compute the posterior distribution, we develop calibrated mean-field variational approximations to guarantee that the frequentist coverage of credible intervals obtained from the approximated posterior is a specified nominal level. Simulation and empirical studies show that the proposed algorithm is computationally much more efficient than the Gibbs sampler and tends to provide stable inference results, especially for high/low quantiles.
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References
Balke, N.S.: Detecting level shifts in time series. J. Bus. Econ. Stat. 11(1), 81–92 (1993)
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)
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)
Carvalho, C.M., Polson, N.G., Scott, J.G.: The horseshoe estimator for sparse signals. Biometrika 97(2), 465–480 (2010)
Cobb, G.W.: The problem of the Nile: conditional solution to a changepoint problem. Biometrika 65(2), 243–251 (1978)
Efron, B.: The Jackknife, the Bootstrap and Other Resampling Plans. SIAM (1982)
Eilers, P.H., De Menezes, R.X.: Quantile smoothing of array CGH data. Bioinformatics 21(7), 1146–1153 (2005)
Faulkner, J.R., Minin, V.N.: Locally adaptive smoothing with Markov random fields and shrinkage priors. Bayesian Anal. 13(1), 225 (2018)
Faulkner, J.R., Magee, A.F., Shapiro, B., et al.: Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories. Biometrics 76(3), 677–690 (2020)
Heng, Q., Zhou, H., Chi, E.C.: Bayesian trend filtering via proximal Markov chain Monte Carlo (2022). arXiv preprint arXiv:2201.00092
Kim, S.J., Koh, K., Boyd, S., et al.: \(\ell _1\) trend filtering. SIAM Rev. 51(2), 339–360 (2009)
Kowal, D.R., Matteson, D.S., Ruppert, D.: Dynamic shrinkage processes. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 81(4), 781–804 (2019)
Kozumi, H., Kobayashi, G.: Gibbs sampling methods for Bayesian quantile regression. J. Stat. Comput. Simul. 81(11), 1565–1578 (2011)
Makalic, E., Schmidt, D.F.: A simple sampler for the horseshoe estimator. IEEE Signal Process. Lett. 23(1), 179–182 (2015)
Nychka, D., Furrer, R., Paige, J., et al.: Fields: tools for spatial data. R package version 9.6 (2017)
Nychka, D., Gray, G., Haaland, P., et al.: A nonparametric regression approach to syringe grading for quality improvement. J. Am. Stat. Assoc. 90(432), 1171–1178 (1995)
Oh, H.S., Nychka, D., Brown, T., et al.: Period analysis of variable stars by robust smoothing. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 53(1), 15–30 (2004)
Park, T., Casella, G.: The Bayesian lasso. J. Am. Stat. Assoc. 103(482), 681–686 (2008)
Politsch, C.A., Cisewski-Kehe, J., Croft, R.A., et al.: Trend filtering-I. a modern statistical tool for time-domain astronomy and astronomical spectroscopy. Mon. Not. R. Astron. Soc. 492(3), 4005–4018 (2020)
Ramdas, A., Tibshirani, R.J.: Fast and flexible ADMM algorithms for trend filtering. J. Comput. Graph. Stat. 25(3), 839–858 (2016)
Roualdes, E.A.: Bayesian trend filtering (2015). arXiv preprint arXiv:1505.07710
Rue, H., Held, L.: Gaussian Markov Random Fields: Theory and Applications. CRC Press, Boca Raton (2005)
Sriram, K.: A sandwich likelihood correction for Bayesian quantile regression based on the misspecified asymmetric Laplace density. Stat. Probab. Lett. 107, 18–26 (2015)
Sriram, K., Ramamoorthi, R., Ghosh, P.: Posterior consistency of Bayesian quantile regression based on the misspecified asymmetric Laplace density. Bayesian Anal. 8(2), 479–504 (2013)
Syring, N., Martin, R.: Calibrating general posterior credible regions. Biometrika 106(2), 479–486 (2019)
Tibshirani, R.J.: Adaptive piecewise polynomial estimation via trend filtering. Ann. Stat. 42(1), 285–323 (2014)
Tibshirani, R.J., Taylor, J.: The solution path of the generalized lasso. Ann. Stat. 39(3), 1335–1371 (2011)
Tran, M.N., Nguyen, T.N., Dao, V.H.: A practical tutorial on variational bayes (2021). arXiv preprint arXiv:2103.01327
Wakayama, T., Sugasawa, S.: Trend filtering for functional data. Stat 12(1), e590 (2023)
Wakayama, T., Sugasawa, S.: Functional horseshoe smoothing for functional trend estimation. Stat. Sin. 34, 3 (2024)
Wand, M.P., Ormerod, J.T., Padoan, S.A., et al.: Mean field variational bayes for elaborate distributions. Bayesian Anal. 6(4), 847–900 (2011)
Wang, Y.X., Sharpnack, J., Smola, A., et al.: Trend filtering on graphs. In: Artificial Intelligence and Statistics, pp 1042–1050. PMLR (2015)
Yamada, H.: Trend extraction from economic time series with missing observations by generalized Hodrick–Prescott filters. Economet. Theor. 38(3), 419–453 (2022)
Yan, Y., Kottas, A.: A new family of error distributions for Bayesian quantile regression (2017). arXiv preprint arXiv:1701.05666
Yu, K., Moyeed, R.A.: Bayesian quantile regression. Stat. Probab. Lett. 54(4), 437–447 (2001)
Acknowledgements
The authors would like to thank the Associate Editor and a reviewer for their valuable comments and suggestions to improve the quality of this article. This work was supported by the JST, the establishment of university fellowships towards the creation of science technology innovation, grant number JPMJFS2129. This work was partially supported by the Japan Society for Promotion of Science (KAKENHI), grant numbers 21K13835 and 21H00699.
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Onizuka, T., Hashimoto, S. & Sugasawa, S. Fast and locally adaptive Bayesian quantile smoothing using calibrated variational approximations. Stat Comput 34, 15 (2024). https://doi.org/10.1007/s11222-023-10327-y
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DOI: https://doi.org/10.1007/s11222-023-10327-y