Bayesian non-parametric simultaneous quantile regression for complete and grid data

Priyam Das and Subhashis Ghosal

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 172-186

Abstract: Bayesian methods for non-parametric quantile regression have been considered with multiple continuous predictors ranging values in the unit interval. Two methods are proposed based on assuming that either the quantile function or the distribution function is smooth in the explanatory variables and is expanded in tensor product of B-spline basis functions. Unlike other existing methods of non-parametric quantile regressions, the proposed methods estimate the whole quantile function instead of estimating on a grid of quantiles. Priors on the coefficients of the B-spline expansion are put in such a way that the monotonicity of the estimated quantile levels are maintained unlike local polynomial quantile regression methods. The proposed methods are also modified for quantile grid data where only the percentile range of each response observations are known. A comparative simulation study of the performances of the proposed methods and some other existing methods are provided in terms of prediction mean squared errors and mean L1-errors over the quartiles. The proposed methods are used to estimate the quantiles of US household income data and North Atlantic hurricane intensity data.

Keywords: B-spline prior; Black-box optimization; Block Metropolis–Hastings; Non-parametric quantile regression; North Atlantic hurricane data; US household income data (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947318300963
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:127:y:2018:i:c:p:172-186

DOI: 10.1016/j.csda.2018.04.007

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().