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S. Ghosal

53 papers hereh-index 356.8k citations191 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author4
  • last author43

Across the 52 of 53 papers where every author was matched, so the position is known.

fields
  • math.ST33
  • stat.ME17
  • stat.ML2
  • stat.AP1
same name
  • S. Ghosal — 21 papers, h 34
  • S. Ghosal — 3 papers, h 10
  • S. Ghosal — 3 papers, h 13
  • S. Ghosal — 2 papers, h 4
  • S. Ghosal — 1 paper, h 6
  • S. Ghosal — 1 paper, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20072025
most citedConvergence rates of posterior distributions for noniid observations

198 citations · 888 across the 47 of their papers we have counts for

collaborators
Showing 2016Show all

4 papers · 1 filter

stat.ME2016

Bayesian Non-parametric Simultaneous Quantile Regression for Complete and Grid Data

Priyam Das, Subhashis Ghosal

In this paper, we consider Bayesian methods for non-parametric quantile regressions with multiple continuous predictors ranging values in the unit interval. In the first method, th…

stat.ME2016★ 28 cited

Bayesian Quantile Regression Using Random B-spline Series Prior

Priyam Das, Subhashis Ghoshal

We consider a Bayesian method for simultaneous quantile regression on a real variable. By monotone transformation, we can make both the response variable and the predictor variable…

stat.ME2016★ 9 cited

Analyzing Ozone Concentration by Bayesian Spatio-temporal Quantile Regression

Priyam Das, Subhashis Ghosal

Ground level Ozone is one of the six common air-pollutants on which the EPA has set national air quality standards. In order to capture the spatio-temporal trend of 1-hour and 8-ho…

math.ST2016

Bayesian mode and maximum estimation and accelerated rates of contraction

William Weimin Yoo, Subhashis Ghosal

We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.