A Bayesian Nonparametric Approach to Inference for Quantile Regression

By Matt Taddy, Athanasios Kottas. 2010

139 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39490be8dd5efea6a5634e4ca3d75933907e99c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Mixture Modeling for Marked Poisson Processes (2010, methodology): “s corresponding to marks and to locations, and the random mixing measure is relied upon to induce dependence between these random variables. This technique has been employed in regression settings by Taddy and Kottas (2010), and provides a fairly automatic procedure for nonparametric model building ”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, background): “Muller, Erkanli, and West (1996), Roeder and Wasserman (1997), Norets and Pelenis (2012), and Taddy and Kottas (2010) suggested obtaining conditional densities of interest from joint distribution estimation.”
Beyond mean regression (2013, methodology): “…Reich et al. (2010) utilising a joint location-scale model for all quantiles with linear predictors for mean and standard deviation and Dirichlet process mixture for the error density, and Taddy and Kottas (2010) utilising a joint Dirichlet process mixture for covariates and responses.”
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation (2014, background): “See Hjort et al. (2010) and Taddy and Kottas (2010), for examples.”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, methodology): “It is possible to avoid the monotonicity problem altogether by specifying a nonparametric model for the conditional distribution FY (y | x) and then inverting it to derive conditional quantile curves QY (τ | x) (Scaccia and Green 2003; Geweke and Keane 2007; Taddy and Kottas 2010).”
Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime (2010, methodology): “Density values for individual particles are calculated, for each of y = 0 or 1 and a set of grid locations, as the Rao-Blackwellized conditional posterior predictive density, marginalized over components l > Lr, as detailed in equation (13) of the appendix; refer to Taddy and Kottas (2009) for furth”
Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, background): “A default speciﬁcation strategy is developed by considering the limiting case of the model as α → 0 + (as in Taddy and Kottas, 2010), which results in a single normal distribution for ( Z , X ).”