print.KhmaladzeTest

Print an object generated by KhmaladzeTest

 regression

Estimation and inference methods for models for conditional quantile functions:
Linear and nonlinear parametric and non-parametric (total variation penalized) models
for conditional quantiles of a univariate response and several methods for handling
censored survival data. Portfolio selection methods based on expected shortfall
risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
<doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression,
CRC Press, <doi:10.1201/9781315120256>.

Roger Koenker

quantreg

Quantile Regression

Stephen Portnoy

Blaise Melly

Achim Zeileis

Philip Grosjean

Cleve Moler

Yousef Saad

Victor Chernozhukov

Ivan Fernandez-Val

Martin Maechler

print.KhmaladzeTest function

<dl><dt>x</dt>
<dd>Object returned from KhmaladzeTest representing the fit of the model.</dd><dt>...</dt>
<dd>Optional arguments.</dd></dl>

Arguments

Print a KhmaladzeTest object — print.KhmaladzeTest

<dl>

<dt>x</dt>
<dd>Object returned from KhmaladzeTest representing the fit of the model.</dd>

<dt>...</dt>
<dd>Optional arguments.</dd>

</dl>

Print a KhmaladzeTest object

print.KhmaladzeTest: Print a KhmaladzeTest object

Description

Usage

Arguments

See Also