predict.qris

Prediction based on fitted quantile regression model

A collection of functions is provided by this package to fit quantiles regression models for censored residual lifetimes. It provides various options for regression parameters estimation: the induced smoothing approach (smooth), and L1-minimization (non-smooth). It also implements the estimation methods for standard errors of the regression parameters estimates based on an efficient partial multiplier bootstrap method and robust sandwich estimator. Furthermore, a simultaneous procedure of estimating regression parameters and their standard errors via an iterative updating procedure is implemented (iterative). For more details, see Kim, K. H., Caplan, D. J., & Kang, S. (2022), "Smoothed quantile regression for censored residual life", Computational Statistics, 1-22 <doi:10.1007/s00180-022-01262-z>.

Kyu Hyun Kim

qris

Quantile Regression Model for Residual Lifetime Using an Induced
Smoothing Approach

Sangwook Kang

Sy Han Chiou

predict.qris function

<dl><dt>object</dt>
<dd>is a qris object</dd>
<dt>newdata</dt>
<dd>is a data frame for an optional new data to do predictions.
If omitted, the fitted values based on the original data and fit will be returned.</dd>
<dt>...</dt>
<dd>for future extension</dd></dl>

Arguments

Prediction for Quantile Regression Model Fitted on Residual life — predict.qris

<dl>

<dt>object</dt>
<dd>is a qris object</dd>


<dt>newdata</dt>
<dd>is a data frame for an optional new data to do predictions.
If omitted, the fitted values based on the original data and fit will be returned.</dd>


<dt>...</dt>
<dd>for future extension</dd>

</dl>

predict.qris: Prediction for Quantile Regression Model Fitted on Residual life

Description

Usage

Value

Arguments