Asymptotic properties of a particular nonlinear regression quantile estimation

2002 ◽  
Vol 60 (4) ◽  
pp. 387-394 ◽  
Author(s):  
Tae Soo Kim ◽  
Hae Kyung Kim ◽  
Sun Hur
Keyword(s):  
Nonlinear Regression ◽  
Asymptotic Properties ◽  
Quantile Estimation ◽  
Regression Quantile

scholarly journals A Method for Confidence Intervals of High Quantiles

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 70
Author(s):  
Mei Ling Huang ◽  
Xiang Raney-Yan
Keyword(s):  
Confidence Intervals ◽  
Computational Algorithm ◽  
Geometric Mean ◽  
Asymptotic Properties ◽  
Quantile Estimation ◽  
Heavy Tailed Distributions ◽  
High Quantiles ◽  
High Quantile ◽  
Heavy Tailed ◽  
Infinite Moments

The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing methods of confidence intervals (CIs) of high quantiles. This paper proposes a new estimator for high quantiles based on the geometric mean. The new estimator has good asymptotic properties as well as it provides a computational algorithm for estimating confidence intervals of high quantiles. The new estimator avoids difficulties, improves efficiency and reduces bias. Comparisons of efficiencies and biases of the new estimator relative to existing estimators are studied. The theoretical are confirmed through Monte Carlo simulations. Finally, the applications on two real-world examples are provided.

ROBUST TEST BASED ON NONLINEAR REGRESSION QUANTILE ESTIMATORS

2005 ◽  
Vol 20 (1) ◽  
pp. 145-159 ◽  
Author(s):  
SEUNG-HOE CHOI ◽  
KYUNG-JOONG KIM ◽  
MYUNG-SOOK LEE
Keyword(s):  
Nonlinear Regression ◽  
Robust Test ◽  
Regression Quantile ◽  
Quantile Estimators

scholarly journals Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Pierre Nguimkeu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Likelihood Estimation ◽  
Small Sample ◽  
First Order ◽  
Wald Tests ◽  
Nonlinear Regression Models ◽  
Mobile Cellular

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

Sign in / Sign up

Export Citation Format

Share Document