Estimating the error distribution function in nonparametric regression with multivariate covariates

2009 ◽  
Vol 79 (7) ◽  
pp. 957-964 ◽  
Author(s):  
Ursula U. Müller ◽  
Anton Schick ◽  
Wolfgang Wefelmeyer
Keyword(s):  
Distribution Function ◽  
Nonparametric Regression ◽  
Error Distribution

Estimating the error distribution function in semiparametric regression

2007 ◽  
Vol 25 (1/2007) ◽  
pp. 1-18 ◽  
Author(s):  
Ursula U. Müller ◽  
Anton Schick ◽  
Wolfgang Wefelmeyer
Keyword(s):  
Distribution Function ◽  
Semiparametric Regression ◽  
Error Distribution

Model-calibration estimation of the distribution function using nonparametric regression

Metrika ◽  
2008 ◽  
Vol 71 (1) ◽  
pp. 33-44 ◽  
Author(s):  
M. Rueda ◽  
I. Sánchez-Borrego ◽  
A. Arcos ◽  
S. Martínez
Keyword(s):  
Distribution Function ◽  
Nonparametric Regression ◽  
Model Calibration

Statistical inferences of form error distribution function

1988 ◽  
Vol 10 (2) ◽  
pp. 97-99 ◽  
Author(s):  
Yang Shunian ◽  
Li Zhu ◽  
Li Guangying
Keyword(s):  
Distribution Function ◽  
Error Distribution ◽  
Form Error ◽  
Statistical Inferences

Statistical Properties of the Unsymmetrical P-Norm Distribution

2010 ◽  
Vol 439-440 ◽  
pp. 1153-1158
Author(s):  
Pan Xiong ◽  
Shuan Li Yuan ◽  
Shao Jie Cheng
Keyword(s):  
Distribution Function ◽  
Error Distribution ◽  
Statistical Properties ◽  
Test Method ◽  
Observation Error ◽  
Random Errors ◽  
True Error ◽  
Practical Situation ◽  
Error Distributions ◽  
The Common

The distribution of observation errors is determined according to their magnitudes by using the distribution collocation test method or figure method taking into account the result, sample total, the interval density etc. It is therefore difficult to get the specific type of error distribution of observations by conventional methods. In analyzing the actual situation of the observation error distribution using their statistical properties, this paper proposes the use of unsymmetrical distribution to express the true distribution of the observation errors. The P-norm distribution is a generalized form of a group of error distributions, and from the statistical properties of random errors we can arrive at an unsymmetrical P-norm distribution according to the practical situation of the occurrence of random errors. The common P-norm distribution is the specific case of this distribution. This paper deduces the density function equation of the unsymmetrical P-norm distribution, obtained the statistical properties of the distribution function and the evaluation of precision index. By choosing appropriate value for p, we can get closer to the distribution function of the true error distribution.

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