scholarly journals Consistency of M-Estimates in General Regression Models

1994 ◽  
Vol 50 (1) ◽  
pp. 93-114 ◽  
Author(s):  
F. Liese ◽  
I. Vajda
Keyword(s):  
Regression Models ◽  
M Estimates ◽  
General Regression

Korrigierte Schätzgleichungen für allgemeine Regressionsmodelle mit Fehlern in den Variablen / Corrected Estimating Equations for General Regression Models with Errors in the Variables

1998 ◽  
Vol 217 (1) ◽  
Author(s):  
Hans Schneeweiß
Keyword(s):  
Regression Models ◽  
Estimating Equations ◽  
General Regression

ZusammenfassungNach einer kurzen Einführung in die Theorie der erwartungstreuen Schätzgleichungen für allgemeine Regressionsmodelle und der korrigierten Schätzgleichungen für Regressionsmodelle mit fehlerbehafteten Kovariablen wird die Approximationsgüte eines auf Reihenentwicklung basierenden Ansatzes von Stefanski diskutiert.

Haar Wavelets in Data Analysis

2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Approximation Theory ◽  
Regression Models ◽  
Haar Wavelets ◽  
General Regression

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

Comparison of the Performance of Several General Regression Models for the Prediction of Bulb Weight of Onion

2021 ◽  
Vol 23 (1) ◽  
pp. 135-148
Author(s):  
Yunjeong Kang ◽  
Myung Hwan Na ◽  
Wanhyun Cho ◽  
Hyeon Seok Ko
Keyword(s):  
Regression Models ◽  
General Regression

scholarly journals General Regression Model for Predicting Surface Topography after Abrasive Blasting

Metals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 938 ◽  
Author(s):  
Dagmar Draganovská ◽  
Gabriela Ižaríková ◽  
Anna Guzanová ◽  
Janette Brezinová
Keyword(s):  
Correlation Analysis ◽  
Regression Model ◽  
Surface State ◽  
Correlation Matrix ◽  
Regression Models ◽  
Roughness Parameters ◽  
Anova Analysis ◽  
Surface Irregularities ◽  
General Regression ◽  
Abrasive Blasting

Abrasive blasting modifies the surface state of pre-treated materials in terms of surface irregularities. Bearing in mind that the roughness characteristics affect the components functionality, it is essential to study and evaluation the surface state of pre-treated materials. The paper deals with evaluation of relation between individual parameters of roughness of the blasted surfaces by the correlation analysis. Based on the measured values on the surfaces which were blasted by various types of blasting devices, the correlation matrix was set and the standard of statistic importance of correlation between the monitored parameters was determined from it. The correlation coefficient was also set. There were found regression models using ANOVA (ANalysis Of Variance). Based on the analysis of the results were also proposed sets of roughness parameters, which can be used in the assessment of the blasted surfaces.

Sign in / Sign up

Export Citation Format

Share Document