Nonparametric error estimation techniques applied to MSTAR data sets

1998 ◽  
Author(s):  
Raman K. Mehra ◽  
Melvyn Huff ◽  
Ravi B. Ravichandran ◽  
Arnold C. Williams
Keyword(s):  
Error Estimation ◽  
Data Sets ◽  
Estimation Techniques

Error Estimation Techniques Based on Defect Computation and Global Reconstruction

2010 ◽  
Author(s):  
Winfried Auzinger ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras
Keyword(s):  
Error Estimation ◽  
Estimation Techniques ◽  
Global Reconstruction

scholarly journals A discrete Ramos-Louzada distribution for asymmetric and over-dispersed data with leptokurtic-shaped: Properties and various estimation techniques with inference

2022 ◽  
Vol 7 (2) ◽  
pp. 1726-1741
Author(s):  
Ahmed Sedky Eldeeb ◽  
◽  
Muhammad Ahsan-ul-Haq ◽  
Mohamed. S. Eliwa ◽  
◽  
...  
Keyword(s):  
Count Data ◽  
Discrete Distribution ◽  
Real Data ◽  
Mass Function ◽  
Probability Mass Function ◽  
Data Sets ◽  
Estimation Techniques ◽  
Proposed Model ◽  
Probability Mass ◽  
Von Mises

<abstract> <p>In this paper, a flexible probability mass function is proposed for modeling count data, especially, asymmetric, and over-dispersed observations. Some of its distributional properties are investigated. It is found that all its statistical and reliability properties can be expressed in explicit forms which makes the proposed model useful in time series and regression analysis. Different estimation approaches including maximum likelihood, moments, least squares, Andersonӳ-Darling, Cramer von-Mises, and maximum product of spacing estimator, are derived to get the best estimator for the real data. The estimation performance of these estimation techniques is assessed via a comprehensive simulation study. The flexibility of the new discrete distribution is assessed using four distinctive real data sets ԣoronavirus-flood peaks-forest fire-Leukemia? Finally, the new probabilistic model can serve as an alternative distribution to other competitive distributions available in the literature for modeling count data.</p> </abstract>

Assessment of two a posteriori error estimators for elasticity problems

2008 ◽  
Vol 35 (11) ◽  
pp. 1239-1250
Author(s):  
A. H. ElSheikh ◽  
S. E. Chidiac ◽  
S. Smith
Keyword(s):  
Finite Element ◽  
Error Estimation ◽  
Posteriori Error ◽  
A Posteriori ◽  
A Posteriori Error Estimators ◽  
A Posteriori Error ◽  
Estimation Techniques ◽  
Error Estimators ◽  
Elasticity Problems ◽  
Posteriori Error Estimators

The main focus of this paper is on the evaluation of local a posteriori error estimation techniques for the finite element method (FEM). The standard error estimation techniques are presented for the coupled displacement fields appearing in elasticity problems. The two error estimators, the element residual method (ERM) and Zienkiewicz–Zhu (ZZ) patch recovery technique, are evaluated numerically and then used as drivers for a mesh adaptation process. The results demonstrate the advantages of employing a posteriori error estimators for obtaining finite element solutions with a pre-specified error tolerance. Of the two methods, the ERM is shown to produce adapted meshes that are similar to those adapted by the exact error. Furthermore, the ERM provides higher quality estimates of the error in the global energy norm when compared to the ZZ estimator.

An Empirical Investigation of Sampling Errors In Educational Survey Research

1979 ◽  
Vol 4 (1) ◽  
pp. 24-40 ◽  
Author(s):  
Kenneth N. Ross
Keyword(s):  
Survey Research ◽  
Error Estimation ◽  
Multivariate Statistics ◽  
Empirical Investigation ◽  
Sampling Technique ◽  
Sampling Errors ◽  
Sample Design ◽  
Estimation Techniques ◽  
Sampling Distributions ◽  
Complex Sample

This investigation examines the influence of sample design on the sampling errors of several multivariate statistics which are frequently used in educational survey research. Student’s empirical sampling technique is used to generate sampling distributions for several complex sample designs which are often used to sample schools, classrooms and students. Some results are presented for two error estimation techniques: “Jackknifing” and “Balanced Repeated Replication”.

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