Convergence theorems on the least square estimators of the structural parameters of a linear explosive model

1974 ◽  
Vol 26 (1) ◽  
pp. 61-85 ◽  
Author(s):  
K. N. Venkataraman
Keyword(s):  
Structural Parameters ◽  
Least Square ◽  
Convergence Theorems ◽  
Least Square Estimators

scholarly journals Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

2019 ◽  
Author(s):  
Bartlomiej Blachowski
Keyword(s):  
Damage Identification ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Least Square ◽  
Truss Structures ◽  
Shape Sensitivity ◽  
Damage Scenarios ◽  
Norm Minimization ◽  
Steel Members ◽  
Upper Deck

The present study deals with a comprehensive approach for damage identification of spatial truss structures. The novelty of the proposed approach consists of a three-level analysis. First, sensitivity of assumed modal characteristics is calculated. Second, natural frequency sensitivity is used to determine hardly identifiable structural parameters and mode shape sensitivity is applied to select damage-sensitive locations of sensors. Third, two sparsity constrained optimization algorithms are tested towards efficient identification of applied damage scenarios. These two algorithms are based on ℓ1-norm minimization and non-negative least square (NNLS) solution.Performances of both proposed algorithms have been compared in two realistic case studies: the first one concerned a three-dimensional truss girder with 61 structural parameters and the second one was devoted to an upper-deck arch bridge composed of 416 steel members.

scholarly journals Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins

2019 ◽  
Vol 17 (1) ◽  
pp. 401-407 ◽  
Author(s):  
L. Naneva ◽  
M. Nedyalkova ◽  
S. Madurga ◽  
F. Mas ◽  
V. Simeonov
Keyword(s):  
Cluster Analysis ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Surface Protein ◽  
Partial Least Square ◽  
Least Square ◽  
Multivariate Statistical ◽  
Genomics And Proteomics ◽  
Cross Covariance ◽  
Allergenic Proteins

AbstractAs a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article.The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS–DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins.

On the statistical properties of least-square estimators of layered neural networks

2004 ◽  
Vol 35 (12) ◽  
pp. 1-9
Author(s):  
Masashi Kitahara ◽  
Taichi Hayasaka ◽  
Naohiro Toda ◽  
Shiro Usui
Keyword(s):  
Neural Networks ◽  
Statistical Properties ◽  
Least Square ◽  
Least Square Estimators

One Parameter A (α) Distribution: Different Methods of Estimation

2021 ◽  
Vol 8 (1) ◽  
pp. 01-09
Author(s):  
Sanku Dey ◽  
Mahendra Saha ◽  
Sankar Goswami
Keyword(s):  
Unknown Parameter ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Point Of View ◽  
Least Square ◽  
Bootstrap Confidence Interval ◽  
Likelihood Estimator ◽  
Data Set ◽  
Von Mises ◽  
Least Square Estimators

This paper addresses the different methods of estimation of the unknown parameter of one parameter A(α) distribution from the frequentist point of view. We briefly describe different approaches, namely, maximum likelihood estimator, least square and weighted least square estimators, maximum product spacing estimators, Cram´er-von Mises estimator and compare those using extensive numerical simulations. Next, we obtain parametric bootstrap confidence interval of the parameter using frequentist approaches. Finally, one real data set has been analysed for illustrative purposes.

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