scholarly journals Efficacy of Msplit Estimation in Displacement Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5047 ◽  
Author(s):  
Zbigniew Wiśniewski ◽  
Robert Duchnowski ◽  
Andrzej Dumalski
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Functional Model ◽  
Point Of View ◽  
Deformation Analysis ◽  
Optimal Assignment ◽  
Functional Models ◽  
Interesting Alternative

Sets of geodetic observations often contain groups of observations that differ from each other in the functional model (or at least in the values of its parameters). Sets of observations obtained at various measurement epochs is a practical example in such a context. From the conventional point of view, for example, in the least squares estimation, subsets in question should be separated before the parameter estimation. Another option would be application of Msplit estimation, which is based on a fundamental assumption that each observation is related to several competitive functional models. The optimal assignment of every observation to the respective functional model is automatic during the estimation process. Considering deformation analysis, each observation is assigned to several functional models, each of which is related to one measurement epoch. This paper focuses on the efficacy of the method in detecting point displacements. The research is based on example observation sets and the application of Monte Carlo simulations. The results were compared with the classical deformation analysis, which shows that the Msplit estimation seems to be an interesting alternative for conventional methods. The most promising are results obtained for disordered observation sets where the Msplit estimation reveals its natural advantage over the conventional approach.

Channeling in 4H-SiC from an Application Point of View

2019 ◽  
Vol 963 ◽  
pp. 386-389 ◽  
Author(s):  
Peter Pichler ◽  
Tomasz Sledziewski ◽  
Volker Häublein ◽  
Anton Bauer ◽  
Tobias Erlbacher
Keyword(s):  
Monte Carlo ◽  
Ion Implantation ◽  
Monte Carlo Simulations ◽  
Electronic Devices ◽  
Point Of View ◽  
Power Electronic ◽  
Application Point ◽  
Doping Profiles

During ion implantation into monocrystalline semiconductors, some of the implanted atoms will be deflected to crystal directions along which they may penetrate deeply into the crystal. We investigate such channeling effects for Al and N implantation into 4H-SiC by Monte Carlo simulations. The focus of the work is on the effects of channeling on doping profiles, the relevance for the net doping of typical power electronic devices, and the influence of scattering oxides.

Performance of Msplit estimates in the context of vertical displacement analysis

2020 ◽  
Vol 14 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Patrycja Wyszkowska ◽  
Robert Duchnowski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Vertical Displacement ◽  
Least Squares Estimation ◽  
Small Magnitude ◽  
Displacement Analysis ◽  
Better Than

AbstractThis paper concerns two types of Msplit estimation: squared Msplit estimation (SMS), which assumes normality of observation errors and absolute Msplit estimation (AMS), which applies {\text{L}_{1}} norm criterion. The main objective of the paper is to assess the accuracy of such estimators in vertical displacement analysis by applying Monte Carlo simulations. Another issue is to compare the accuracy of both estimators with the accuracy of the least squares estimation (LS). The paper shows that the accuracy of both Msplit estimates is like the accuracy of LS estimates. However, if some nonrandom errors occur, then accuracy of AMS estimates might be better than the accuracy of the rest of the estimates considered here. It stems from the fact that AMS estimates are robust against disturbances which have a small magnitude. It is also worth noting that the accuracy of both Msplit estimates might depend on the magnitude of the displacement.

scholarly journals Using Bayesian methods for the parameter estimation of deformation monitoring networks

2008 ◽  
Vol 8 (2) ◽  
pp. 335-347 ◽  
Author(s):  
E. Tanir ◽  
K. Felsenstein ◽  
M. Yalcinkaya
Keyword(s):  
Parameter Estimation ◽  
Prior Information ◽  
Likelihood Function ◽  
Deformation Monitoring ◽  
Point Of View ◽  
Deformation Analysis ◽  
Landslide Monitoring ◽  
Deformation Model ◽  
Additional Parameter ◽  
Monitoring Networks

Abstract. In order to investigate the deformations of an area or an object, geodetic observations are repeated at different time epochs and then these observations of each period are adjusted independently. From the coordinate differences between the epochs the input parameters of a deformation model are estimated. The decision about the deformation is given by appropriate models using the parameter estimation results from each observation period. So, we have to be sure that we use accurately taken observations (assessing the quality of observations) and that we also use an appropriate mathematical model for both adjustment of period measurements and for the deformation modelling (Caspary, 2000). All inaccuracies of the model, especially systematic and gross errors in the observations, as well as incorrectly evaluated a priori variances will contaminate the results and lead to apparent deformations. Therefore, it is of prime importance to employ all known methods which can contribute to the development of a realistic model. In Albertella et al. (2005), a new testing procedure from Bayesian point of view in deformation analysis was developed by taking into consideration prior information about the displacements in case estimated displacements are small w.r.t. (with respect to) measurement precision. Within our study, we want to introduce additional parameter estimation from the Bayesian point of view for a deformation monitoring network which is constructed for landslide monitoring in Macka in the province of Trabzon in north eastern Turkey. We used LSQ parameter estimation results to set up prior information for this additional parameter estimation procedure. The Bayesian inference allows evaluating the probability of an event by available prior evidences and collected observations. Bayes theorem underlines that the observations modify through the likelihood function the prior knowledge of the parameters, thus leading to the posterior density function of the parameters themselves.

ANOMALOUS RELAXATION IN FRACTAL AND DISORDERED STRUCTURES

1996 ◽  
Vol 10 (26) ◽  
pp. 3561-3568 ◽  
Author(s):  
S. FUJIWARA ◽  
F. YONEZAWA
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Exponential Type ◽  
Point Of View ◽  
Interesting Aspect ◽  
Fractal Structures ◽  
Physical Systems ◽  
Disordered Structures ◽  
Relaxation Functions ◽  
Anomalous Relaxation

For the purpose of clarifying the mechanisms of the anomalous relaxation, we carry out the Monte Carlo simulations of random walks in fractal and disordered structures. From our calculations of the relaxation functions and the complex susceptibilities, we find that the anomalous relaxation is of the Cole-Cole type in fractal structures while it is of the stretched-exponential type in non-fractal disordered structures. A most interesting aspect indicated by the results of our work is that the concept of “fractal”, originally introduced from purely mathematical point of view, is shown to play an important role in understanding some properties of realistic physical systems.

scholarly journals Parameter Estimation and Joint Confidence Regions for the Parameters of the Generalized Lindley Distribution

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Wenhao Gui ◽  
Man Chen
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Existence And Uniqueness ◽  
Confidence Regions ◽  
Lindley Distribution ◽  
Two Parameters ◽  
Inverse Moment

We deal with the problem of estimating the parameters of the generalized Lindley distribution. Besides the classical estimator, inverse moment and modified inverse estimators are proposed and their properties are investigated. A condition for the existence and uniqueness of the inverse moment and modified inverse estimators of the parameters is established. Monte Carlo simulations are conducted to compare the estimators’ performances. Two methods for constructing joint confidence regions for the two parameters are also proposed and their performances are discussed. A real example is presented to illustrate the proposed methods.

DOES THE "KNEE" IN PRIMARY COSMIC RAY SPECTRUM EXIST?

2003 ◽  
Vol 18 (18) ◽  
pp. 1225-1234 ◽  
Author(s):  
YURI V. STENKIN
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Power Law ◽  
Cosmic Ray ◽  
New Physics ◽  
Point Of View ◽  
Experimental Point ◽  
Primary Spectrum ◽  
Hadronic Structure ◽  
Knee Problem

The problem of the knee in primary cosmic ray at energy about 3–5 PeV is the most exciting problem in cosmic ray physics. Since 1958, physicists have been trying to solve this problem. In our opinion, the problem could be solved from the experimental point of view, whereas the primary spectrum would follow a pure power law. A key to the "knee" problem lies in the hadronic structure of EAS and its propagation in the Earth's atmosphere. Neither exotic processes nor new physics are used. An explanation of the approach and some results of Monte Carlo simulations are given below.

scholarly journals Gini Regressions and Heteroskedasticity

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Arthur Charpentier ◽  
Ndéné Ka ◽  
Stéphane Mussard ◽  
Oumar Ndiaye
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Generalized Least Squares ◽  
U Statistics ◽  
White Test ◽  
Gini Regression

We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data.

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