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 Determination of Terrain Profile from TLS Data by Applying Msplit Estimation

2020 ◽  
Vol 13 (1) ◽  
pp. 31
Author(s):  
Patrycja Wyszkowska ◽  
Robert Duchnowski ◽  
Andrzej Dumalski
Keyword(s):  
Data Processing ◽  
Least Squares ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Vertical Displacement ◽  
Least Squares Estimation ◽  
Displacement Analysis ◽  
Empirical Tests ◽  
The Absolute

This paper presents an application of an Msplit estimation in the determination of terrain profiles from terrestrial laser scanning (TLS) data. We consider the squared Msplit estimation as well as the absolute Msplit estimation. Both variants have never been used to determine terrain profiles from TLS data (the absolute Msplit estimation has never been applied in any TLS data processing). The profiles are computed by applying polynomials of a different degree, determining which coefficients are estimated using the method in question. For comparison purposes, the profiles are also determined by applying a conventional least squares estimation. The analyses are based on simulated as well as real TLS data. The actual objects have been chosen to contain terrain details (or obstacles), which provide some measurements which are not referred to as terrain surface; here, they are regarded as outliers. The empirical tests prove that the proposed approach is efficient and can provide good terrain profiles even if there are outliers in an observation set. The best results are obtained when the absolute Msplit estimation is applied. One can suggest that this method can be used in a vertical displacement analysis in mining damages or ground disasters.

scholarly journals A Comprehensive Hα Nova Survey of M81

2004 ◽  
Vol 194 ◽  
pp. 240-240
Author(s):  
James D. Neill ◽  
Michael M. Shara ◽  
Elaine Halbedel ◽  
Viktor Malnushenko
Keyword(s):  
Monte Carlo ◽  
Spatial Distribution ◽  
Monte Carlo Simulations ◽  
Lower Limit ◽  
Radial Distribution ◽  
Major Axis ◽  
Stellar Populations ◽  
Light Curves ◽  
Total Light ◽  
Better Than

A spatially complete Hα survey of M81 for novae was conducted continuously over a 5 month interval using the Calypso Telescope at Kitt Peak, AZ. A raw nova rate for M81 gives 23 yr–1 which is a lower limit. Monte Carlo simulations using nova light curves and survey frame limits yield a nova rate of . Using this value and the K-band photometry for M81 from the 2MASS Large Galaxy Atlas of Jarret et al. (2003) gives a luminosity specific nova rate of .The spatial distribution of the novae follows the bulge light much better than the disk or total light according to KS tests of their radial distribution. The asymmetric nova distribution across the major axis line of M81 implies a bulge-to-disk nova ratio of > 9 and supports the idea that novae originate primarily in older stellar populations.

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.

scholarly journals A Study on the Amplitude Comparison Monopulse Algorithm

2020 ◽  
Vol 10 (11) ◽  
pp. 3966
Author(s):  
Minjeong Kim ◽  
Daseon Hong ◽  
Sungsu Park
Keyword(s):  
Monte Carlo ◽  
Numerical Simulations ◽  
Least Squares ◽  
Estimation Method ◽  
Prediction Equation ◽  
Least Squares Estimation ◽  
Estimation Accuracy ◽  
Prediction Equations ◽  
Special Case ◽  
Rms Errors

This paper presents two amplitude comparison monopulse algorithms and their covariance prediction equation. The proposed algorithms are based on the iterated least-squares estimation method and include the conventional monopulse algorithm as a special case. The proposed covariance equation is simple but predicts RMS errors very accurately. This equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar, and is also applicable to the conventional monopulse algorithm. The proposed algorithms and covariance prediction equations are validated by the numerical simulations with 100,000 Monte Carlo runs.

scholarly journals Systematic bias of selected estimates applied in vertical displacement analysis

2020 ◽  
Vol 10 (1) ◽  
pp. 41-47 ◽  
Author(s):  
P. Wyszkowska ◽  
R. Duchnowski
Keyword(s):  
Least Squares ◽  
Vertical Displacement ◽  
Systematic Bias ◽  
Displacement Analysis ◽  
Robust Estimates ◽  
Gross Error ◽  
Linear Estimators ◽  
M Estimate ◽  
Response Biases ◽  
Least Squares Estimates

AbstractIn surveying problems we almost always use unbiased estimators; however, even unbiased estimator might yield biased assessments, which is due to data. In statistics one distinguishes several types of such biases, for example, sampling, systemic or response biases. Considering surveying observation sets, bias from data might result from systematic or gross errors of measurements. If nonrandom errors in an observation set are known, then bias can easily be determined for linear estimates (e.g., least squares estimates). In the case of non-linear estimators, it is not so simple. In this paper we are focused on a vertical displacement analysis and we consider traditional least squares estimate, two Msplitestimates and two basic robust estimates, namely M-estimate, R-estimate. The main aim of the paper is to assess estimate biases empirically by applying Monte Carlo method. The smallest biases are obtained for M- and R-estimates, especially for a high magnitude of a gross error. On the other hand, there are several cases when Msplitestimates are the best. Such results are acquired when the magnitude of a gross error is moderate or small. The outcomes confirm that bias of Msplitestimates might vary for different point displacements.

Evaluation methods for estimation of Weibull parameters used in Monte Carlo simulations for safety analysis of pressure vessels

2021 ◽  
Vol 63 (4) ◽  
pp. 379-385
Author(s):  
Bin Wang ◽  
Faisal Islam ◽  
Georg W. Mair
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Monte Carlo Simulations ◽  
Sample Size ◽  
Least Squares ◽  
Test Data ◽  
Pressure Vessels ◽  
Small Sample ◽  
Least Squares Regression ◽  
Weibull Parameters

Abstract The test data for static burst strength and load cycle fatigue strength of pressure vessels can often be well described by Gaussian normal or Weibull distribution functions. There are various approaches which can be used to determine the parameters of the Weibull distribution function; however, the performance of these methods is uncertain. In this study, six methods are evaluated by using the criterion of OSL (observed significance level) from Anderson-Darling (AD) goodness of Fit (GoF), These are: a) the norm-log based method, b) least squares regression, c) weighted least squares regression, d) a linear approach based on good linear unbiased estimators, e) maximum likelihood estimation and f) method of moments estimation. In addition, various approaches of ranking function are considered. The results show that there are no outperforming methods which can be identified clearly, primarily due to the limitation of the small sample size of the test data used for Weibull analysis. This randomness resulting from the sampling is further investigated by using Monte Carlo simulations, concluding that the sample size of the experimental data is more crucial than the exact method used to derive Weibull parameters. Finally, a recommendation is made to consider the uncertainties of the limitations due to the small size for pressure vessel testing and also for general material testing.

A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

2007 ◽  
Vol 15 (2) ◽  
pp. 165-181 ◽  
Author(s):  
Boris Shor ◽  
Joseph Bafumi ◽  
Luke Keele ◽  
David Park
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Political Science ◽  
Multilevel Modeling ◽  
Multilevel Model ◽  
Multilevel Models ◽  
Cross Sectional ◽  
Better Than ◽  
Analysis Of Time Series

The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.

Sign in / Sign up

Export Citation Format

Share Document