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.

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 Improved Dividend Estimation from Intraday Quotes

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 95
Author(s):  
Pontus Söderbäck ◽  
Jörgen Blomvall ◽  
Martin Singull
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Options Market ◽  
Market Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Least Squares Estimates ◽  
Estimation Quality

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

scholarly journals A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia

2018 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Hasih Pratiwi ◽  
Yuliana Susanti ◽  
Sri Sulistijowati Handajani
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Likelihood Estimation ◽  
Corn Production ◽  
Least Squares Fit ◽  
Class Of Estimators ◽  
M Estimation ◽  
Heavy Tailed ◽  
Least Squares Estimates ◽  
Huber Estimator

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.<br />Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator

Sign in / Sign up

Export Citation Format

Share Document