Least median of squares: a robust method for outlier and model error detection in regression and calibration The least median of squares method is a robust regression method, which means that it is not sensitive to outliers or other violations of the assumption of the usual normal model. This ...
We introduce a new, robust algorithm which recovers the fit corresponding to the absolute majority (at least 51 percent) of the pixels in the processing window. The algorithm uses the least median of squares (LMedS) estimator recently introduced in the statistics literature. We show that the ...
This study examines the performance of robust method of the least median squares (LMS) and the multi-stage method (MM) compared to OLS in a regression analysis of data which contains outliers. Data analysis was performed on simulation data and oil palm production data. Based on the average ...
Least median of squares and iteratively re‐weighted least squares as robust linear regression methods for fluorimetric determination of α‐lipoic acid in capsules in ideal and non‐ideal cases of linearityfluorescence quenchingiteratively re‐weighted least squares...
Robust fitting methods, intended for data sets possibly contaminated with invalid observations, are gaining increased use in analysis of fishery data. In particular, the method of least median of squares (LMS) has attracted attention. Its hallmark is high statistical resistance, which makes it ...
The optimization problem that arises out of the least median of squared residuals method in linear regression is analyzed. To simplify the analysis, it is ... N Krivulin - Physica-Verlag HD 被引量: 8发表: 1992年 Robust point pattern relaxation matching with missing or spurious points and ran...
In this paper, a robust estimation method, least median of squares (LMS) is used to identify outliers in land value data. Once the outliers are identified, are the land value equations re-estimated. The results show that a few observations can have a significant effect on the estimated ...
values of xi = (xi] , . . . ,xi,), which have a large influence KEY WORDS: Least squares method; Outliers; Robust (called leverage) on the fit. regression; Breakdown point. The next step in this direction was the M estimator (Huber 1973, p . 800), based on the idea of ...
A notion of d-fullness is introduced to study a robust extension of the maximum likelihood principle. Some results about the breakdown point of existing robust estimators follow. 关键词: Robust statistics maximum likelihood breakdown point DOI: 10.1016/0167-7152(93)90155-C 被引量: 33 年份:...
Both, the approaches LS as well as LP, are sensitive to outliers, and more robust methods are needed for handling outliers. The method of least absolute deviations (LAD) based on medians is a more robust approach, since it is superior to OLS when there are outliers in the data (see, ...