Least trimmed squares
Sparse least trimmed squares regression.
... SPARSE LEAST TRIMMED SQUARES REGRESSION By Andreas Alfons, Christophe Croux and Sarah Gelper ...known least trimmed squares (LTS) ...
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Asymptotics of Least Trimmed Squares Regression
... the least trimmed squares (LTS), proposed by Rousseeuw (1985) and derive asymptotic results allowing for nonlinear-regression and time-series ...
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Robust estimation of the vector autoregressive model by a least trimmed squares procedure.
... multivariate least squares estimator for (3) by a highly robust ...Multivariate Least Trimmed Squares (MLTS) es- timator, discussed in Agull´o, Croux and Van Aelst ...a trimmed ...
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Handling multicollinearity and outliers using weighted ridge least trimmed squares
... ridge least trimmed squares ...Ordinary Least Squares (OLS), Ridge Regression (RR),Robust Ridge Regression (RRR) such as Ridge LeastMedian Squares (RLMS), Ridge Least ...
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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... Regularized robust Nonlinear Least Trimmed Squares es- timator has been proposed in this study by adding an Elastic net penalty to the NLTS loss function. Robust generalized cross-validation was used ...
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Estimasi nonlinear least trimmed squares (NLTS) pada model regresi nonlinier yang dikenai outlier
... Nonlinear Least Trimmed Squares (NLTS) pada Model Regresi Nonlinier yang Dikenai Outlier, digunakan pendekatan deskriptif kuantitatif dengan menggambarkan data yang sudah ada dan disusun kembali ...
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Robust Kalman filter and smoother for errors-in-variables model with observation outliers based on Least-Trimmed-Squares
... Abstract— In this paper, we propose a robust Kalman filter and smoother for the errors-in-variables (EIV) state space model subject to observation noise with outliers. We introduce the EIV problem with outliers and then ...
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The multivariate least trimmed squares estimator.
... On the other hand, the MMIM improves the MSE of the initial MLTS in case of normal or exponential carriers, and can even be better than RMLTS, but it is much worse for the Cau[r] ...
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Reweighted Least Trimmed Squares: An Alternative to One-Step Estimators
... In statistics, techniques robust to atypical observations have recently been studied since such observations can arise for many reasons: heavy-tailed data distributions, miscoding, or heterogeneity not captured or ...
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Sparse least trimmed squares regression for analyzing high-dimensional large data sets
... Simula- tion results and a real data application to protein and gene expression data of the NCI-60 cancer cell panel illustrated the excellent performance of sparse LTS and showed that i[r] ...
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
... The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. Under certain assumptions, the OLS estimates are the best linear unbiased estimates. One of the important ...
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Robust estimation of the vector autoregressive model by a trimmed least squares procedure.
... In this paper we propose a robust procedure to estimate vector autoregressive models, to select their order, and to construct confidence bounds around the impulse [r] ...
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THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... LINEAR REGRESSION LINEAR REGRESSION is a powerfull tool for studying fundamental relationships between two (or more) RVs Y and X. The method is based on the method of least squares. Let’s discuss the ...
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Weighted least squares and adaptive least squares: further empirical evidence
... weighted least squares (WLS), also in conjunction with HC standard ...adaptive least squares (ALS), where it is ‘decided’ from the data whether the applied researcher should use either OLS or ...
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Least Squares Estimation
... The least squares criterion is a computationally convenient measure of ...example, least absolute deviations, which is more robust against out- ...
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The Method of Least Squares
... Abstract The Method of Least Squares is a procedure to determine the best fit line to data; the proof uses simple calculus and linear algebra. The basic problem is to find the best fit straight line y = ax ...
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The Method of Least Squares
... We can surmount this problem by taking a logarithmic transform of the data. Setting 𝒦 = log 𝑘, ℱ = log 𝐹 and ℛ = log 𝑟, the relation 𝐹 = 𝑘/𝑟 𝑛 becomes ℱ = 𝑛ℛ + 𝒦. We are now in a situation where we can apply the Method ...
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The Method of Least Squares
... 1 Introduction The least square methods ( LSM ) is probably the most popular tech- nique in statistics. This is due to several factors. First, most com- mon estimators can be casted within this framework. For ...
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