Perbandingan Metode Least Trimmed Squares dan Penaksir M dalam Mengatasi Permasalahan Data Pencilan [PDF]
, 2013 Analisis regresi digunakan untuk mengetahui hubungan antar variabel.Salah satu metode penaksir parameter dalam model regresi ini ialah metode kuadratterkecil.
Darnius, O. (Open) +2 more
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General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1) [PDF]
High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, such as the least trimmed squares and maximum trimmed likelihood, and ...
Cizek, P.
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Evolutionary algorithms for robust methods [PDF]
A drawback of robust statistical techniques is the increased computational effort often needed compared to non robust methods. Robust estimators possessing the exact fit property, for example, are NP-hard to compute.
Morell, Oliver, Nunkesser, Robin
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Confidence Intervals For An Effect Size When Variances Are Not Equal [PDF]
, 2006 Confidence intervals must be robust in having nominal and actual probability coverage in close agreement. This article examined two ways of computing an effect size in a two-group problem: (a) the classic approach which divides the mean difference by a ...
Algina, James +2 more
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares [PDF]
, 2009 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.
Uraibi, Hassan Sami
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New algorithms for computing the least trimmed squares estimator
, 2022 Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does, Rousseeuw (1984) proposed to minimize the sum of $h$ ($n/2 \leq h < n$) smallest squared residuals, the resulting estimator is called least trimmed squares (LTS).
openaire +2 more sources
A Fast Algorithm for Robust Regression with Penalised Trimmed Squares
, 2009 The presence of groups containing high leverage outliers makes linear regression a difficult problem due to the masking effect. The available high breakdown estimators based on Least Trimmed Squares often do not succeed in detecting masked high leverage ...
A Giloni +34 more
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Robust estimation of the vector autoregressive model by a least trimmed squares procedure. [PDF]
The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure.
Croux, Christophe, Joossens, Kristel
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On robust cross-validation for nonparametric smoothing [PDF]
, 2010 Procedures for local-constant smoothing are investigated in a broad variety of data situations with outliers and jumps. Moving window and nearest neighbour versions of mean and median smoothers are considered, as well as double window and linear ...
Fried, Roland +2 more
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A review of outlier detection procedures used in Surveying Engineering [PDF]
, 2013 The method of least squares is the most widely used parameter estimation tool in surveying engineering. It is implemented by minimizing the sum of squares of weighted residuals.
Yetkin, Mevlut
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