Results 61 to 70 of about 4,362,963 (201)
MENGATASI PENCILAN PADA PEMODELAN REGRESI LINEAR BERGANDA DENGAN METODE REGRESI ROBUST PENAKSIR LMS
Barekeng, 2019 Ordinary Least Squares (OLS) is frequent used method for estimating parameters. OLS estimator is not a robust regression procedure for the presence of outliers, so the estimate becomes inappropriate.
Farida Daniel
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On the use of robust regression in econometrics [PDF]
The use of robust regression estimators has gained popularity among applied econometricians. The main argument invoked to justify the use of the robust estimators is that they provide efficiency gains in the presence of outliers or non-normal errors ...
J.M.C. Santos Silva, Markus Baldauf
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A Comparison of the Robust Zero-Inflated and Hurdle Models with an Application to Maternal Mortality
Mathematical and Computational ApplicationsThis study evaluates the performance of count regression models in the presence of zero inflation, outliers, and overdispersion using both simulated and real-world maternal mortality dataset.
Phelo Pitsha +2 more
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Robust estimation in linear regression models with fixed effects [PDF]
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regression estimates in linear models with fixed effects. We propose to calculate the principal sensitivity components associated to each cluster and delete the ...
Betsabe Perez +2 more
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COMPARATION ON SEVERAL SMOOTHING METHODS IN NONPARAMETRIC REGRESSION [PDF]
, 2011 There are three nonparametric regression methods covered in this section. These are Moving Average Filtering-Based Smoothing, Local Regression Smoothing, and Kernel Smoothing Methods.
Isnanto, R.Rizal, Rizal Isnanto, R
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EconomiesArtificial intelligence (AI) is a key driver of the energy transition and sustainable economic development. However, the specific mechanisms through which AI adoption impacts renewable energy production versus consumption remain poorly understood.
Laura Vasilescu +3 more
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On the implementation of LIR: the case of simple linear regression with interval data [PDF]
, 2012 This paper considers the problem of simple linear regression with interval-censored data. That is, n pairs of intervals are observed instead of the n pairs of precise values for the two variables (dependent and independent).
Cattaneo, Marco E.G.V. +2 more
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On Robust Estimation of Error Variance in (Highly) Robust Regression
Measurement Science Review, 2020 The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on
Kalina Jan, Tichavský Jan
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, 2014 Any multiple Criteria Decision Aiding (MCDA) method needs some preference parameters. The Decision Maker (DM) could be asked to provide directly all these parameters; however, because it needs a great cognitive effort, the indirect preference information is more used in practice. Starting from the indirect preference information, usually there could be
Corrente S +3 more
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Iteratively Reweighted Blind Deconvolution With Adaptive Regularization Parameter Estimation
IEEE Access, 2017 In many realistic image processing applications, the acquired images often suffer from mixed noises and blurring, which greatly degrade the image quality.
Houzhang Fang +3 more
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