On Robust Estimation of Error Variance in (Highly) Robust Regression
نویسندگان
چکیده
منابع مشابه
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملRobust Estimation of Wavelet Variance
The wavelet variance provides a scale-based decomposition of the process variance for a time series or a random field and has been used to analyze various multi-scale processes. Examples of such processes include atmospheric pressure, deviations in time as kept by atomic clocks, soil properties in agricultural plots, snow fields in the polar regions and brightness temperature maps of South Paci...
متن کاملEstimation of observation-error variance in errors-in-variables regression
Assessing the variability of an estimator is a key component of the process of statistical inference. In nonparametric regression, estimating observation-error variance is the principal ingredient needed to estimate the variance of the regression mean. Although there is an extensive literature on variance estimation in nonparametric regression, the techniques developed in conventional settings ...
متن کاملfor tests of moderators and model fit in robust variance estimation in meta - regression
Title Page Title: Small-sample adjustments for tests of moderators and model fit in robust variance estimation in meta-regression Abstract Body Background / Context: Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The main focus in randomized experiments is often on the average treatment effect across all participants in the study, yet when t...
متن کاملRobust Estimation in Linear Regression Model: the Density Power Divergence Approach
The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data. In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this est...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Measurement Science Review
سال: 2020
ISSN: 1335-8871
DOI: 10.2478/msr-2020-0002