Robust regression
View article: Modeling Non-Stationary Time Series Using Robust Wavelet-Based Regression Techniques
Modeling Non-Stationary Time Series Using Robust Wavelet-Based Regression Techniques Open
Classical regression models are highly sensitive to outliers, heteroskedasticity, and high-frequency noise, which are common characteristics of financial and economic time series. Robust regression techniques have been developed to reduce …
View article: Modeling Non-Stationary Time Series Using Robust Wavelet-Based Regression Techniques
Modeling Non-Stationary Time Series Using Robust Wavelet-Based Regression Techniques Open
Classical regression models are highly sensitive to outliers, heteroskedasticity, and high-frequency noise, which are common characteristics of financial and economic time series. Robust regression techniques have been developed to reduce …
View article: Linear regression full models (respondents who passed the manipulation check).
Linear regression full models (respondents who passed the manipulation check). Open
Linear regression full models (respondents who passed the manipulation check).
View article: Linear regression full models (attentive respondents).
Linear regression full models (attentive respondents). Open
Linear regression full models (attentive respondents).
View article: Wavelet-Based Robust Regression for Non-Stationary Time Series Analysis
Wavelet-Based Robust Regression for Non-Stationary Time Series Analysis Open
View article: Softmax-Based Deep Neural Network in Regression
Softmax-Based Deep Neural Network in Regression Open
Regression is a fundamental problem in statistics and machine learning, where the true output is a continuous and stochastic function of the input. Regression methods model the output variables using training datasets composed of input-out…
View article: Wavelet-Based Robust Regression for Non-Stationary Time Series Analysis
Wavelet-Based Robust Regression for Non-Stationary Time Series Analysis Open
View article: Exploratory factor analysis with polychoric correlations and robust weighted least squares (WLSMV) estimation on two factors of the Persian version of the RSES (N = 270).
Exploratory factor analysis with polychoric correlations and robust weighted least squares (WLSMV) estimation on two factors of the Persian version of the RSES (N = 270). Open
Exploratory factor analysis with polychoric correlations and robust weighted least squares (WLSMV) estimation on two factors of the Persian version of the RSES (N = 270).
View article: Hybrid Robust Beta Regression Based on Support Vector Machines and Iterative Reweighted Least Squares
Hybrid Robust Beta Regression Based on Support Vector Machines and Iterative Reweighted Least Squares Open
In this paper, we examine and compare the performance of several beta regression approaches for response variables constrained to the (0,1) interval, focusing on robustness in the presence of outliers and nonlinear relationships. Since the…
View article: Bayesian conjugate analysis for federated statistical inference
Bayesian conjugate analysis for federated statistical inference Open
In many research settings, sufficiently large sample sizes can only be achieved by combining data from multiple collection sites. However, pooling individual participant data in a central server is often restricted due to regulatory constr…
View article: Bayesian conjugate analysis for federated statistical inference
Bayesian conjugate analysis for federated statistical inference Open
In many research settings, sufficiently large sample sizes can only be achieved by combining data from multiple collection sites. However, pooling individual participant data in a central server is often restricted due to regulatory constr…
View article: Robust Poisson regression for factors associated with being discharged alive among participants (N = 139).
Robust Poisson regression for factors associated with being discharged alive among participants (N = 139). Open
Robust Poisson regression for factors associated with being discharged alive among participants (N = 139).
View article: values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB).
values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB). Open
values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB).
View article: Empirical Performance of Nonparametric Regression with Heteroscedasticity
Empirical Performance of Nonparametric Regression with Heteroscedasticity Open
Heteroscedasticity is a well-known violation of an assumption in parametric regression analysis. In such cases, to handle this problem, a generalized least squares method is used. In this article, we have manifested the robustness of nonpa…
View article: Partial F-Statistics and Durban-Wu-Hausman test from the first Stage Regression of the Two Stage Least Squares (2SLS).
Partial F-Statistics and Durban-Wu-Hausman test from the first Stage Regression of the Two Stage Least Squares (2SLS). Open
Partial F-Statistics and Durban-Wu-Hausman test from the first Stage Regression of the Two Stage Least Squares (2SLS).
View article: Spectrally and temporally segmented regression of nuisance signals in high-speed resting-state fMRI
Spectrally and temporally segmented regression of nuisance signals in high-speed resting-state fMRI Open
Purpose Prior work has shown that whole-band linear regression of nuisance signals can introduce artifactual connectivity in high-frequency resting-state fMRI. Errors of motion regressors and non-stationarity of nuisance signals exacerbate…
View article: tahagill/Phredator: v1.1.0 - Adaptive Threshold Calibration
tahagill/Phredator: v1.1.0 - Adaptive Threshold Calibration Open
What's New in v1.1.0 New Features Adaptive Quality Threshold Calibration (AQTC) - Statistical algorithm using Median Absolute Deviation (MAD) for robust outlier detection Trend Analysis - Linear regression to detect quality degradation pat…
View article: tahagill/Phredator: v1.1.0 - Adaptive Threshold Calibration
tahagill/Phredator: v1.1.0 - Adaptive Threshold Calibration Open
What's New in v1.1.0 New Features Adaptive Quality Threshold Calibration (AQTC) - Statistical algorithm using Median Absolute Deviation (MAD) for robust outlier detection Trend Analysis - Linear regression to detect quality degradation pat…
View article: A robust logistic regression approach enhanced by hyperparameter optimization techniques through swarm intelligence and genetic algorithms: Advancing cancer diagnosis
A robust logistic regression approach enhanced by hyperparameter optimization techniques through swarm intelligence and genetic algorithms: Advancing cancer diagnosis Open
View article: INDUCTION AND APPLICATION RESEARCH ON LEAST SQUARES OPTIMIZATION TECHNIQUES IN MULTI-ASSET HEDGING
INDUCTION AND APPLICATION RESEARCH ON LEAST SQUARES OPTIMIZATION TECHNIQUES IN MULTI-ASSET HEDGING Open
Least squares hedging is one of the most widely used optimization tools in financial risk management due to its simplicity, analytical clarity, and strong empirical performance. As modern financial markets evolve toward greater complexity …
View article: INDUCTION AND APPLICATION RESEARCH ON LEAST SQUARES OPTIMIZATION TECHNIQUES IN MULTI-ASSET HEDGING
INDUCTION AND APPLICATION RESEARCH ON LEAST SQUARES OPTIMIZATION TECHNIQUES IN MULTI-ASSET HEDGING Open
Least squares hedging is one of the most widely used optimization tools in financial risk management due to its simplicity, analytical clarity, and strong empirical performance. As modern financial markets evolve toward greater complexity …
View article: InterpIoU: Robust bounding box regression loss within an interpolation-based IoU framework
InterpIoU: Robust bounding box regression loss within an interpolation-based IoU framework Open
View article: CLINICAL EVALUATION OF ROBUST OPTIMIZATION WITH REDUCED MARGINS IN IMPT FOR HEAD-AND-NECK CANCER
CLINICAL EVALUATION OF ROBUST OPTIMIZATION WITH REDUCED MARGINS IN IMPT FOR HEAD-AND-NECK CANCER Open
View article: Dose standardization for transcranial electrical stimulation: an accessible approach
Dose standardization for transcranial electrical stimulation: an accessible approach Open
Transcranial electrical stimulation (tES) is a widely used non-invasive brain stimulation technique. However, due to high inter-individual variability in the induced electric fields (E-fields), a fixed stimulation current delivers an incon…
View article: A Comparative Analysis of Regression Models for Predicting COVID-19 Mortality
A Comparative Analysis of Regression Models for Predicting COVID-19 Mortality Open
The rapid proliferation of COVID-19 data necessitated robust machine learning models for forecasting and analysis. This study aimed to identify the most suitable regression model for predicting COVID-19 mortality by comparing Simple Linear…
View article: Optimal Transport for Robust High-Dimensional Inference
Optimal Transport for Robust High-Dimensional Inference Open
The analysis of high-dimensional data presents significant challenges for classical statistical inference, which is often compromised by the curse of dimensionality and sensitivity to outliers. This paper introduces a novel framework for r…
View article: Optimal Transport for Robust High-Dimensional Inference
Optimal Transport for Robust High-Dimensional Inference Open
The analysis of high-dimensional data presents significant challenges for classical statistical inference, which is often compromised by the curse of dimensionality and sensitivity to outliers. This paper introduces a novel framework for r…
View article: Sparse-Smooth Spatially Varying Coefficient Quantile Regression
Sparse-Smooth Spatially Varying Coefficient Quantile Regression Open
We develop a convex framework for spatially varying coefficient quantile regression that, for each predictor, separates a location-invariant \emph{global} effect from a \emph{spatial deviation}. An adaptive group penalty selects whether a …
View article: Sparse-Smooth Spatially Varying Coefficient Quantile Regression
Sparse-Smooth Spatially Varying Coefficient Quantile Regression Open
We develop a convex framework for spatially varying coefficient quantile regression that, for each predictor, separates a location-invariant \emph{global} effect from a \emph{spatial deviation}. An adaptive group penalty selects whether a …
View article: Optimal Transport for Robust High-Dimensional Inference
Optimal Transport for Robust High-Dimensional Inference Open
The analysis of high-dimensional data presents significant challenges for classical statistical inference, which is often compromised by the curse of dimensionality and sensitivity to outliers. This paper introduces a novel framework for r…