Depth-weighted robust multivariate regression with application to sparse data

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...

Multivariate factorizable expectile regression with application to fMRI data

A multivariate expectile regression model is proposed to analyze the tail events of large cross-sectional and spatial data, when the tail events are linked by a latent factor structure. The computational advantage of the method is demonstrated, and the estimation risk is analyzed for every fixed number of iteration and fixed sample size, when the latent factors are either exactly or approximate...

Bayesian Robust Multivariate Linear Regression With Incomplete Data

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Sparse Multivariate Regression With Covariance Estimation.

We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization a...

Robust Multivariate Regression