Due to the fact that mediation model involves several linear regression equations, there is concern not only when the data contain observations that are extreme in the response variable but also in the regressor space, namely the leverage points. The Diagnostic Robust Generalized Potentials (DRGP) procedure in multiple linear regression incorporated the Robust Mahanalobis Distance based on the ...

We present a technique for efficiently constructing a “clean” texture map of a partially occluded building facade from a series of images taken by a moving camera. After a robust registration procedure, building regions blocked by trees, signs, people, and other foreground objects are automatically inferred via the median absolute deviation of colors from different source images mapping to the ...

We propose a least absolute deviation model for obtaining OWA operator weights: Minimize ∑i=n−1 i=1 |wi − wi−1| subject to orness(W ) = ∑n i=1 n−i n−1wi = α, 0 ≤ α ≤ 1, w1 + · · ·+ wn = 1, 0 ≤ wi, i = 1, · · · , n. Recently, the extended minimax disparity problem was proved by Hong [Fuzzy Sets and Systems, 168 (2011) 35-46]. In this paper, we investigated the equivalence of the solutions for th...

An autoregressive-moving average model in which all of the roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximation to the likelihood of the model in the ca...

In this work, a novel wavelet-based denoising method for electrocardiogram signal is proposed. A threshold is derived by considering energy contribution of awavelet subband, noise variance which is based on a novel Gaussian measure, Kurtosis, and number of samples. The robust noise estimator, median absolute deviation, is scaled by a normalized wavelet subband Kurtosis instead of conventional s...

Although regression quantiles (RQs) are increasingly becoming popular, they are still playing a second fiddle role to the ordinary least squares estimator like their robust counterparts due to the perceived complexity of the robust statistical methodology. In order to make them attractive to statistical practitioners, an endeavor to studentize robust estimators has been undertaken by some resea...