Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson estimator (NWK) is one the most important nonparametric that often regression models with fixed bandwidth. In this article, we consider four new Proposed Adaptive Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Deviation) rather t...

PURPOSE Multiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify t...

BACKGROUND 3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician. METHODS This paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-f...

In this work, the primary compressive strength components of human femur trabecular bone are qualitatively assessed using image processing and wavelet analysis. The Primary Compressive (PC) component in planar radiographic femur trabecular images (N=50) is delineated by semi-automatic image processing procedure. Auto threshold binarization algorithm is employed to recognize the presence of mine...

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of L1 regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviatio...

We propose a single-index diiusion model in this paper. This model can avoid thècurse of dimensionality' in estimating a multivariate nonparametric conditional variance. We adopt an absolute deviation estimation method to estimate the model. Comparing with the commonly used estimators, the absolute deviation estimator is more stable and ef-cient. Some simulations and applications to real data a...

In this paper we consider diierent aspects of robust 1-median problems on a tree network with uncertain or dynamically changing edge lengths and vertex weights which can also take negative values. The dynamic nature of a parameter is modeled by a linear function of time. A linear algorithm is designed for the absolute dynamic robust 1-median problem on a tree. The dynamic robust deviation 1-med...

We propose a single-index di usion model in this paper. This model can avoid the `curse of dimensionality' in estimating a multivariate nonparametric conditional variance. We adopt an absolute deviation estimation method to estimate the model. Comparing with the commonly used estimators, the absolute deviation estimator is more stable and efcient. Some simulations and applications to real data ...

We study nonparametric inference of stochastic models driven by stable Lévy processes. We introduce a nonparametric estimator of the stable index that achieves the parametric √ n rate of convergence. For the volatility function, due to the heavy-tailedness, the classical least-squares method is not applicable. We then propose a nonparametric least-absolute-deviation or median-quantile estimator...