JMASM43: TEEReg: Trimmed Elemental Estimation (R)

Bootstrap Confidence Intervals and Coverage Probabilities of Regression Parameter Estimates Using Trimmed Elemental Estimation

Robust Frequency Estimation Using Elemental Sets∗

The extraction of sinusoidal signals from time-series data is a classic problem of ongoing interest in the statistics and signal processing literatures. Obtaining least squares estimates is difficult because the sum of squares has local minima O(1/n) apart in the frequencies. In practice the frequencies are often estimated using ad hoc and inefficient methods. Problems of data quality have rece...

Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improv...

Title L-moments, Trimmed L-moments, L-comoments, and Many Distributions Version 0.97.4 Depends R (> = 2.7.0), utils Date 2009-10-28

Description The package implements the statistical theory of L-moments including L-moment estimation, probability-weighted moment estimation, parameter estimation for numerous familiar and not-so-familiar distributions, and L-moment estimation for the same distributions from the parameters. L-moments are derived from the expectations of order statistics and are linear with respect to the probab...

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso

Gaussian Graphical Models (GGMs) are popular tools for studying network structures. However, many modern applications such as gene network discovery and social interactions analysis often involve high-dimensional noisy data with outliers or heavier tails than the Gaussian distribution. In this paper, we propose the Trimmed Graphical Lasso for robust estimation of sparse GGMs. Our method guards ...