Due to factors such as climate change,forest fire,plague of insects on lumber quality,it is important to update procedures in American Society for Testing and Materials(ASTM)Standard D1990(adopted in
Variable selection is central to sparse modeling,and many methods have been proposed under various model assumptions.In this talk,we will present a model-free variable selection method that allows for
Data contamination refers to the phenomenon where part of the data is randomly replaced by data generated from an unknown distribution.It applies to a wide range of real world problems related to data
This talk considers an improvement of variational mode decomposition(VMD)in the presence of missing values.VMD developed by Dragomiretskiy and Zosso(2014)efficiently decomposes a signal into some mean
We propose a cox model with latent variables to investigate the observed and latent risk factors of the failure time of interest.Each latent risk factor is characterized by correlated observed variabl
Modern biological techniques have led to a variety of types of data,which are often used to identify important biomarkers for certain diseases based on appropriate statistical methods,such as feature
Two-level factorial designs are considered under a conditional model with a pair of conditional and conditioning factors.Such a pair can arise in many practical situations.With properly defined main e
A commonly used assumption in spatial analysis is that the spatial process is isotropic,implying that only the distance,but not the orientation matters in the specification of the spatial structure.Ho
Sample surveys are widely used to obtain information about totals,means,medians,and other parameters of finite populations.In many applications,similar information is desired for subpopulations such a
The key of tip growth in eukaryotes is the polarized distribution on plasma membrane of a particle named ROP1.This distribution is the result of a positive feedback loop,whose mechanism can be describ