Density estimation and application to robust multiuser detection in heavy tailed noise

2003 ◽  
Author(s):  
D.S. Pham ◽  
A.M. Zoubir
Keyword(s):  
Density Estimation ◽  
Multiuser Detection ◽  
Heavy Tailed

scholarly journals Log-Transform Kernel Density Estimation of Income Distribution

2016 ◽  
Vol 91 (1-2) ◽  
pp. 141-159 ◽  
Author(s):  
Arthur Charpentier ◽  
Emmanuel Flachaire
Keyword(s):  
Income Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Methods ◽  
Density Functions ◽  
Income Distributions ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Positive Support

Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.

scholarly journals Kernel density estimation for heavy-tailed distributions using the champernowne transformation

Statistics ◽  
2005 ◽  
Vol 39 (6) ◽  
pp. 503-516 ◽  
Author(s):  
Tine Buch-larsen ◽  
Jens Perch Nielsen ◽  
Montserrat Guillén ◽  
Catalina Bolancé
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed

scholarly journals Moment-Based Density Approximation Techniques as Applied to Heavy-tailed Distributions

2019 ◽  
Vol 8 (3) ◽  
pp. 1
Author(s):  
John Sang Jin Kang ◽  
Serge B. Provost ◽  
Jiandong Ren
Keyword(s):  
Density Estimation ◽  
Density Functions ◽  
Density Approximation ◽  
Simulation Studies ◽  
Esscher Transform ◽  
Approximation Techniques ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Made In

Several advances are made in connection with the approximation and estimation of heavy-tailed distributions. It is first explained that on initially applying the Esscher transform to heavy-tailed density functions such as the Pareto, Studentt and Cauchy, said densities can be approximated by employing a certain moment-based methodology. Alternatively, density approximants can be obtained by appropriately truncating such distributions or mapping them onto finite supports. These techniques are then extended to the context of density estimation, their validity being demonstrated by means of simulation studies. As well, illustrative actuarial examples are presented.

Multiuser Detection in Heavy Tailed Noise

2002 ◽  
Vol 12 (2-3) ◽  
pp. 262-273 ◽  
Author(s):  
Abdelhak M. Zoubir ◽  
Ramon F. Brcich
Keyword(s):  
Multiuser Detection ◽  
Heavy Tailed

Body tail adaptive kernel density estimation for nonnegative heavy-tailed data

2021 ◽  
Vol 27 (1) ◽  
pp. 57-69
Author(s):  
Yasmina Ziane ◽  
Nabil Zougab ◽  
Smail Adjabi
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Kernel Estimator ◽  
Real Data ◽  
Density Region ◽  
Data Set ◽  
Bandwidth Parameter ◽  
Adaptive Kernel ◽  
Heavy Tailed

Abstract In this paper, we consider the procedure for deriving variable bandwidth in univariate kernel density estimation for nonnegative heavy-tailed (HT) data. These procedures consider the Birnbaum–Saunders power-exponential (BS-PE) kernel estimator and the bayesian approach that treats the adaptive bandwidths. We adapt an algorithm that subdivides the HT data set into two regions, high density region (HDR) and low-density region (LDR), and we assign a bandwidth parameter for each region. They are derived by using a Monte Carlo Markov chain (MCMC) sampling algorithm. A series of simulation studies and real data are realized for evaluating the performance of a procedure proposed.

Sign in / Sign up

Export Citation Format

Share Document