A Comparative Study of Likelihood Ratio Based Forensic Text Comparison Procedures: Multivariate Kernel Density with Lexical Features vs. Word N-grams vs. Character N-grams

2014 ◽  
Author(s):  
Shunichi Ishihara
Keyword(s):  
Comparative Study ◽  
Likelihood Ratio ◽  
Kernel Density

Kernel density‐based likelihood ratio tests for linear regression models

2020 ◽  
Vol 40 (1) ◽  
pp. 119-132
Author(s):  
Feifei Yan ◽  
Qing‐Song Xu ◽  
Man‐Lai Tang ◽  
Ziqi Chen
Keyword(s):  
Linear Regression ◽  
Likelihood Ratio ◽  
Regression Models ◽  
Kernel Density ◽  
Likelihood Ratio Tests ◽  
Linear Regression Models

scholarly journals A Comparative Study for Bandwidth Selection in Kernel Density Estimation

2010 ◽  
Vol 9 (1) ◽  
pp. 263-273
Author(s):  
Omar M. Eidous ◽  
Mohammad Abd Alrahem Shafeq Marie ◽  
Mohammed H. Baker Al-Haj Ebrahem
Keyword(s):  
Comparative Study ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Bandwidth Selection ◽  
Kernel Density

scholarly journals Interest of Boundary Kernel Density Techniques in Evaluating an Approximation Error of Queueing Systems Characteristics

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Aïcha Bareche ◽  
Djamil Aïssani
Keyword(s):  
Comparative Study ◽  
Density Function ◽  
Numerical Evaluation ◽  
Queueing Systems ◽  
Approximation Error ◽  
Kernel Density ◽  
Nonparametric Methods ◽  
Simulation Studies ◽  
Stationary Distributions ◽  
Numerical Examples

We show the interest of nonparametric methods taking into account the boundary correction techniques for a numerical evaluation of an approximation error between the stationary distributions ofG/M/1andM/M/1queueing systems, when the density function of the general arrivals lawGin theG/M/1system is unknown and defined on a bounded support. To compute this error, we use two kinds of norms: theL1norm and the weight norm. Numerical examples based on simulation studies are presented for the two cases of considered norms. A comparative study of the results has been provided.

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