Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: The Case Study of Potenza Hilltop Town (Southern Italy)

2008 ◽  
pp. 415-429 ◽  
Author(s):  
Maria Danese ◽  
Maurizio Lazzari ◽  
Beniamino Murgante
Keyword(s):  
Risk Analysis ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Seismic Risk ◽  
Southern Italy ◽  
Kernel Density ◽  
Estimation Methods ◽  
Seismic Risk Analysis ◽  
Geostatistical Approach

Collaborative project planning: A case study of seismic risk analysis using an e-engineering hub

2006 ◽  
Vol 57 (3) ◽  
pp. 218-230 ◽  
Author(s):  
Z. Ren ◽  
C.J. Anumba ◽  
T.M. Hassan ◽  
G. Augenbroe ◽  
M. Mangini
Keyword(s):  
Risk Analysis ◽  
Seismic Risk ◽  
Project Planning ◽  
Seismic Risk Analysis ◽  
Collaborative Project

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 Application of Kernel Density Estimation in Lamb Wave-Based Damage Detection

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Long Yu ◽  
Zhongqing Su
Keyword(s):  
Damage Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Lamb Wave ◽  
Kernel Density ◽  
Nonparametric Methods ◽  
Measured Data ◽  
Consensus Algorithm ◽  
Estimation Methods ◽  
Empirical Methods

The present work concerns the estimation of the probability density function (p.d.f.) of measured data in the Lamb wave-based damage detection. Although there was a number of research work which focused on the consensus algorithm of combining all the results of individual sensors, the p.d.f. of measured data, which was the fundamental part of the probability-based method, was still given by experience in existing work. Based on the analysis about the noise-induced errors in measured data, it was learned that the type of distribution was related with the level of noise. In the case of weak noise, the p.d.f. of measured data could be considered as the normal distribution. The empirical methods could give satisfied estimating results. However, in the case of strong noise, the p.d.f. was complex and did not belong to any type of common distribution function. Nonparametric methods, therefore, were needed. As the most popular nonparametric method, kernel density estimation was introduced. In order to demonstrate the performance of the kernel density estimation methods, a numerical model was built to generate the signals of Lamb waves. Three levels of white Gaussian noise were intentionally added into the simulated signals. The estimation results showed that the nonparametric methods outperformed the empirical methods in terms of accuracy.

scholarly journals Exploration of diffusion kernel density estimation in agricultural drought risk analysis: a case study in Shandong, China

2015 ◽  
Vol 3 (11) ◽  
pp. 6757-6789
Author(s):  
W. Chen ◽  
Z. Shao ◽  
L. K. Tiong
Keyword(s):  
Risk Analysis ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Standardized Precipitation Index ◽  
Kernel Density ◽  
Agricultural Drought ◽  
Gaussian Kernel ◽  
Spatial And Temporal Variation ◽  
Drought Risk ◽  
Diffusion Kernel

Abstract. Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based) drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE) and diffusion kernel density estimation (DKDE) are carried out to analyze the effect of drought intensity and drought duration. The results show that DKDE is relatively more accurate without boundary-leakage. Combined with the GIS technique, the drought risk is presented which reveals the spatial and temporal variation of agricultural droughts for corn in Shandong. The estimation provides a different way to study the occurrence frequency and severity of drought risk from multiple perspectives.

Favorability functions based on kernel density estimation for logistic models: A case study

2008 ◽  
Vol 52 (9) ◽  
pp. 4533-4543 ◽  
Author(s):  
Ana Colubi ◽  
Gil González-Rodríguez ◽  
María José Domínguez-Cuesta ◽  
Montserrat Jiménez-Sánchez
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Logistic Models ◽  
Kernel Density
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