An extended quadrature-based moment method with log-normal kernel density functions

2015 ◽  
Vol 131 ◽  
pp. 323-339 ◽  
Author(s):  
E. Madadi-Kandjani ◽  
A. Passalacqua
2016 ◽  
Vol 91 (1-2) ◽  
pp. 141-159 ◽  
Author(s):  
Arthur Charpentier ◽  
Emmanuel Flachaire

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.


2020 ◽  
Vol 11 (2) ◽  
pp. 137-145
Author(s):  
Mărgărit-Mircea NISTOR ◽  
Alexandru-Sabin NICULA ◽  
Ştefan DEZSI ◽  
Dănuţ PETREA ◽  
Shankar Acharya KAMARAJUGEDDA ◽  
...  

The variation of tourism flow and its spatial representation are indispensable for transport companies, accommodation facilities and future estimations regarding the international arrivals. The major implication for tourism flow mapping is related to the country of origin of tourists, their liquid assets, and tourism statistical database. The approach of tourism flow mapping representation, at least using lines and density, should be based on the spatial characteristics of the objects. In this study, the database consisting of international arrivals in different cities of Romania was used as an example. Thus, GIS-based Kernel density of the tourists’ flow was proposed. To illustrate the international demand, data on arrivals for 33 countries over the period 2015-2017 were used. ‘XY To Line’ and ‘Kernel Density’ functions served to create the convergence lines between the origin countries and Romania. The very high density was found for the European countries with an increase of 13% and 25% between 2015 and 2016, as well as between 2015 and 2017. Map analysis indicated an increase of the density area by 0.7% for 2016 and 1.7% for 2017. The proposed methods, including lines and density, contribute to the mapping of the flow of the international arrivals in Romania.


Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 873
Author(s):  
Jinxin Wang ◽  
Chi Zhang ◽  
Xiuzhen Ma ◽  
Zhongwei Wang ◽  
Yuandong Xu ◽  
...  

The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.


2020 ◽  
pp. 193672442097534
Author(s):  
Josefa Ramoni-Perazzi ◽  
Giampaolo Orlandoni-Merli

Informality is a common problem in Colombia, with almost 50 percent of the workers employed in this sector. This may be a solution for unemployment, but it is a lose/lose game unless the individuals have a comparative advantage in the informal sector. This article uses information from the Colombian Great Integrated Household Survey (GIHS) to analyze the wage gap between formal and informal urban sectors in two different periods, 2008:4 and 2017:4, using a semiparametric approach. Kernel density functions by groups are estimated; counterfactuals are generated by weighting wages of informal sector workers by their probability of working in the formal sector, to estimate how much an informal sector worker could make if treated as formal, according to his characteristics. The results indicate that only some groups (self-employed and some entrepreneurs) are better off if formalized.


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