Unrestricted multivariate medians for adaptive filtering of color images

Reduction of impulse noise in color images is a fundamental task in the image processing field. A number of approaches have been proposed to solve this problem in literature, and many of them rely on some multivariate median computed on a relevant image window. However, little attention has been paid to the comparative assessment of the distinct medians that can be used for this purpose. In this paper we carry out such a study, and its conclusions lead us to design a new image denoising procedure. Quantitative and qualitative results are shown, which demonstrate the advantages of our method in terms of noise reduction, detail preservation and stability with respect to a selection of well-known proposals.Presentado en el IX Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

A novel three-class ROC method for eQTL analysis

The problem of identifying genetic factors underlying complex and quantitative traits such as height, weight and disease susceptibility in natural populations has become a major theme of research in recent years. Aiming at revealing the inter-dependency and causal relationship between the underlying genotypes and observed phenotypes, researchers from different areas have developed a variety of methods for expression quantitative trait loci (eQTL) mapping. Most of these methods rely on resampling-based algorithms that are computationally very expensive. To overcome the disadvantages of the current techniques, we propose a novel nonparametric method based on the volume under surface (VUS) within the framework of three-class receiver operating characteristic (ROC) analysis. With the fast algorithms developed, we can reduce the computation time of the genomewide analysis from several months down to several days. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the International Conference on Machine Learning and Cybernetics, 2010, v. 6, p. 3056-306

Unrestricted multivariate medians for adaptive filtering of color images

Reduction of impulse noise in color images is a fundamental task in the image processing field. A number of approaches have been proposed to solve this problem in literature, and many of them rely on some multivariate median computed on a relevant image window. However, little attention has been paid to the comparative assessment of the distinct medians that can be used for this purpose. In this paper we carry out such a study, and its conclusions lead us to design a new image denoising procedure. Quantitative and qualitative results are shown, which demonstrate the advantages of our method in terms of noise reduction, detail preservation and stability with respect to a selection of well-known proposals.Presentado en el IX Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Two symmetric and computationally efficient Gini correlations

© 2020 Courtney Vanderford et al., published by De Gruyter. Standard Gini correlation plays an important role in measuring the dependence between random variables with heavy-tailed distributions. It is based on the covariance between one variable and the rank of the other. Hence for each pair of random variables, there are two Gini correlations and they are not equal in general, which brings a substantial difficulty in interpretation. Recently, Sang et al (2016) proposed a symmetric Gini correlation based on the joint spatial rank function with a computation cost of O(n2) where n is the sample size. In this paper, we study two symmetric and computationally efficient Gini correlations with the computational complexity of O(n log n). The properties of the new symmetric Gini correlations are explored. The influence function approach is utilized to study the robustness and the asymptotic behavior of these correlations. The asymptotic relative efficiencies are considered to compare several popular correlations under symmetric distributions with different tail-heaviness as well as an asymmetric log-normal distribution. Simulation and real data application are conducted to demonstrate the desirable performance of the two new symmetric Gini correlations

Memory Properties Of Transformations Of Linear Processes And Symmetric Gini Correlation

A large class of time series processes can be modeled by linear processes, including a subset of the fractional ARIMA process. Transformation of linear processes is one of the most popular topics in univariate time-series analysis in recent years. In this dissertation, we study the memory properties of transformations of linear processes. Our results show that the transformations of short-memory time series still have short-memory and the transformation of long-memory time series may have different weaker memory parameters which depend on the power rank of the transformation. In particular, we provide the memory parameters of the FARIMA (p,d,q) processes. As an example, the memory properties of call option processes at different strike prices are discussed in details. When we develop the memory properties of transformation of linear processes, we use the Pearson correlation to measure the memory. Correlation is another big topic in statistics, which is used to measure the dependence of stochastic processes or random variables. Standard Gini correlation is one of the correlations to measure the dependence between random variables with heavy tailed distributions. However, the asymmetry of Gini covariance and correlation brings a substantial difficulty in interpretation. In this dissertation, we propose a symmetric Gini-type covariance and correlation (ρg) based on the joint rank function. The proposed correlation ρg is symmetric and is more robust than the Pearson correlation but less robust than the Kendall\u27s τ correlation in terms of influence functions. Furthermore, we establish the relationship between ρg and the linear correlation ρ for a class of random vectors in the family of elliptical distributions, which allows us to estimate ρ based on estimation of ρg. We compare asymptotic efficiencies of linear correlation estimators based on the symmetric Gini, and the proposed measure ρg shows superior finite sample performance, which makes it attractive in applications

Asymptotic mean and variance of Gini correlation for bivariate normal samples

This paper derives the asymptotic analytical forms of the mean and variance of the Gini correlation (GC) with respect to samples drawn from bivariate normal populations. The asymptotic relative efficiency (ARE) of the Gini correlation to Pearson's product moment correlation coefficient (PPMCC) is investigated under the normal assumptions. To gain further insight into GC, we also compare the Gini correlation to other two closely related correlation coefficients, namely, the order statistics correlation coefficient (OSCC) and Spearman's rho (SR). Theoretical and simulation results suggest that the performance of GC lies in between those of OSCC and SR when estimating the correlation coefficient of the bivariate normal population. The newly found theoretical results along with other desirable properties enable GC to be a useful alternative to the existing coefficients, especially when one wants to make a trade-off between the efficiency and robustness to monotone nonlinearity

The Impact of Corporate Governance and external audit on controlling discretionary accruals: A study of impacts on earnings management based on FTSE350, UK.

While the interest of shareholders contradicts with the interests of the managers, agency problem appears. However, the principle of the agency theory is to establish the relationship between the shareholders and managers; and this thesis relies on the involvement of corporate governance and external audit who can resolve the issues between them. The main aim of this study is to identify the impact of corporate governance and external audit on controlling the discretionary accrual based on the FTSE350, in the UK. This study has considered the performance matched discretionary accruals to measure the magnitude of the discretionary accruals. The monitoring devices are established in the segmenting the models in two different categories; these are corporate governance and external audit. There are two models; first model and second model, formed and the hypotheses are created based on those attributes of the corporate governance and external audit. This study has considered the data from FTSE350 index of the UK; from 2014 – 2019. The variables of the first model; non-executive director’s fees and block holders are positively associated while managerial ownership and non-executive director’s meeting are negatively associated at 0.05 significant level. Further, remuneration committee independence is posi- tively associated with earnings management at P-value<0.1. On the other hand, the variables of second model, non-audit fee is positively associated whereas audit fee, Auditors with industrial specialism, audit expertise are negatively associated at P-value<0.05