This paper presents the results of the statistical analysis of microbiological, physical and chemical parameters related to the quality of the water used in rice fields in Southern Brazil. Data were collected during three consecutive crop years, within structure of a comprehensive monitoring program. The indicators used were: potential hydrogen, electrical conductivity, turbidity, nitrogen, pho...

The performance results of the athletes competed in the 1988-2008 Olympic Games were analyzed (n = 166). The data were obtained from the IAAF official protocols. In the principal component analysis, the first three principal components explained 70% of the total variance. In the 1st principal component (with 43.1% of total variance explained) the largest factor loadings were for 100m (0.89), 40...

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analys...

Principal component analysis is a valid method used for data compression and information extraction in a given set of experiments. It is a well-known classical data analysis technique. There are a number of algorithms for solving the problems, some scaling better than others. Wheat ranks as the staple food of most of the nations as well as an agent of poverty reduction, food security and world ...

Environmetric techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA) were applied for the assessment of spatial and temporal variations of a large complex water quality data set of the Songkhram River Basin, generated during 15 years (1995–2009) by monitoring of 17 parameters at 5 different sites. Hierarchical CA grouped...

This statistical study has been carried out to examine and interpret the income types that make up sources of students. For this purpose, a data set created using 14 European countries included all for 9 variables. These have chosen as they did not contain missing each variable. Initially, factor analysis, which is method suitable purpose study, applied set. Then, principal component analysis (...

In this paper, we analyze the performance of a semiparametric principal component analysis named Copula Component Analysis (COCA) (Han & Liu, 2012) when the data are dependent. The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. We study the scenario where the observations are drawn from non-i.i.d. processes ...

mobile ad-hoc networks (manets) by contrast of other networks have more vulnerability because of having nature properties such as dynamic topology and no infrastructure. therefore, a considerable challenge for these networks, is a method expansion that to be able to specify anomalies with high accuracy at network dynamic topology alternation. in this paper, two methods proposed for dynamic anom...

We consider the problem of outlier rejection in single subspace learning. Classical approaches work with a direct representation of the subspace, and are thus efficient when the subspace dimension is small. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such it is particularly suitable for high-dimensional subspaces. We pose th...

The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. application of PCA financial setting associated with several difficulties, such as numerical instability nonstationarity. We attempt to resolve them by proposing two new variants PCA: an iterated (IPCA) exponentially weighted moving (EWMPCA). Both rely on the Ogita-Aishima iter...