This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is...

Meta-analysis is a quantitative method for synthesising results from individual studies to estimate an overall effect. Since its introduction in the social sciences by Glass [11], there has been an enormous increase, both in epidemiology and in clinical trials, in the use of meta-analysis as a statistical technique for combining the results of individual analyses; and it has become an important...

This article is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks. Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of Principal Component Analysis....

Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) industrial induced draft (ID) fan. The high vibration issues in ID fans cause failure impellers and, sometimes, complete breakdown fan-motor system. system equipment should be reliable avo...

abstract despite the significance of climate change assessment on regional planning of a basin, most of the previous researches have been focused on the point assessment of this phenomenon. this paper uses statistical tests as well as regional assessment to investigate the impact of climate change on the gorganroud-gharehsou basin. in this regard, various tests including man-kendall, cumulating...

In this study, multivariate statistical methods including factor, principal component and cluster analysis were applied to surface water quality data sets obtained from the Tahtali River Basin, Turkey. Factor and principal components analysis results revealed that surface water quality was mainly controlled by agricultural uses and domestic discharges. Cluster analysis generated two clusters. B...

Principal Component Analysis (PCA) finds a linear mapping and maximizes the variance of the data which makes PCA sensitive to outliers and may cause wrong eigendirection. In this paper, we propose techniques to solve this problem; we use the data-centering method and reestimate the covariance matrix using robust statistic techniques such as median, robust scaling which is a booster to datacente...

The analysis of polychoric correlations via principal component analysis and exploratory factor analysis are well-known approaches to determine the dimensionality of ordered categorical items. However, the application of these approaches has been considered as critical due to the possible indefiniteness of the polychoric correlation matrix. A possible solution to this problem is the application...

In genome-wide association studies, penalization is an important approach for identifying genetic markers associated with disease. Motivated by the fact that there exists natural grouping structure in single nucleotide polymorphisms and, more importantly, such groups are correlated, we propose a new penalization method for group variable selection which can properly accommodate the correlation ...