Article history: Received date: 13 September, 2016 Review date: 2 October 2016 Accepted date:20 November 2016 Printed on line: 5 January Purpose: The present study was done to introduce an efficient tool in the field of moral behavior. Material & Method: method of the study was correlational, its approach was test developing and its population was students of Islamic Azad University- Ast...

Groundwater resource is vital for industrial, drinking and irrigation purposes in the Dagujia river basin, China. The objective of this work was to comprehensively assess hydrochemical characteristics evolution processes Quaternary aquifer (QA) bedrock (BA) basin using statistical methods plots. In total, 56 groundwater samples were collected from QA (34 samples) BA (22 samples). addition, comb...

ABSTRACT: Quantitative structure-activity relationship (QSAR) study on the piperidone-grafted mono- and bis-spirooxindole-hexahydropyrrolizines as potent butyrylcholinestrase (BuChE) inhibitors were carried out using statistical methods, molecular dynamics and molecular docking simulation. QSAR methodologies, including classification and regression tree (CART), multiple linear regression (MLR),...

In this paper, we consider and introduce methods for robust principal component analysis (PCA), including also cases where there are missing values in the data. PCA is a widely applied standard statistical method for data preprocessing, compression, and analysis. It is based on the second-order statistics of the data and is optimal for Gaussian data, but is often applied to data sets having unk...

Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...

The rapid development of information technologies enables researchers to collect and store functional data at a low cost. As a result, the quantitative analysis of functional data becomes practically feasible, which naturally calls for new statistical methods to serve such a purpose. To this end, we propose a new model, namely, “Mixture of Gaussian Processes” in this paper. Our method can be vi...

The principal component analysis (also named Karhunen–Loève transformation) and the factor analysis are both tools of the multivariate statistics, more precisely the exploratory data analysis. They are used e.g. in data mining or machine learning. Although they share the same goal, they reach it with different methods. Over the years, some misunderstandings came up, how these methods differ fro...

Estimating design flow in ungauged basins is a task frequently encountered in the design and planning of hydraulic and water resources engineering. Regionalization is a way to deal with this issue. In this study, a regional formula for peak flows was established using gauged flows and basin topographic characteristics in order to estimate the design flows in ungauged areas within the homogeneou...

In this paper, we propose a new classification technique based on the Minimum Component Analysis (MCA) instead of the traditional Principal Components Analysis (PCA). Most existing classification techniques based on PCA represent a class by its principal component. However, the principal component is not always the best choice since there is a high possibility for classes to overlap with each o...