Dimensioni e direttrici del mutamento socio-economico in prospettiva territoriale dal 1951 a oggi

2013 ◽  
pp. 78-92
Author(s):  
Domenico Maddaloni ◽  
Fiorenzo Parziale
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Southern Italy ◽  
Principal Component ◽  
Research Strategy ◽  
Two Phase ◽  
Economic Changes ◽  
Local Contexts ◽  
Italian Provinces ◽  
History Of

In this study we go back to examine the economic and sociological changes throughout the local contexts and divisions of our country. The instrument used is a research strategy that combines a two-phase principal component analysis developed by Di Franco and Marradi with multiple linear regression. From data inherent to four key moments in the recent history of Southern Italy and the whole country - 1951, 1971, 1991 and 2007 - we obtain four «photographs» of dimensions that clarify the structure of the selected variables. We then propose two models of path analysis that underline the causal links between the factors emerged from the PCA, in order to reconstruct the socio-economic changes in the Italian provinces from 1951 to 2007.

scholarly journals Ecological Risk Assessment and Contamination History of Heavy Metals in the Sediments of Chagan Lake, Northeast China

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 894
Author(s):  
Panfeng Liu ◽  
Chaojie Zheng ◽  
Meilan Wen ◽  
Xianrong Luo ◽  
Zhiqiang Wu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Ecological Risk ◽  
Surface Sediment ◽  
Northeast China ◽  
Principal Component ◽  
Potential Ecological Risk ◽  
210Pb Dating ◽  
History Of ◽  
Chagan Lake

The study deals with the spatio-temporal distribution of heavy metals in the sediments of Chagan lake, Northeast China. The pollution history of heavy metals is studied simultaneously through the 210Pb dating method by analyzing the characteristic of As, Hg, Cd, Cr, Ni, Cu, Pb, and Zn concentration-depth profiles. The potential ecological risk index (RI) and geo-accumulation index (Igeo) were used to evaluate the contamination degree. Principal component analysis (PCA), based on the logarithmic transformation and isometric log-ratio (ilr) transformed data, was applied with the aim of identifying the sources of heavy metals. The element concentrations show that the heavy metals are enriched in the surface sediment and sediment core with a varying degree, which is higher in the surficial residue. The results of Igeo indicate that the Cd and Hg in the surface sediment have reached a slightly contaminated level while other elements, uncontaminated. The results of RI show that the study area can be classified as an area with moderate ecological risk in which Cd and Hg mostly contribute to the overall risk. For the sediment core, the 210Pb dating results accurately reflect the sedimentary history over 153 years. From two evaluation indices (RI and Igeo) calculated by element concentration, there is no contamination, and the potential ecological risk is low during this period. The comparative study between raw and ilr transformed data shows that the closure effect of the raw data can be eliminated by ilr transformation. After that, the components obtained by robust principal component analysis (RPCA) are more representative than those obtained by PCA, both based on ilr transformed dataset, after eliminating the influence of outliers. Based on ilr transformed data with RPCA, three primary sources could be inferred: Cr, Ni, As, Zn, and Cu are mainly derived from natural sources; the main source of Cd and Hg are associated with agricultural activities and energy development; as for Pb, it originated from traffic and coal-burning activities, which is consistent with the fact that the development of tourism, fishery, and agriculture industries has led to the continuous increasing levels of anthropogenic Pb in Chagan Lake. The summarized results and conclusions will undoubtedly enhance the governmental awareness of heavy metal pollution and facilitate appropriate pollution control measures in Chagan Lake.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

The Quantitative Evaluation on the Response of Climate Change upon the Sustainability of the Agricultural Production

2010 ◽  
Vol 129-131 ◽  
pp. 1161-1165
Author(s):  
Lin Chun Hou ◽  
Hui Qin Li
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Agricultural Production ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Environmental Condition ◽  
Land Utilization ◽  
Agricultural Society ◽  
Per Capita

The aim: quantitatively evaluate the response of climate change upon the sustainability of the agricultural production. The method: the paper selected two regions (Hubei and shan’xi province) which represented different climate environment, utilized modern statistic data, Principal Component Analysis and multivariate linear regression to quantitatively evaluate the influence of climate change upon agricultural production through isolating climate environment from arable area, land utilization and management and landform and so on. The conclusion: The study indicated that when environmental condition turned good to agriculture, the function of environmental condition to agriculture relatively decreased; the capability of agricultural society and production decreased too, and people could select the land to cultivate, where agricultural productivity is higher. And that when environmental condition turned bad to agriculture, the function of environmental condition to agriculture relatively increased; the capability of agricultural society and production increased, too; people could not put emphasis on the land where agricultural productivity is higher, whereas focused on productivity per capita.

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

scholarly journals Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

2018 ◽  
Vol 26 (0) ◽  
pp. 170-176 ◽  
Author(s):  
Stephen J.H. Yang ◽  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Jeff C.H. Huang ◽  
Hiroaki Ogata ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Academic Performance ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals Relationship between the Physical Properties and Hand of Jean Fabric

2016 ◽  
Vol 16 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Atsushi Kawamura ◽  
Chunhong Zhu ◽  
Julie Peiffer ◽  
KyoungOk Kim ◽  
Yi Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Evaluation System ◽  
Principal Component ◽  
Semantic Differential ◽  
Differential Method ◽  
Fabric Hand

Abstract We investigated the distinctive characteristics of jean fabrics (denim fabrics obtained from jeans) and compared the physical properties and the hand. We used 13 kinds of jean fabric from commercial jeans and 26 other fabric types. The physical properties were measured using the Kawabata evaluation system, and the fabric hand was evaluated by 20 subjects using a semantic differential method. To characterise the hand of jean fabrics compared with other fabrics, we used principal component analysis and obtained three principal components. We found that jean fabrics were characterised by the second principal component, which was affected by feelings of thickness and weight. We further characterised the jean fabrics according to ‘softness & smoothness’ and ‘non-fullness’, depending on country of origin and type of manufacturer. The three principal components were analysed using multiple linear regression to characterise the components according to the physical properties. We explained the hand of fabrics including jean fabrics using its association with physical properties.

scholarly journals Performance Analysis of Classifiers in Identifying NREM Sleep and Awaken Stages from EEG Signals

2020 ◽  
Vol 8 (6) ◽  
pp. 4321-4326
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Linear Regression ◽  
Expectation Maximization ◽  
Principal Component ◽  
Nrem Sleep ◽  
Component Analysis ◽  
Medical Procedure ◽  
Eeg Signals ◽  
Non Linear

Electroencephalogram is a medical procedure which helps in analyzing the activities of the brain through electrical signals. In this paper a simple classification technique of EEG signal into two stages as NREM sleep and awaken stages had been undertaken. Classifying these stages helps the physician to understand the patient's sleep disorder by knowing whether the person's brain is in NREM sleep or awaken stages. Physionet EEG signals are samples of 256 signals per second for 10 seconds duration is used in this work. Then the EEG samples properties are analyzed through various parameters like statistical features, entropy Pearson correlation coefficient, Power spectral density, scatter plots and Hilbert transform plots. The classification of NREM sleep and awaken stage is performed by the ten different classifiers broadly grouped into non linear and hybrid one. The classifiers used include Linear Regression, Non Linear Regression, Logistic Regression, Principal Component Analysis, Kernel Principal Component Analysis, Expectation Maximization, Compensatory Expectation Maximization, Expectation Maximization with Logistic Regression Compensatory Expectation Maximization with Logistic Regression, and Firefly. The performances of the classifiers are analyzed using regular parameters like sensitivity, accuracy, specificity, performance index. The highest accuracy of 95.575% is achieved with linear regression for awaken signal and an accuracy of 95.315% is achieved using kernel PCA for sleep signal.

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