scholarly journals Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators

2020 ◽  
pp. 1-22 ◽  
Author(s):  
Alfredo Cartone ◽  
Paolo Postiglione
2019 ◽  
Vol 34 (6) ◽  
pp. 908-909
Author(s):  
K Hakinson ◽  
J Moses ◽  
J RIvera ◽  
A Guerra ◽  
M Davis ◽  
...  

Abstract Objective Examine the relationship of verbal mediation with visual memory errors and intelligence to understand the role of spoken language on other assessment measures. Method Assessment records were obtained from a Veteran Affairs clinic for veterans (n=100) with diverse neuropsychiatric conditions who completed the Wechsler Adult Intelligence Scale, third edition (WAIS-III), Multilingual Aphasia Examination (MAE), and Benton Visual Retention Test (BVRT). A Principal Component Analysis (PCA) was used to examine the interrelationship among these assessments. The components of spoken language, types of errors on the BVRT, and the four factors of the WAIS-III were factored using the PCA to identify common sources of variance. Results A principal component analysis revealed a six-factor model explaining 68.16% of the shared variance among the WAIS-III factors, MAE components, and BVRT Errors. Omission errors loaded with Processing Speed and Controlled Word Association. Distortions and size errors loaded with Perceptual Organization. Size errors also loaded with Verbal Comprehension and Visual Naming. Misplacements loaded with Working Memory and Sentence Repetition. Misplacements, perseverations, and omissions loaded with the Token Test (a measure associated with auditory comprehension). Rotation errors loaded with Perceptual Organization. Conclusions Results indicated significant shared variance between visual memory errors, spoken language, and intelligence factors. This suggests that spoken language is involved in the process of visual memory, and deficits in spoken language may result in increased errors on visual memory tasks. Therefore, treatment recommendations for visual memory difficulties should take into consideration verbal capabilities and intelligence factors to better individualize treatment.


2010 ◽  
Vol 3 (5) ◽  
Author(s):  
Mario Bettenbühl ◽  
Claudia Paladini ◽  
Konstantin Mergenthaler ◽  
Reinhold Kliegl ◽  
Ralf Engbert ◽  
...  

During visual fixation on a target, humans perform miniature (or fixational) eye movements consisting of three components, i.e., tremor, drift, and microsaccades. Microsaccades are high velocity components with small amplitudes within fixational eye movements. However, microsaccade shapes and statistical properties vary between individual observers. Here we show that microsaccades can be formally represented with two significant shapes which we identfied using the mathematical definition of singularities for the detection of the former in real data with the continuous wavelet transform. For character-ization and model selection, we carried out a principal component analysis, which identified a step shape with an overshoot as first and a bump which regulates the overshoot as second component. We conclude that microsaccades are singular events with an overshoot component which can be detected by the continuous wavelet transform.


Author(s):  
Christian Mormont ◽  
Patrick Fontan

Abstract. According to the theory of identification, men are more likely to qualify their Rorschach human content responses as males, and women as females. These assumptions were tested in an empirical investigation using a Belgian nonpatient sample of 800. All human responses and their location were listed. Analyses were carried out on the 10 Cards and on the formal quality (FQo vs. FQu/−) of all human responses according to the subject’s and the examiner’s sex. Variables were first submitted to principal component analysis, and resulting components were compared in a 2 × 2 design in order to assess examiners’ and participants’ sex potential effects on human responses sex assignments. Univariate and multivariate ANOVA revealed no or only negligible differences. In a second step, distributions of masculine, feminine, and neutral human responses across 16 card locations that commonly elicit human responses were submitted to hierarchical clustering in order to identify masculine, feminine, and neutral locations in Rorschach cards. Chi-square tests revealed no significant association between participants’ sex and human responses locations. Results do not corroborate predictions according to the theory of identification but they do, however, highlight the role of the distal features of blots.


2010 ◽  
pp. 171-193
Author(s):  
Sean Eom

This chapter describes the factor procedure. The first section of the chapter begins with the definition of factor analysis. This is the statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables (factor). ACA uses principal component analysis to group authors into several catagories with similar lines of research. We also present many different approaches of preparing datasets including manual data inputs, in-file statement, and permanent datasets. We discuss each of the key SAS statements including DATA, INPUT, CARDS, PROC, and RUN. In addition, we examine several options statements to specify the followings: method for extracting factors; number of factors, rotation method, and displaying output options.


2006 ◽  
Vol 06 (01) ◽  
pp. L17-L28 ◽  
Author(s):  
JOSÉ MANUEL LÓPEZ-ALONSO ◽  
JAVIER ALDA

Principal Component Analysis (PCA) has been applied to the characterization of the 1/f-noise. The application of the PCA to the 1/f noise requires the definition of a stochastic multidimensional variable. The components of this variable describe the temporal evolution of the phenomena sampled at regular time intervals. In this paper we analyze the conditions about the number of observations and the dimension of the multidimensional random variable necessary to use the PCA method in a sound manner. We have tested the obtained conditions for simulated and experimental data sets obtained from imaging optical systems. The results can be extended to other fields where this kind of noise is relevant.


1998 ◽  
Vol 25 (6) ◽  
pp. 1050-1058 ◽  
Author(s):  
T O Siew-Yan-Yu ◽  
J Rousselle ◽  
G Jacques ◽  
V.-T.-V. Nguyen

A definition of homogeneous regions in terms of precipitation regime is achieved by the use of principal component analysis (PCA). The method has been shown to be a reliable regionalization tool even though it was applied to a territory showing rather complex physiography and high precipitation variation. Results based on the application of the PCA to the interstation correlation matrix of precipitation have indicated four distinct homogeneous regions. These regional patterns can be explained by the orographic effect and by the circulation of air masses within the study region.Key words: homogeneous regions, rainfall, principal component analysis, orographic effect.


Author(s):  
Matt Olfat ◽  
Anil Aswani

Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.


2006 ◽  
Vol 38 (2) ◽  
pp. 299-319 ◽  
Author(s):  
Stephan Huckemann ◽  
Herbert Ziezold

Classical principal component analysis on manifolds, for example on Kendall's shape spaces, is carried out in the tangent space of a Euclidean mean equipped with a Euclidean metric. We propose a method of principal component analysis for Riemannian manifolds based on geodesics of the intrinsic metric, and provide a numerical implementation in the case of spheres. This method allows us, for example, to compare principal component geodesics of different data samples. In order to determine principal component geodesics, we show that in general, owing to curvature, the principal component geodesics do not pass through the intrinsic mean. As a consequence, means other than the intrinsic mean are considered, allowing for several choices of definition of geodesic variance. In conclusion we apply our method to the space of planar triangular shapes and compare our findings with those of standard Euclidean principal component analysis.


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