Unfolding Models for Asymmetric Dissimilarity Data With External Information Based on Path Structures

2018 ◽  
Vol 6 (3) ◽  
pp. 53-66
Author(s):  
Kensuke Tanioka ◽  
Hiroshi Yadohisa
Keyword(s):  
Principal Component ◽  
External Information ◽  
Visualization Method ◽  
Low Dimensions ◽  
Dissimilarity Data ◽  
Asymmetric Relation ◽  
Loading Matrix ◽  
Unfolding Models ◽  
Asymmetric Multidimensional Scaling ◽  
Path Structure

This article contains asymmetric dissimilarity data which is observed in various situations. In asymmetric dissimilarity data, dissimilarity from subject i to j and from subject j to i are not the same necessarily. Asymmetric multidimensional scaling (AMDS) is a visualization method for describing the asymmetric relations between subjects, given asymmetric dissimilarity data for subjects. It is sure that AMDS is a useful tool for interpreting the asymmetric relation, however, existing AMDS cannot be considered for the external information, even if the external information of the same subjects for the asymmetric dissimilarity data is given. If the estimated coordinates can be interpreted from the loading matrix for the external information like principal component analysis (PCA), the AMDS become more useful. This is because we can interpret the relation between the estimated asymmetries and the factors of the external information on the low dimensions. In this article, we proposed new AMDS with external information. In addition to that, the proposed method can consider the path structure for variables like SEM.

scholarly journals Geração de Cenários de Estresse para Curva de Juros

2011 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Alan De Genaro Dario ◽  
Mariela Fernández
Keyword(s):  
Principal Component Analysis ◽  
Interest Rate ◽  
Term Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Market Data ◽  
Low Dimensions ◽  
Hjm Model ◽  
The Interest Rate

This article describes the use of the Heath-Jarrow-Morton framework to generate stress scenarios for the term structure of the interest rate. By means of principal component analysis it is possible to reduce the dimensions of the problem and create a bridge between the information a specialist possesses for defining scenarios, such information generally being of low dimensions, and the robustness of the HJM model. The methodology is applied to Brazilian Market data during the market meltdown in 2008 and from other occasions.

scholarly journals Dark energy survey year 1 results: Constraining baryonic physics in the Universe

2021 ◽  
Vol 502 (4) ◽  
pp. 6010-6031
Author(s):  
Hung-Jin Huang ◽  
Tim Eifler ◽  
Rachel Mandelbaum ◽  
Gary M Bernstein ◽  
Anqi Chen ◽  
...  
Keyword(s):  
Dark Energy ◽  
Large Scale ◽  
Principal Component ◽  
External Information ◽  
Small Scale ◽  
Acoustic Oscillations ◽  
Dark Energy Survey ◽  
Theoretical Predictions ◽  
Hydrodynamical Simulations ◽  
Energy Survey

ABSTRACT Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3 × 2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modelling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude (Q1) generally reflects the strength of baryon feedback. With the upper limit of Q1 prior being bound by the Illustris feedback scenarios, we reach $\sim 20{{\ \rm per\ cent}}$ improvement in the constraint of $S_8=\sigma _8(\Omega _{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3 × 2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative Q1 prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than 2σ.

scholarly journals Construction of Health Infrastructure Index in Haryana: An Econometric Approach

1970 ◽  
Vol 9 (1) ◽  
pp. 103-123 ◽  
Author(s):  
M. M. Goel ◽  
Ishu Garg
Keyword(s):  
Composite Index ◽  
Principal Component ◽  
The State ◽  
Annual Growth Rate ◽  
Compound Annual Growth Rate ◽  
Health Infrastructure ◽  
Bartlett’S Test ◽  
Loading Matrix ◽  
Component Loading ◽  
Econometric Approach

The provision of health infrastructure is one of the major areas of concern in Indian economy including Haryana. Health infrastructure which comprises all the resources necessary to provide health services, is proved to be essential to create health human capital. Thus, being a merit good, establishment of health infrastructure is the prime duty of the State. With this backdrop, the present study is attempted to construct health infrastructure index for the State of Haryana. On the basis of the available data taken from various issues of Statistical Abstract of Haryana, fourteen indicators of health infrastructure are considered for the period of twenty one years from 1991-92 to 201112. First of all, the collected data is analyzed by computing descriptive statistics which reveal that seven indicators of health infrastructure possess positive compound annual growth rate (CAGR) while seven others have negative CAGR and certain indicators have experienced high variations in their number over the years. Next, normalization of data is done and then by applying principal component analysis (PCA), composite index for health infrastructure is constructed in various steps including correlation matrix, KMO measure and Bartlett’s test; eigenvalues of components; component loading matrix; calculation of weights for variables (indicators of health infrastructure) and finally health infrastructure index. As per index scores, ranks are given to the State for its health infrastructure for all twenty one years. It is found that health infrastructure in Haryana for the year 2004-05 have attained rank 1st with index score 1.000, followed by 2011-12 with score 0.837 and the year 2003-04 having the value of 0.764. Between 1991-92 and 2011-12, up and downs in index scores as well as in ranks are seen. Besides, score of health infrastructure index remain up to 0.5 for eleven years while above 0.5 for ten years. However, the year 200910 can be considered quite embarrassing for which score of health infrastructure index is zero indicating availability of health infrastructure was at lowest level in this year. Fortunately, the year 2011-12 having 2nd rank in health infrastructure index arises a ray of hope for the further promotion in the availability of health infrastructural facilities in coming years in State of Haryana. However, negative growth rates of certain indicators and low scores of health infrastructure index calls for immediate attention of Government with sufficient investments towards health infrastructure in Haryana.

Using Principle Component Analysis and Clustering Algorithm to Identify Power Factors and Transformation Pattern of Regional Construction

2016 ◽  
Vol 13 (10) ◽  
pp. 6585-6605
Author(s):  
Jingxiao Zhang ◽  
Haiyan Xie ◽  
Klaus Schmidt ◽  
Hui Li
Keyword(s):  
Information Integration ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Shaanxi Province ◽  
External Information ◽  
Construction Enterprises ◽  
Brand Building ◽  
Implementation Level ◽  
Transformational Development

The purpose of this research is to analyze the power factors that drive regional development of construction enterprises in order to identify the potential problems within their corresponding transformational pattern. The authors used principal component analysis, and exhaustive clustering experiments and K-means’ ANOVA to verify the feasibility and operability of a regional transformational path. With the data collected from 123 enterprises in the Shaanxi province, China, the results showed that there were 4 power factors: (a) implementation level of transformational development, (b) external information integration, (c) project knowledge management, and (d) corporate brand building support. The overall power factor (Fs) was 6.987, with the σ value of 1.78, min value of 2.895, and max value of 10.843. This research for the first time quantified the influence of the power factors to different clusters of companies and their levels of transformational development. This research discussed their corresponding strengths, weaknesses and provided practical tactics for them to establish suitable development strategies. This research could guide the regional efforts of the industry in reaching higher levels of transformation evolvement, and the emerging framework targets change-based momentum and dynamic clustering elements to the transformational development of regional construction.

Digital Processing of STEM Images

1981 ◽  
Vol 39 ◽  
pp. 236-237
Author(s):  
A. V. Crewe ◽  
M. Ohtsuki
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Assembly Language ◽  
Processing System ◽  
Principal Component ◽  
Digital Processing ◽  
Image Analysis System ◽  
Black And White ◽  
Analysis System ◽  
Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

Quantitative Analysis Of X-Ray Images From Geological Materials

1990 ◽  
Vol 48 (2) ◽  
pp. 244-245
Author(s):  
J. M. Paque ◽  
R. Browning ◽  
P. L. King ◽  
P. Pianetta
Keyword(s):  
Quantitative Analysis ◽  
Bulk Composition ◽  
Principal Component ◽  
Analytical Description ◽  
Bulk Sample ◽  
Geological Samples ◽  
X Ray ◽  
Software And Hardware ◽  
And Storage ◽  
Insight Into

Geological samples typically contain many minerals (phases) with multiple element compositions. A complete analytical description should give the number of phases present, the volume occupied by each phase in the bulk sample, the average and range of composition of each phase, and the bulk composition of the sample. A practical approach to providing such a complete description is from quantitative analysis of multi-elemental x-ray images.With the advances in recent years in the speed and storage capabilities of laboratory computers, large quantities of data can be efficiently manipulated. Commercial software and hardware presently available allow simultaneous collection of multiple x-ray images from a sample (up to 16 for the Kevex Delta system). Thus, high resolution x-ray images of the majority of the detectable elements in a sample can be collected. The use of statistical techniques, including principal component analysis (PCA), can provide insight into mineral phase composition and the distribution of minerals within a sample.

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