Using Horn's Parallel Analysis Method in Exploratory
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Choosing Principal Components: a New Graphical Method Based on Bayesian Model Selection
Choosing principal components: a new graphical method based on Bayesian model selection Philipp Auer Daniel Gervini1 University of Ulm University of Wisconsin–Milwaukee October 31, 2007 1Corresponding author. Email: [email protected]. Address: P.O. Box 413, Milwaukee, WI 53201, USA. Abstract This article approaches the problem of selecting significant principal components from a Bayesian model selection perspective. The resulting Bayes rule provides a simple graphical technique that can be used instead of (or together with) the popular scree plot to determine the number of significant components to retain. We study the theoretical properties of the new method and show, by examples and simulation, that it provides more clear-cut answers than the scree plot in many interesting situations. Key Words: Dimension reduction; scree plot; factor analysis; singular value decomposition. MSC: Primary 62H25; secondary 62-09. 1 Introduction Multivariate datasets usually contain redundant information, due to high correla- tions among the variables. Sometimes they also contain irrelevant information; that is, sources of variability that can be considered random noise for practical purposes. To obtain uncorrelated, significant variables, several methods have been proposed over the years. Undoubtedly, the most popular is Principal Component Analysis (PCA), introduced by Hotelling (1933); for a comprehensive account of the methodology and its applications, see Jolliffe (2002). In many practical situations the first few components accumulate most of the variability, so it is common practice to discard the remaining components, thus re- ducing the dimensionality of the data. This has many advantages both from an inferential and from a descriptive point of view, since the leading components are often interpretable indices within the context of the problem and they are statis- tically more stable than subsequent components, which tend to be more unstruc- tured and harder to estimate. -
Combining Diacritical Marks Range: 0300–036F the Unicode Standard
Combining Diacritical Marks Range: 0300–036F The Unicode Standard, Version 4.0 This file contains an excerpt from the character code tables and list of character names for The Unicode Standard, Version 4.0. Characters in this chart that are new for The Unicode Standard, Version 4.0 are shown in conjunction with any existing characters. For ease of reference, the new characters have been highlighted in the chart grid and in the names list. This file will not be updated with errata, or when additional characters are assigned to the Unicode Standard. See http://www.unicode.org/charts for access to a complete list of the latest character charts. Disclaimer These charts are provided as the on-line reference to the character contents of the Unicode Standard, Version 4.0 but do not provide all the information needed to fully support individual scripts using the Unicode Standard. For a complete understanding of the use of the characters contained in this excerpt file, please consult the appropriate sections of The Unicode Standard, Version 4.0 (ISBN 0-321-18578-1), as well as Unicode Standard Annexes #9, #11, #14, #15, #24 and #29, the other Unicode Technical Reports and the Unicode Character Database, which are available on-line. See http://www.unicode.org/Public/UNIDATA/UCD.html and http://www.unicode.org/unicode/reports A thorough understanding of the information contained in these additional sources is required for a successful implementation. Fonts The shapes of the reference glyphs used in these code charts are not prescriptive. Considerable variation is to be expected in actual fonts. -
Staar Grade 4 Writing Tb Released 2018
STAAR® State of Texas Assessments of Academic Readiness GRADE 4 Writing Administered April 2018 RELEASED Copyright © 2018, Texas Education Agency. All rights reserved. Reproduction of all or portions of this work is prohibited without express written permission from the Texas Education Agency. WRITING Writing Page 3 Writing Page 4 WRITTEN COMPOSITION Writing Page 5 WRITTEN COMPOSITION: Expository READ the following quotation. I do not know of anyone who has gotten to the top without hard work. —Margaret Thatcher THINK about all the hard work you do. It may be work you do at school, at home, or outside. WRITE about one type of hard work you do. Tell about your work and explain why it is so hard to do. Be sure to — • clearly state your central idea • organize your writing • develop your writing in detail • choose your words carefully • use correct spelling, capitalization, punctuation, grammar, and sentences Writing Page 6 USE THIS PREWRITING PAGE TO PLAN YOUR COMPOSITION. MAKE SURE THAT YOU WRITE YOUR COMPOSITION ON THE LINED PAGE IN THE ANSWER DOCUMENT. Writing Page 7 USE THIS PREWRITING PAGE TO PLAN YOUR COMPOSITION. MAKE SURE THAT YOU WRITE YOUR COMPOSITION ON THE LINED PAGE IN THE ANSWER DOCUMENT. Writing Page 8 REVISING AND EDITING Writing Page 9 Read the selection and choose the best answer to each question. Then fill in the answer on your answer document. Maggie wrote this paper in response to a class assignment. Read the paper and think about any revisions Maggie should make. When you finish reading, answer the questions that follow. © Christian Musat/Fotolia © Christian Musat/Fotolia The Rhino’s Horn (1) The rhinoceros is a huge mammal that is native to Africa and Asia. -
Principal Component Analysis (PCA) As a Statistical Tool for Identifying Key Indicators of Nuclear Power Plant Cable Insulation
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2017 Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation Chamila Chandima De Silva Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Materials Science and Engineering Commons, Mechanics of Materials Commons, and the Statistics and Probability Commons Recommended Citation De Silva, Chamila Chandima, "Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation" (2017). Graduate Theses and Dissertations. 16120. https://lib.dr.iastate.edu/etd/16120 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation by Chamila C. De Silva A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Materials Science and Engineering Program of Study Committee: Nicola Bowler, Major Professor Richard LeSar Steve Martin The student author and the program of study committee are solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred. -
Exam-Srm-Sample-Questions.Pdf
SOCIETY OF ACTUARIES EXAM SRM - STATISTICS FOR RISK MODELING EXAM SRM SAMPLE QUESTIONS AND SOLUTIONS These questions and solutions are representative of the types of questions that might be asked of candidates sitting for Exam SRM. These questions are intended to represent the depth of understanding required of candidates. The distribution of questions by topic is not intended to represent the distribution of questions on future exams. April 2019 update: Question 2, answer III was changed. While the statement was true, it was not directly supported by the required readings. July 2019 update: Questions 29-32 were added. September 2019 update: Questions 33-44 were added. December 2019 update: Question 40 item I was modified. January 2020 update: Question 41 item I was modified. November 2020 update: Questions 6 and 14 were modified and Questions 45-48 were added. February 2021 update: Questions 49-53 were added. July 2021 update: Questions 54-55 were added. Copyright 2021 by the Society of Actuaries SRM-02-18 1 QUESTIONS 1. You are given the following four pairs of observations: x1=−==−=( 1,0), xx 23 (1,1), (2, 1), and x 4(5,10). A hierarchical clustering algorithm is used with complete linkage and Euclidean distance. Calculate the intercluster dissimilarity between {,xx12 } and {}x4 . (A) 2.2 (B) 3.2 (C) 9.9 (D) 10.8 (E) 11.7 SRM-02-18 2 2. Determine which of the following statements is/are true. I. The number of clusters must be pre-specified for both K-means and hierarchical clustering. II. The K-means clustering algorithm is less sensitive to the presence of outliers than the hierarchical clustering algorithm. -
Dynamic Factor Models Catherine Doz, Peter Fuleky
Dynamic Factor Models Catherine Doz, Peter Fuleky To cite this version: Catherine Doz, Peter Fuleky. Dynamic Factor Models. 2019. halshs-02262202 HAL Id: halshs-02262202 https://halshs.archives-ouvertes.fr/halshs-02262202 Preprint submitted on 2 Aug 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. WORKING PAPER N° 2019 – 45 Dynamic Factor Models Catherine Doz Peter Fuleky JEL Codes: C32, C38, C53, C55 Keywords : dynamic factor models; big data; two-step estimation; time domain; frequency domain; structural breaks DYNAMIC FACTOR MODELS ∗ Catherine Doz1,2 and Peter Fuleky3 1Paris School of Economics 2Université Paris 1 Panthéon-Sorbonne 3University of Hawaii at Manoa July 2019 ∗Prepared for Macroeconomic Forecasting in the Era of Big Data Theory and Practice (Peter Fuleky ed), Springer. 1 Chapter 2 Dynamic Factor Models Catherine Doz and Peter Fuleky Abstract Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework. -
Principal Components Analysis Maths and Statistics Help Centre
Principal Components Analysis Maths and Statistics Help Centre Usage - Data reduction tool - Vital first step in the multivariate analysis of continuous data - Used to find patterns in high dimensional data - Expresses data such that similarities and differences are highlighted - Once the patterns in the data are found, PCA is used to reduce the dimensionality of the data without much loss of information (choosing the right number of principal components is very important) - If you are using PCA for modelling purposes (either subsequent gradient analyses or regression) then normality is ideal. If it is for data reduction or exploratory purposes, then normality is not a strict requirement How does PCA work? - Uses the covariance/correlations of the raw data to define principal components that combine many correlated variables - The principal components are uncorrelated - Similar concept to regression – in regression you use one line to describe a relationship between two variables; here the principal component represents the line. Given a point on the line, or a value of the principal component you can discover the values of the variables. Choosing the right number of principal components - This is one of the most important parts of PCA. The number you choose needs to be the ones that give you the most information without significant loss of information. - Scree plots show the eigenvalues. These are used to tell us how important the principal components are. - When the scree plot plateaus then no more principal components are needed. - The loadings are a measure of how much each original variable contributes to each of the principal components. -
Diacritics-ELL.Pdf
Diacritics J.C. Wells, University College London Dkadvkxkdw avf ekwxkrhykwjkrh qavow axxadjfe xs pfxxfvw sg xjf aptjacfx, gsv f|aqtpf xjf adyxf addfrx sr xjf ‘ kr dag‘. M swx parhyahf svxjshvatjkfw cawfe sr xjf Laxkr aptjacfx qaof wsqf ywf sg ekadvkxkdw, aw kreffe es xjswf cawfe sr sxjfv aptjacfxw are {vkxkrh w}wxfqw. Tjf gsdyw sg xjkw avxkdpf kw sr xjf vspf sg ekadvkxkdw kr xjf svxjshvatj} sg parhyahfw {vkxxfr {kxj xjf Laxkr aptjacfx. Ireffe, xjf svkhkr sg wsqf pfxxfvw xjax avf rs{ a wxareave tavx sg xjf aptjacfx pkfw kr xjf ywf sg ekadvkxkdw. Tjf pfxxfv G {aw krzfrxfe kr Rsqar xkqfw aw a zavkarx sg C, ekwxkrhykwjfe c} xjf dvswwcav sr xjf ytwxvsof. Tjf pfxxfv J {aw rsx ekwxkrhykwjfe gvsq I, rsv U gvsq V, yrxkp xjf 16xj dfrxyv} (Saqtwsr 1985: 110). Tjf rf{ pfxxfv 1 kw sczksywp} a zavkarx sr r are ws dsype cf wffr aw krdsvtsvaxkrh a ekadvkxkd xakp. Dkadvkxkdw tvstfv, xjsyhj, avf wffr aw qavow axxadjfe xs a cawf pfxxfv. Ir xjkw wfrwf, m y 1 es rsx krzspzf ekadvkxkdw. Tjf f|xfrwkzf ywf sg ekadvkxkdw xs wyttpfqfrx xjf Laxkr aptjacfx kr dawfw {jfvf kx {aw wffr aw kraefuyaxf gsv xjf wsyrew sg sxjfv parhyahfw kw hfrfvapp} axxvkcyxfe xs xjf vfpkhksyw vfgsvqfv Jar Hyw (1369-1415), {js efzkwfe a vfgsvqfe svxjshvatj} gsv C~fdj krdsvtsvaxkrh 9addfrxfe: pfxxfvw wydj aw ˛ ¹ = > ?. M swx ekadvkxkdw avf tpadfe acszf xjf cawf pfxxfv {kxj {jkdj xjf} avf awwsdkaxfe. A gf{, js{fzfv, avf tpadfe cfps{ kx (aw “) sv xjvsyhj kx (aw B). 1 Laxkr pfxxfvw dsqf kr ps{fv-dawf are yttfv-dawf zfvwksrw. -
Unicode Alphabets for L ATEX
Unicode Alphabets for LATEX Specimen Mikkel Eide Eriksen March 11, 2020 2 Contents MUFI 5 SIL 21 TITUS 29 UNZ 117 3 4 CONTENTS MUFI Using the font PalemonasMUFI(0) from http://mufi.info/. Code MUFI Point Glyph Entity Name Unicode Name E262 � OEligogon LATIN CAPITAL LIGATURE OE WITH OGONEK E268 � Pdblac LATIN CAPITAL LETTER P WITH DOUBLE ACUTE E34E � Vvertline LATIN CAPITAL LETTER V WITH VERTICAL LINE ABOVE E662 � oeligogon LATIN SMALL LIGATURE OE WITH OGONEK E668 � pdblac LATIN SMALL LETTER P WITH DOUBLE ACUTE E74F � vvertline LATIN SMALL LETTER V WITH VERTICAL LINE ABOVE E8A1 � idblstrok LATIN SMALL LETTER I WITH TWO STROKES E8A2 � jdblstrok LATIN SMALL LETTER J WITH TWO STROKES E8A3 � autem LATIN ABBREVIATION SIGN AUTEM E8BB � vslashura LATIN SMALL LETTER V WITH SHORT SLASH ABOVE RIGHT E8BC � vslashuradbl LATIN SMALL LETTER V WITH TWO SHORT SLASHES ABOVE RIGHT E8C1 � thornrarmlig LATIN SMALL LETTER THORN LIGATED WITH ARM OF LATIN SMALL LETTER R E8C2 � Hrarmlig LATIN CAPITAL LETTER H LIGATED WITH ARM OF LATIN SMALL LETTER R E8C3 � hrarmlig LATIN SMALL LETTER H LIGATED WITH ARM OF LATIN SMALL LETTER R E8C5 � krarmlig LATIN SMALL LETTER K LIGATED WITH ARM OF LATIN SMALL LETTER R E8C6 UU UUlig LATIN CAPITAL LIGATURE UU E8C7 uu uulig LATIN SMALL LIGATURE UU E8C8 UE UElig LATIN CAPITAL LIGATURE UE E8C9 ue uelig LATIN SMALL LIGATURE UE E8CE � xslashlradbl LATIN SMALL LETTER X WITH TWO SHORT SLASHES BELOW RIGHT E8D1 æ̊ aeligring LATIN SMALL LETTER AE WITH RING ABOVE E8D3 ǽ̨ aeligogonacute LATIN SMALL LETTER AE WITH OGONEK AND ACUTE 5 6 CONTENTS -
Optimal Principal Component Analysis of STEM XEDS Spectrum Images Pavel Potapov1,2* and Axel Lubk2
Potapov and Lubk Adv Struct Chem Imag (2019) 5:4 https://doi.org/10.1186/s40679-019-0066-0 RESEARCH Open Access Optimal principal component analysis of STEM XEDS spectrum images Pavel Potapov1,2* and Axel Lubk2 Abstract STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workfow step by step on an example of a complex semiconductor structure con- sisting of a number of diferent phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the frst time and described in details. Keywords: PCA, Spectrum image, Reconstruction, Denoising, STEM, XEDS, EDS, EDX Background components [3–9]. In general terms, PCA reduces the Scanning transmission electron microscopy (STEM) dimensionality of a large dataset by projecting it into an delivers images of nanostructures at high spatial resolu- orthogonal basic of lower dimension. It can be shown tion matching that of broad beam transmission electron that among all possible linear projections, PCA ensures microscopy (TEM). Additionally, modern STEM instru- the smallest Euclidean diference between the initial and ments are typically equipped with electron energy-loss projected datasets or, in other words, provides the mini- spectrometers (EELS) and/or X-rays energy-dispersive mal least squares errors when approximating data with a spectroscopy (XEDS, sometimes abbreviated as EDS smaller number of variables [10]. -
An Eigenvector Variability Plot
Statistica Sinica 19 (2009), 1741-1754 AN EIGENVECTOR VARIABILITY PLOT I-Ping Tu, Hung Chen and Xin Chen Academia Sinica, Taiwan, National Taiwan University and UC San Francisco Abstract: Principal components analysis is perhaps the most widely used method for exploring multivariate data. In this paper, we propose a variability plot composed of measures on the stability of each eigenvector over samples as a data exploration tool. We also show that this variability measure gives a good measure on the intersample variability of eigenvectors through asymptotic analysis. For distinct eigenvalues, the asymptotic behavior of this variability measure is comparable to the size of the asymptotic covariance of the eigenvector in Anderson (1963). Applying this method to a gene expression data set for a gastric cancer study, many hills on the proposed variability plot are observed. We are able to show that each hill groups a set of multiple eigenvalues. When the intersample variability of eigenvectors is considered, the cuto® point on informative eigenvectors should not be on the top of the hill as suggested by the proposed variability plot. We also try the proposed method on functional data analysis through a simulated data set with dimension p greater than sample size n. The proposed variability plot is successful at distinguishing the signal components, noise components and zero eigenvalue components. Key words and phrases: Dimension reduction, eigenvector, functional data analysis, principal components analysis, resampling, the multiplicity of eigenvalues, the scree plot. 1. Introduction During the last two decades, analyzing huge data sets has become common to scientists in many ¯elds. Thus one sees microarray data in gene expression experiments, 3-D microscopy reconstruction of macromolecules, and MRI image analysis in brain neural network studies, for example. -
Principal Components Analysis (Pca)
PRINCIPAL COMPONENTS ANALYSIS (PCA) Steven M. Holand Department of Geology, University of Georgia, Athens, GA 30602-2501 3 December 2019 Introduction Suppose we had measured two variables, length and width, and plotted them as shown below. Both variables have approximately the same variance and they are highly correlated with one another. We could pass a vector through the long axis of the cloud of points and a second vec- tor at right angles to the first, with both vectors passing through the centroid of the data. Once we have made these vectors, we could find the coordinates of all of the data points rela- tive to these two perpendicular vectors and re-plot the data, as shown here (both of these figures are from Swan and Sandilands, 1995). In this new reference frame, note that variance is greater along axis 1 than it is on axis 2. Also note that the spatial relationships of the points are unchanged; this process has merely rotat- ed the data. Finally, note that our new vectors, or axes, are uncorrelated. By performing such a rotation, the new axes might have particular explanations. In this case, axis 1 could be regard- ed as a size measure, with samples on the left having both small length and width and samples on the right having large length and width. Axis 2 could be regarded as a measure of shape, with samples at any axis 1 position (that is, of a given size) having different length to width ratios. PC axes will generally not coincide exactly with any of the original variables.