Construction of an index by maximization of the sum of its absolute correlation coefficients with the constituent variables
|
|
- Shanna Palmer
- 5 years ago
- Views:
Transcription
1 Construction of an index by axiization of the su of its absolute correlation coefficients with the constituent variables SK Mishra Departent of Econoics North-Eastern Hill University Shillong (India) I. Introduction: On any occasions we need to construct an index that represents a nuber of variables (indicators). Cost of living index, general price index, huan developent index, index of level of developent, etc are soe of the exaples that are constructed by a weighted (linear) aggregation of a host of variables. The general forula of construction of such an index (OECD, 003) ay be given as I = w x w x + w x w x ; i = 1,,..., n i i 1 i1 i i = 1 where w is the weight assigned to the observations of x = ( x1, x,..., xn ). The weights, w ( w1, w,..., w ) the variables x ; 1,,..., th variable, x and reains constant over all =, are deterined by the iportance assigned to =. The criterion on which iportance of a variable (vis-à-vis other variables) is deterined ay be varied and usually has its own logic (Munda and Nardo, 005). For exaple, in constructing a cost of living index iportance of a coodity is deterined by the proportion of consuption expenditure allocated to that particular coodity and in constructing the huan developent index variables such as literacy, life expectancy or incoe are weighted according to the iportance assigned to the in accordance with their perceived roles in deterining huan developent status. In any cases, however, the analyst does not have any preferred eans or logic to deterine the relative iportance of different variables. In such cases, weights are assigned atheatically. One of the ethods to deterine such atheatical weights is the Principal Coponents analysis (McCracken, 000). In the Principal Coponents analysis (Kendall & Stuart, 1968, pp ) weights are deterined such that the su of the squared correlation coefficients of the index with the constituent variables (used to construct the index) is axiized. In other words, weights in I = w x are deterined such that r ( I, x ) is axiized. Here = 1 r( I, x ) is the coefficient of correlation between the index I and the variable x. The Principal Coponents analysis is a very well established statistical ethod that has excellent atheatical properties. Fro x = ( x1, x,..., x ) one ay obtain (or fewer) indices that are orthogonal with each other. These indices together explain cent percent variation in the original variables x = ( x1, x,..., x ). Moreover, the first Principal Coponent (often used to ake a single index) explains the largest proportion of variation in the variables x = ( x1, x,..., x ).
2 II. Soe Practical Probles with the Principal Coponents Analysis: Although the Principal Coponents analysis has excellent atheatical properties, one ay face soe difficulties in using it if one desires to construct a single index of the variables that are not very highly correlated aong theselves. The ethod has a tendency to pick up the subset of highly correlated variables to ake the first coponent, assign arginal weights to relatively poorly correlated subset of variables and/or relegate the latter subset to construction of the subsequent principal coponents. Now if one has to construct a single index, such an index underines the poorly correlated set of variables. As a result, practically speaking, the index so constructed is the weighted aggregation of only the preferred (highly correlated) set of variables. In this sense, the index so constructed is elitist in nature that has a preference to the highly correlated subset over the poorly correlated subset of variables. Further, since there is no dependable ethod available to obtain a coposite index by erging two or ore principal coponents, the deferred set of variables never finds its representation in the further analysis. III. A Wider View of Constructing an Index: possibilities of axiizing = 1 r( I, x ) L 1 / L Let us now investigate into the to obtain weights to construct I = wx. This is only a Minkowsky generalization of axiization of r ( I, x ) or (equivalently) 1 / r( I, x ). It can be shown that as ( ) = 1 = 1 L, the index becoes ore and ore egalitarian with an ever-stronger tendency to assign weights such that all or ost of the variables are equally correlated with the index. In so doing, it axiizes the inial correlation of the index with its constituent variables or in other words it gives us the axiin index. However, for L = 1 the index is inclusive in nature that assigns reasonable (although saller) weights to the ebers of less correlated subset of variables, but has no tendency to underine the less correlated variables and their representation. This property of the index obtained by axiizing r( I, x ) or axiizing the inial correlation is attractive and useful. The obective of this paper is to illustrate this fact. IV. An Experient: We have conducted (liited) experients on constructing indices by axiizing (a) su of squared correlation, which is the standard Principal Coponents analysis, (b) axiin correlation, and (c) axiizing the su of absolute correlations. For sake of identification, we would call the I-, I-M and I-1 respectively. The experients have been conducted for (i) highly correlated variables and (ii) poorly correlated variables. V. The Method of Optiization: The ethod of constructing indices by the Principal Coponents is available in any software packages such as STATISTICA or SPSS. However, the ethod to construct indices by axiin correlation or axiization of the su of absolute correlation is not available. We have obtained all indices (I-, I-M and I-1) by solving ax = 1 r( I, x ) L 1 / L such that I = 1 = wx where w are the decision variables. It
3 is an intricate non-linear optiization proble. Any powerful non-linear prograing software ay possibly be used for optiization (see Kuester and Mize, 1973 for classical ethods and FORTRAN progras). However, we have used the Differential Evolution (DE) ethod of Global Optiization (which is in the broader faily of the Genetic algoriths). The optiization ay also be done by the Particle Swar ethod often used in Artificial Intelligence (see Mishra, 006). We have found that the Repulsive Particle Swar (RPS) ethod perfors as effectively as the Differential Evolution ethod. We have not presented the results of the RPS optiization to avoid duplication of results. The FORTRAN codes of DE or RPS ay be obtained fro the author on request. VI. Findings: The results of our experients are presented in tables 1 through -c. It is evident fro the correlation atrices associated with tables 1 through -c that in case of highly correlated variables [Table-1(i)], all the three ethods have a tendency to yield indices that represent all the constituent variables. However, when the variables are poorly correlated [Tables -a(i) onwards], the principal coponent index (I-) has a tendency to underine soe variables by poorly correlating with the (and thus not representing the, or relegating the to be represented by the subsequent principal coponents). On the contrary, I-M and I-1 assign reasonable weights to those variables and thus includes the. Nevertheless, it ay be noted that I-1 and I-M pay the cost in ters of the explained variance [su of squared r(i, x )] in the constituent variables. VII. Concluding Rearks: In this exercise we have shown that the principal coponent indices are elitist and they have a tendency to underine the iportance of poorly correlated variables. On the other hand, I-1 is ore inclusive, and has a tendency to represent even the poorly correlated variables. The I-M indices are egalitarian in nature. It would depend on the analyst whether he is interested in egalitarian, inclusive or elitist ethod of constructing indices when the constituent variables are not very highly correlated aong theselves. This paper has opened up the option to choose the ethod of constructing a desired type of index. References Kendall, MG and Stuart, A (1968): The Advanced Theory of Statistics, Charles Griffin & Co. London, vol. 3. Kuester, J.L. and Mize, J.H. (1973): Optiization Techniques with Fortran, McGraw-Hill Book Co. New York. McCracken, K (000): Soe Coents on the Seifa96 Indexes, Paper presented in the 10 th Biennial Conference of the Australian Population Association, Melbourne, Australia. Mishra, SK (006): Global Optiization by Differential Evolution and Particle Swar Methods: Evaluation on Soe Benchark Functions. SSRN Munda, G. and Nardo, M (005) : Constructing Consistent Coposite Indicators: The Issue of Weights, EUR 1834 EN, Institute for the Protection and Security of the citizen, European Coission, Luxebourg. OECD (003) Coposite Indicators of Country Perforance: A Critical Assessent, DST/IND(003)5, Paris. 3
4 Table-1(i): Construction of Indices with Highly Correlated Variables x 1 x x 3 x 4 x 5 I- I-M I Coefficient of correlation of x with the Index SAR SSR Index I I-M I-1 SAR=Su of absolute correlation coefficients; SSR=Su of squared correlation coefficients Table-1(ii): Correlation aong Variables and Indices [Ref. Table-1(i)] 4
5 Table--a(i): Construction of Indices with Poorly Correlated Variables x 1 x x 3 x 4 x 5 I- I-M I Coefficient of correlation of x with the Index SAR SSR Index I I-M I-1 SAR=Su of absolute correlation coefficients; SSR=Su of squared correlation coefficients Table--a(ii): Correlation aong Variables and Indices [Ref. Table--a(i)] 5
6 Table--b(i): Construction of Indices with Poorly Correlated Variables x 1 x x 3 x 4 x 5 I- I-M I Coefficient of correlation of x with the Index SAR SSR Index I I-M I-1 SAR=Su of absolute correlation coefficients; SSR=Su of squared correlation coefficients Table--b(ii): Correlation aong Variables and Indices [Ref. Table--b(i)] 6
7 Table--c(i): Construction of Indices with Poorly Correlated Variables x 1 x x 3 x 4 x 5 I- I-M I Coefficient of correlation of x with the Index SAR SSR Index I I-M I-1 SAR=Su of absolute correlation coefficients; SSR=Su of squared correlation coefficients Table--c(ii): Correlation aong Variables and Indices [Ref. Table--c(i)] 7
Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon
Model Fitting CURM Background Material, Fall 014 Dr. Doreen De Leon 1 Introduction Given a set of data points, we often want to fit a selected odel or type to the data (e.g., we suspect an exponential
More informationIntelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines
Intelligent Systes: Reasoning and Recognition Jaes L. Crowley osig 1 Winter Seester 2018 Lesson 6 27 February 2018 Outline Perceptrons and Support Vector achines Notation...2 Linear odels...3 Lines, Planes
More informationOptimum Value of Poverty Measure Using Inverse Optimization Programming Problem
International Journal of Conteporary Matheatical Sciences Vol. 14, 2019, no. 1, 31-42 HIKARI Ltd, www.-hikari.co https://doi.org/10.12988/ijcs.2019.914 Optiu Value of Poverty Measure Using Inverse Optiization
More informationSoft Computing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis
Soft Coputing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis Beverly Rivera 1,2, Irbis Gallegos 1, and Vladik Kreinovich 2 1 Regional Cyber and Energy Security Center RCES
More informationOptimal Pigouvian Taxation when Externalities Affect Demand
Optial Pigouvian Taxation when Externalities Affect Deand Enda Patrick Hargaden Departent of Econoics University of Michigan enda@uich.edu Version of August 2, 2015 Abstract Purchasing a network good such
More informationEnsemble Based on Data Envelopment Analysis
Enseble Based on Data Envelopent Analysis So Young Sohn & Hong Choi Departent of Coputer Science & Industrial Systes Engineering, Yonsei University, Seoul, Korea Tel) 82-2-223-404, Fax) 82-2- 364-7807
More informationFeature Extraction Techniques
Feature Extraction Techniques Unsupervised Learning II Feature Extraction Unsupervised ethods can also be used to find features which can be useful for categorization. There are unsupervised ethods that
More informationDetermining OWA Operator Weights by Mean Absolute Deviation Minimization
Deterining OWA Operator Weights by Mean Absolute Deviation Miniization Micha l Majdan 1,2 and W lodziierz Ogryczak 1 1 Institute of Control and Coputation Engineering, Warsaw University of Technology,
More informationExperimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis
City University of New York (CUNY) CUNY Acadeic Works International Conference on Hydroinforatics 8-1-2014 Experiental Design For Model Discriination And Precise Paraeter Estiation In WDS Analysis Giovanna
More informationIntroduction to Machine Learning. Recitation 11
Introduction to Machine Learning Lecturer: Regev Schweiger Recitation Fall Seester Scribe: Regev Schweiger. Kernel Ridge Regression We now take on the task of kernel-izing ridge regression. Let x,...,
More informationChapter 6: Economic Inequality
Chapter 6: Econoic Inequality We are interested in inequality ainly for two reasons: First, there are philosophical and ethical grounds for aversion to inequality per se. Second, even if we are not interested
More informationCombining Classifiers
Cobining Classifiers Generic ethods of generating and cobining ultiple classifiers Bagging Boosting References: Duda, Hart & Stork, pg 475-480. Hastie, Tibsharini, Friedan, pg 246-256 and Chapter 10. http://www.boosting.org/
More informationA Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words)
1 A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine (1900 words) Contact: Jerry Farlow Dept of Matheatics Univeristy of Maine Orono, ME 04469 Tel (07) 866-3540 Eail: farlow@ath.uaine.edu
More informationSupport Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization
Recent Researches in Coputer Science Support Vector Machine Classification of Uncertain and Ibalanced data using Robust Optiization RAGHAV PAT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering
More informationKernel Methods and Support Vector Machines
Intelligent Systes: Reasoning and Recognition Jaes L. Crowley ENSIAG 2 / osig 1 Second Seester 2012/2013 Lesson 20 2 ay 2013 Kernel ethods and Support Vector achines Contents Kernel Functions...2 Quadratic
More information1 Bounding the Margin
COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #12 Scribe: Jian Min Si March 14, 2013 1 Bounding the Margin We are continuing the proof of a bound on the generalization error of AdaBoost
More informationDepartment of Electronic and Optical Engineering, Ordnance Engineering College, Shijiazhuang, , China
6th International Conference on Machinery, Materials, Environent, Biotechnology and Coputer (MMEBC 06) Solving Multi-Sensor Multi-Target Assignent Proble Based on Copositive Cobat Efficiency and QPSO Algorith
More informationCOS 424: Interacting with Data. Written Exercises
COS 424: Interacting with Data Hoework #4 Spring 2007 Regression Due: Wednesday, April 18 Written Exercises See the course website for iportant inforation about collaboration and late policies, as well
More informationThe dynamic game theory methods applied to ship control with minimum risk of collision
Risk Analysis V: Siulation and Hazard Mitigation 293 The dynaic gae theory ethods applied to ship control with iu risk of collision J. Lisowski Departent of Ship Autoation, Gdynia Maritie University, Poland
More informationA note on the multiplication of sparse matrices
Cent. Eur. J. Cop. Sci. 41) 2014 1-11 DOI: 10.2478/s13537-014-0201-x Central European Journal of Coputer Science A note on the ultiplication of sparse atrices Research Article Keivan Borna 12, Sohrab Aboozarkhani
More informationHomework 3 Solutions CSE 101 Summer 2017
Hoework 3 Solutions CSE 0 Suer 207. Scheduling algoriths The following n = 2 jobs with given processing ties have to be scheduled on = 3 parallel and identical processors with the objective of iniizing
More informationMathematical Model and Algorithm for the Task Allocation Problem of Robots in the Smart Warehouse
Aerican Journal of Operations Research, 205, 5, 493-502 Published Online Noveber 205 in SciRes. http://www.scirp.org/journal/ajor http://dx.doi.org/0.4236/ajor.205.56038 Matheatical Model and Algorith
More informationWhen Short Runs Beat Long Runs
When Short Runs Beat Long Runs Sean Luke George Mason University http://www.cs.gu.edu/ sean/ Abstract What will yield the best results: doing one run n generations long or doing runs n/ generations long
More informationDeflation of the I-O Series Some Technical Aspects. Giorgio Rampa University of Genoa April 2007
Deflation of the I-O Series 1959-2. Soe Technical Aspects Giorgio Rapa University of Genoa g.rapa@unige.it April 27 1. Introduction The nuber of sectors is 42 for the period 1965-2 and 38 for the initial
More informationE0 370 Statistical Learning Theory Lecture 6 (Aug 30, 2011) Margin Analysis
E0 370 tatistical Learning Theory Lecture 6 (Aug 30, 20) Margin Analysis Lecturer: hivani Agarwal cribe: Narasihan R Introduction In the last few lectures we have seen how to obtain high confidence bounds
More informationOPTIMIZATION OF SPECIFIC FACTORS TO PRODUCE SPECIAL ALLOYS
5 th International Conference Coputational Mechanics and Virtual Engineering COMEC 2013 24-25 October 2013, Braşov, Roania OPTIMIZATION OF SPECIFIC FACTORS TO PRODUCE SPECIAL ALLOYS I. Milosan 1 1 Transilvania
More informationA Model for the Selection of Internet Service Providers
ISSN 0146-4116, Autoatic Control and Coputer Sciences, 2008, Vol. 42, No. 5, pp. 249 254. Allerton Press, Inc., 2008. Original Russian Text I.M. Aliev, 2008, published in Avtoatika i Vychislitel naya Tekhnika,
More informationInteractive Markov Models of Evolutionary Algorithms
Cleveland State University EngagedScholarship@CSU Electrical Engineering & Coputer Science Faculty Publications Electrical Engineering & Coputer Science Departent 2015 Interactive Markov Models of Evolutionary
More informationCurious Bounds for Floor Function Sums
1 47 6 11 Journal of Integer Sequences, Vol. 1 (018), Article 18.1.8 Curious Bounds for Floor Function Sus Thotsaporn Thanatipanonda and Elaine Wong 1 Science Division Mahidol University International
More informationAlgorithms for parallel processor scheduling with distinct due windows and unit-time jobs
BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES Vol. 57, No. 3, 2009 Algoriths for parallel processor scheduling with distinct due windows and unit-tie obs A. JANIAK 1, W.A. JANIAK 2, and
More informationNon-Parametric Non-Line-of-Sight Identification 1
Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,
More informationThis model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t.
CS 493: Algoriths for Massive Data Sets Feb 2, 2002 Local Models, Bloo Filter Scribe: Qin Lv Local Models In global odels, every inverted file entry is copressed with the sae odel. This work wells when
More informationRevealed Preference with Stochastic Demand Correspondence
Revealed Preference with Stochastic Deand Correspondence Indraneel Dasgupta School of Econoics, University of Nottingha, Nottingha NG7 2RD, UK. E-ail: indraneel.dasgupta@nottingha.ac.uk Prasanta K. Pattanaik
More informationMeasures of average are called measures of central tendency and include the mean, median, mode, and midrange.
CHAPTER 3 Data Description Objectives Suarize data using easures of central tendency, such as the ean, edian, ode, and idrange. Describe data using the easures of variation, such as the range, variance,
More informationPrincipal Components Analysis
Principal Coponents Analysis Cheng Li, Bingyu Wang Noveber 3, 204 What s PCA Principal coponent analysis (PCA) is a statistical procedure that uses an orthogonal transforation to convert a set of observations
More informationNow multiply the left-hand-side by ω and the right-hand side by dδ/dt (recall ω= dδ/dt) to get:
Equal Area Criterion.0 Developent of equal area criterion As in previous notes, all powers are in per-unit. I want to show you the equal area criterion a little differently than the book does it. Let s
More informationKeywords: Estimator, Bias, Mean-squared error, normality, generalized Pareto distribution
Testing approxiate norality of an estiator using the estiated MSE and bias with an application to the shape paraeter of the generalized Pareto distribution J. Martin van Zyl Abstract In this work the norality
More informationQualitative Modelling of Time Series Using Self-Organizing Maps: Application to Animal Science
Proceedings of the 6th WSEAS International Conference on Applied Coputer Science, Tenerife, Canary Islands, Spain, Deceber 16-18, 2006 183 Qualitative Modelling of Tie Series Using Self-Organizing Maps:
More informationN-Point. DFTs of Two Length-N Real Sequences
Coputation of the DFT of In ost practical applications, sequences of interest are real In such cases, the syetry properties of the DFT given in Table 5. can be exploited to ake the DFT coputations ore
More informationDistributed Subgradient Methods for Multi-agent Optimization
1 Distributed Subgradient Methods for Multi-agent Optiization Angelia Nedić and Asuan Ozdaglar October 29, 2007 Abstract We study a distributed coputation odel for optiizing a su of convex objective functions
More informationChapter 1: Basics of Vibrations for Simple Mechanical Systems
Chapter 1: Basics of Vibrations for Siple Mechanical Systes Introduction: The fundaentals of Sound and Vibrations are part of the broader field of echanics, with strong connections to classical echanics,
More informationIntroduction to Discrete Optimization
Prof. Friedrich Eisenbrand Martin Nieeier Due Date: March 9 9 Discussions: March 9 Introduction to Discrete Optiization Spring 9 s Exercise Consider a school district with I neighborhoods J schools and
More informationPattern Recognition and Machine Learning. Artificial Neural networks
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2017 Lessons 7 20 Dec 2017 Outline Artificial Neural networks Notation...2 Introduction...3 Key Equations... 3 Artificial
More informationINTELLECTUAL DATA ANALYSIS IN AIRCRAFT DESIGN
INTELLECTUAL DATA ANALYSIS IN AIRCRAFT DESIGN V.A. Koarov 1, S.A. Piyavskiy 2 1 Saara National Research University, Saara, Russia 2 Saara State Architectural University, Saara, Russia Abstract. This article
More informationSupport Vector Machines MIT Course Notes Cynthia Rudin
Support Vector Machines MIT 5.097 Course Notes Cynthia Rudin Credit: Ng, Hastie, Tibshirani, Friedan Thanks: Şeyda Ertekin Let s start with soe intuition about argins. The argin of an exaple x i = distance
More informationSupport Vector Machines. Machine Learning Series Jerry Jeychandra Blohm Lab
Support Vector Machines Machine Learning Series Jerry Jeychandra Bloh Lab Outline Main goal: To understand how support vector achines (SVMs) perfor optial classification for labelled data sets, also a
More informationma x = -bv x + F rod.
Notes on Dynaical Systes Dynaics is the study of change. The priary ingredients of a dynaical syste are its state and its rule of change (also soeties called the dynaic). Dynaical systes can be continuous
More informationR. L. Ollerton University of Western Sydney, Penrith Campus DC1797, Australia
FURTHER PROPERTIES OF GENERALIZED BINOMIAL COEFFICIENT k-extensions R. L. Ollerton University of Western Sydney, Penrith Capus DC1797, Australia A. G. Shannon KvB Institute of Technology, North Sydney
More informationTHE CAPACIATED TRANSPORTATION PROBLEM IN LINEAR FRACTIONAL FUNCTIONALS PROGRAMMING
J. Operations Research Soc. of Japan VoJ. 10, Nos. I & 2 October 1967 1967 The Operations Research Society of Japan THE CAPACIATED TRANSPORTATION PROBLEM IN LINEAR FRACTIONAL FUNCTIONALS PROGRAMMING SURESH
More informationApproximation in Stochastic Scheduling: The Power of LP-Based Priority Policies
Approxiation in Stochastic Scheduling: The Power of -Based Priority Policies Rolf Möhring, Andreas Schulz, Marc Uetz Setting (A P p stoch, r E( w and (B P p stoch E( w We will assue that the processing
More informationExplicit solution of the polynomial least-squares approximation problem on Chebyshev extrema nodes
Explicit solution of the polynoial least-squares approxiation proble on Chebyshev extrea nodes Alfredo Eisinberg, Giuseppe Fedele Dipartiento di Elettronica Inforatica e Sisteistica, Università degli Studi
More informationEXPLICIT CONGRUENCES FOR EULER POLYNOMIALS
EXPLICIT CONGRUENCES FOR EULER POLYNOMIALS Zhi-Wei Sun Departent of Matheatics, Nanjing University Nanjing 10093, People s Republic of China zwsun@nju.edu.cn Abstract In this paper we establish soe explicit
More informationA Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair
Proceedings of the 6th SEAS International Conference on Siulation, Modelling and Optiization, Lisbon, Portugal, Septeber -4, 006 0 A Siplified Analytical Approach for Efficiency Evaluation of the eaving
More informationIs Walras s Theory So Different From Marshall s?
Is Walras s Theory So Different Fro Marshall s? Ezra Davar (Independent Researcher) Anon VeTaar 4/1, Netanya 40, Israel E-ail: ezra.davar@gail.co Received: July 17, 014 Accepted: August 5, 014 Published:
More informationResearch in Area of Longevity of Sylphon Scraies
IOP Conference Series: Earth and Environental Science PAPER OPEN ACCESS Research in Area of Longevity of Sylphon Scraies To cite this article: Natalia Y Golovina and Svetlana Y Krivosheeva 2018 IOP Conf.
More informationEfficient Filter Banks And Interpolators
Efficient Filter Banks And Interpolators A. G. DEMPSTER AND N. P. MURPHY Departent of Electronic Systes University of Westinster 115 New Cavendish St, London W1M 8JS United Kingdo Abstract: - Graphical
More informationMálaga Economic Theory Research Center Working Papers
Málaga Econoic Theory Research Center Working Papers Unequivocal Majority and Maskin-Monotonicity Pablo Aorós WP 2008-3 March 2008 Departaento de Teoría e Historia Econóica Facultad de Ciencias Econóicas
More informationTopic 5a Introduction to Curve Fitting & Linear Regression
/7/08 Course Instructor Dr. Rayond C. Rup Oice: A 337 Phone: (95) 747 6958 E ail: rcrup@utep.edu opic 5a Introduction to Curve Fitting & Linear Regression EE 4386/530 Coputational ethods in EE Outline
More informationFault Tree Modeling for Redundant Multi-Functional Digital Systems
International Journal of Perforability Engineering, Vol. 3, No. 3, July, 2007, pp. 329-336 RAMS Consultants Printed in India Fault Tree Modeling for Redundant Multi-Functional Digital Systes HYUN GOOK
More informationRevealed Preference and Stochastic Demand Correspondence: A Unified Theory
Revealed Preference and Stochastic Deand Correspondence: A Unified Theory Indraneel Dasgupta School of Econoics, University of Nottingha, Nottingha NG7 2RD, UK. E-ail: indraneel.dasgupta@nottingha.ac.uk
More informationMSEC MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL SOLUTION FOR MAINTENANCE AND PERFORMANCE
Proceeding of the ASME 9 International Manufacturing Science and Engineering Conference MSEC9 October 4-7, 9, West Lafayette, Indiana, USA MSEC9-8466 MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL
More informationList Scheduling and LPT Oliver Braun (09/05/2017)
List Scheduling and LPT Oliver Braun (09/05/207) We investigate the classical scheduling proble P ax where a set of n independent jobs has to be processed on 2 parallel and identical processors (achines)
More informationA Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness
A Note on Scheduling Tall/Sall Multiprocessor Tasks with Unit Processing Tie to Miniize Maxiu Tardiness Philippe Baptiste and Baruch Schieber IBM T.J. Watson Research Center P.O. Box 218, Yorktown Heights,
More informationlecture 36: Linear Multistep Mehods: Zero Stability
95 lecture 36: Linear Multistep Mehods: Zero Stability 5.6 Linear ultistep ethods: zero stability Does consistency iply convergence for linear ultistep ethods? This is always the case for one-step ethods,
More informationUnsupervised Learning: Dimension Reduction
Unsupervised Learning: Diension Reduction by Prof. Seungchul Lee isystes Design Lab http://isystes.unist.ac.kr/ UNIST Table of Contents I.. Principal Coponent Analysis (PCA) II. 2. PCA Algorith I. 2..
More informationOn Rough Interval Three Level Large Scale Quadratic Integer Programming Problem
J. Stat. Appl. Pro. 6, No. 2, 305-318 2017) 305 Journal of Statistics Applications & Probability An International Journal http://dx.doi.org/10.18576/jsap/060206 On Rough Interval Three evel arge Scale
More informationMULTIAGENT Resource Allocation (MARA) is the
EDIC RESEARCH PROPOSAL 1 Designing Negotiation Protocols for Utility Maxiization in Multiagent Resource Allocation Tri Kurniawan Wijaya LSIR, I&C, EPFL Abstract Resource allocation is one of the ain concerns
More informationUse of PSO in Parameter Estimation of Robot Dynamics; Part One: No Need for Parameterization
Use of PSO in Paraeter Estiation of Robot Dynaics; Part One: No Need for Paraeterization Hossein Jahandideh, Mehrzad Navar Abstract Offline procedures for estiating paraeters of robot dynaics are practically
More information3.3 Variational Characterization of Singular Values
3.3. Variational Characterization of Singular Values 61 3.3 Variational Characterization of Singular Values Since the singular values are square roots of the eigenvalues of the Heritian atrices A A and
More informationA LOSS FUNCTION APPROACH TO GROUP PREFERENCE AGGREGATION IN THE AHP
ISAHP 003, Bali, Indonesia, August 7-9, 003 A OSS FUNCTION APPROACH TO GROUP PREFERENCE AGGREGATION IN THE AHP Keun-Tae Cho and Yong-Gon Cho School of Systes Engineering Manageent, Sungkyunkwan University
More informationOBJECTIVES INTRODUCTION
M7 Chapter 3 Section 1 OBJECTIVES Suarize data using easures of central tendency, such as the ean, edian, ode, and idrange. Describe data using the easures of variation, such as the range, variance, and
More informationA proposal for a First-Citation-Speed-Index Link Peer-reviewed author version
A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version Made available by Hasselt University Library in Docuent Server@UHasselt Reference (Published version): EGGHE, Leo; Bornann,
More informationSupport Vector Machines. Goals for the lecture
Support Vector Machines Mark Craven and David Page Coputer Sciences 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Soe of the slides in these lectures have been adapted/borrowed fro aterials developed
More informationSequence Analysis, WS 14/15, D. Huson & R. Neher (this part by D. Huson) February 5,
Sequence Analysis, WS 14/15, D. Huson & R. Neher (this part by D. Huson) February 5, 2015 31 11 Motif Finding Sources for this section: Rouchka, 1997, A Brief Overview of Gibbs Sapling. J. Buhler, M. Topa:
More informationAP Physics C: Mechanics 2007 Scoring Guidelines
AP Physics C: Mechanics 007 Scoring Guidelines The College Board: Connecting Students to College Success The College Board is a not-for-profit ebership association whose ission is to connect students to
More informationEquilibria on the Day-Ahead Electricity Market
Equilibria on the Day-Ahead Electricity Market Margarida Carvalho INESC Porto, Portugal Faculdade de Ciências, Universidade do Porto, Portugal argarida.carvalho@dcc.fc.up.pt João Pedro Pedroso INESC Porto,
More informationThe Methods of Solution for Constrained Nonlinear Programming
Research Inventy: International Journal Of Engineering And Science Vol.4, Issue 3(March 2014), PP 01-06 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.co The Methods of Solution for Constrained
More informationIntroduction to Optimization Techniques. Nonlinear Programming
Introduction to Optiization echniques Nonlinear Prograing Optial Solutions Consider the optiization proble in f ( x) where F R n xf Definition : x F is optial (global iniu) for this proble, if f( x ) f(
More informationPage 1 Lab 1 Elementary Matrix and Linear Algebra Spring 2011
Page Lab Eleentary Matri and Linear Algebra Spring 0 Nae Due /03/0 Score /5 Probles through 4 are each worth 4 points.. Go to the Linear Algebra oolkit site ransforing a atri to reduced row echelon for
More informationChapter 6 1-D Continuous Groups
Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:
More informationCourse Notes for EE227C (Spring 2018): Convex Optimization and Approximation
Course Notes for EE227C (Spring 2018): Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee227c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee227c@berkeley.edu October
More informationPattern Recognition and Machine Learning. Learning and Evaluation for Pattern Recognition
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2017 Lesson 1 4 October 2017 Outline Learning and Evaluation for Pattern Recognition Notation...2 1. The Pattern Recognition
More informationWarning System of Dangerous Chemical Gas in Factory Based on Wireless Sensor Network
565 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 59, 07 Guest Editors: Zhuo Yang, Junie Ba, Jing Pan Copyright 07, AIDIC Servizi S.r.l. ISBN 978-88-95608-49-5; ISSN 83-96 The Italian Association
More informationConstructing Locally Best Invariant Tests of the Linear Regression Model Using the Density Function of a Maximal Invariant
Aerican Journal of Matheatics and Statistics 03, 3(): 45-5 DOI: 0.593/j.ajs.03030.07 Constructing Locally Best Invariant Tests of the Linear Regression Model Using the Density Function of a Maxial Invariant
More informationThe accelerated expansion of the universe is explained by quantum field theory.
The accelerated expansion of the universe is explained by quantu field theory. Abstract. Forulas describing interactions, in fact, use the liiting speed of inforation transfer, and not the speed of light.
More informationCSE525: Randomized Algorithms and Probabilistic Analysis May 16, Lecture 13
CSE55: Randoied Algoriths and obabilistic Analysis May 6, Lecture Lecturer: Anna Karlin Scribe: Noah Siegel, Jonathan Shi Rando walks and Markov chains This lecture discusses Markov chains, which capture
More informationSymbolic Analysis as Universal Tool for Deriving Properties of Non-linear Algorithms Case study of EM Algorithm
Acta Polytechnica Hungarica Vol., No., 04 Sybolic Analysis as Universal Tool for Deriving Properties of Non-linear Algoriths Case study of EM Algorith Vladiir Mladenović, Miroslav Lutovac, Dana Porrat
More informationInfinitely Many Trees Have Non-Sperner Subtree Poset
Order (2007 24:133 138 DOI 10.1007/s11083-007-9064-2 Infinitely Many Trees Have Non-Sperner Subtree Poset Andrew Vince Hua Wang Received: 3 April 2007 / Accepted: 25 August 2007 / Published online: 2 October
More informationBoosting with log-loss
Boosting with log-loss Marco Cusuano-Towner Septeber 2, 202 The proble Suppose we have data exaples {x i, y i ) i =... } for a two-class proble with y i {, }. Let F x) be the predictor function with the
More informationPattern Classification using Simplified Neural Networks with Pruning Algorithm
Pattern Classification using Siplified Neural Networks with Pruning Algorith S. M. Karuzzaan 1 Ahed Ryadh Hasan 2 Abstract: In recent years, any neural network odels have been proposed for pattern classification,
More information3.8 Three Types of Convergence
3.8 Three Types of Convergence 3.8 Three Types of Convergence 93 Suppose that we are given a sequence functions {f k } k N on a set X and another function f on X. What does it ean for f k to converge to
More informationThe Algorithms Optimization of Artificial Neural Network Based on Particle Swarm
Send Orders for Reprints to reprints@benthascience.ae The Open Cybernetics & Systeics Journal, 04, 8, 59-54 59 Open Access The Algoriths Optiization of Artificial Neural Network Based on Particle Swar
More informatione-companion ONLY AVAILABLE IN ELECTRONIC FORM
OPERATIONS RESEARCH doi 10.1287/opre.1070.0427ec pp. ec1 ec5 e-copanion ONLY AVAILABLE IN ELECTRONIC FORM infors 07 INFORMS Electronic Copanion A Learning Approach for Interactive Marketing to a Custoer
More informationHandout 7. and Pr [M(x) = χ L (x) M(x) =? ] = 1.
Notes on Coplexity Theory Last updated: October, 2005 Jonathan Katz Handout 7 1 More on Randoized Coplexity Classes Reinder: so far we have seen RP,coRP, and BPP. We introduce two ore tie-bounded randoized
More informationŞtefan ŞTEFĂNESCU * is the minimum global value for the function h (x)
7Applying Nelder Mead s Optiization Algorith APPLYING NELDER MEAD S OPTIMIZATION ALGORITHM FOR MULTIPLE GLOBAL MINIMA Abstract Ştefan ŞTEFĂNESCU * The iterative deterinistic optiization ethod could not
More informationChaotic Coupled Map Lattices
Chaotic Coupled Map Lattices Author: Dustin Keys Advisors: Dr. Robert Indik, Dr. Kevin Lin 1 Introduction When a syste of chaotic aps is coupled in a way that allows the to share inforation about each
More informationHierarchical central place system and agglomeration economies on households
Hierarchical central place syste and aggloeration econoies on households Daisuke Nakaura, Departent of International Liberal Arts, Fukuoka Woen s University Executive suary Central place theory shows that
More informationGenetic Quantum Algorithm and its Application to Combinatorial Optimization Problem
Genetic Quantu Algorith and its Application to Cobinatorial Optiization Proble Kuk-Hyun Han Dept. of Electrical Engineering, KAIST, 373-, Kusong-dong Yusong-gu Taejon, 305-70, Republic of Korea khhan@vivaldi.kaist.ac.kr
More informationInclusions Between the Spaces of Strongly Almost Convergent Sequences Defined by An Orlicz Function in A Seminormed Space
Inclusions Between the Spaces of Strongly Alost Convergent Seuences Defined by An Orlicz Function in A Seinored Space Vinod K. Bhardwaj and Indu Bala Abstract The concept of strong alost convergence was
More informationApplying Genetic Algorithms to Solve the Fuzzy Optimal Profit Problem
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 8, 563-58 () Applying Genetic Algoriths to Solve the Fuzzy Optial Profit Proble FENG-TSE LIN AND JING-SHING YAO Departent of Applied Matheatics Chinese Culture
More information