DOCSLIB.ORG

PERPUSTAKAAN UTM 2015 Categories: STATISTICS & PROBABILITY Quartiles: Q2 by Wos

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
PERPUSTAKAAN UTM 2015 Categories: STATISTICS & PROBABILITY Quartiles: Q2 by Wos PERPUSTAKAAN UTM 2015 Categories: STATISTICS & PROBABILITY Quartiles: Q2 by WoS Journal Impact Factor 5-Year JCR Abbreviated Rank Full Journal Title ISSN Total Cites Impact without Journal Impact Title Factor Self Cites Factor 1 ANNALS OF PROBABILITY ANN PROBAB 0091-1798 4,226 1.42 1.326 1.701 2 BIOMETRIKA BIOMETRIKA 0006-3444 15,774 1.418 1.359 2.281 SCANDINAVIAN ACTUARIAL 3 SCAND ACTUAR J 0346-1238 370 1.412 1.265 1.212 JOURNAL COMPUTATIONAL COMPUT STAT DATA UTM 4 STATISTICS & DATA 0167-9473 6,012 1.4 1.07 1.51 AN ANALYSIS REVSTAT-STATISTICAL 4 REVSTAT-STAT J 1645-6726 92 1.4 1.333 0.794 JOURNAL OXFORD BULLETIN OF OXFORD B ECON 6 0305-9049 1,986 1.368 1.356 1.885 ECONOMICS AND STATISTICS STAT 7 BAYESIAN ANALYSIS BAYESIAN ANAL 1931-6690 841 1.343 1.239 2.443 SORT-STATISTICS AND SORT-STAT OPER RES 8 OPERATIONS RESEARCH 1696-2281 85 1.333 1.292 1.133 T TRANSACTIONS 8 EXTREMES EXTREMES 1386-1999 457 1.333 1.156 1.5 JOURNAL OF J COMPUT GRAPH 2015 10 COMPUTATIONAL AND 1061-8600 2,699 1.222 1.121 1.812 STAT GRAPHICAL STATISTICS INTERNATIONAL 11 INT STAT REV 0306-7734 1,131 1.2 1.15 1.28 STATISTICAL REVIEW 12 ECONOMETRIC REVIEWS ECONOMET REV 0747-4938 730 1.189 1.17 1.116 13 BERNOULLIPERPUSTAKAANBERNOULLI 1350-7265 1,327 1.161 1.083 1.296 14 STATISTICA SINICA STAT SINICA 1017-0405 2,269 1.158 1.099 1.591 JOURNAL OF QUALITY 15 J QUAL TECHNOL 0022-4065 2,046 1.152 0.848 2.096 TECHNOLOGY INSURANCE MATHEMATICS 16 INSUR MATH ECON 0167-6687 2,141 1.128 0.646 1.582 & ECONOMICS STATISTICAL APPLICATIONS STAT APPL GENET 17 IN GENETICS AND 2194-6302 1,415 1.127 1.098 1.537 MOL MOLECULAR BIOLOGY Journal Impact Factor 5-Year JCR Abbreviated Rank Full Journal Title ISSN Total Cites Impact without Journal Impact Title Factor Self Cites Factor ANNALES DE L INSTITUT HENRI POINCARE- ANN I H POINCARE- 18 0246-0203 904 1.059 1 1.253 PROBABILITES ET PR STATISTIQUES STOCHASTIC PROCESSES 19 STOCH PROC APPL 0304-4149 3,810 1.056 0.897 1.388 AND THEIR APPLICATIONS 20 R JOURNAL R J 2073-4859 166 1.038 1 1.455 ADVANCES IN DATA ADV DATA ANAL 21 ANALYSIS AND 1862-5347 143 1.026 0.923 1.348 CLASSI CLASSIFICATION 22 STATA JOURNAL STATA J 1536-867X 2,195 1 0.813 3.168 23 TEST TEST-SPAIN 1133-0686 517 UTM0.984 0.871 1.356 24 ECONOMETRIC THEORY ECONOMET THEOR 0266-4666 1,777 0.978 0.899 1.437 25 STATISTICAL MODELLING STAT MODEL 1471-082X 399 0.977 0.953 1.155 ELECTRONIC JOURNAL OF 26 ELECTRON J STAT 1935-7524 684 0.957 0.919 1.325 STATISTICS 27 BIOMETRICAL JOURNAL BIOMETRICAL J 0323-3847 1,690 0.945 0.807 1.433 JOURNAL OF MULTIVARIATE J MULTIVARIATE 28 0047-259X 3,117 0.934 0.803 1.153 ANALYSIS ANAL ENVIRONMENTAL AND 29 ENVIRON ECOL STAT 1352-8505 672 0.925 0.91 1.319 ECOLOGICAL STATISTICS 30 AMERICAN STATISTICIAN AM STAT 0003-130520153,713 0.915 0.712 1.335 METHODOLOGY AND METHODOL COMPUT 31 COMPUTING IN APPLIED 1387-5841 350 0.913 0.837 0.857 APPL PROBABILITY *PERPUSTAKAAN UTM 2015 PERPUSTAKAAN .
Load more

Recommended publications
  • Brendan K. Beare
    Brendan K. Beare Department of Economics University of California – San Diego 9500 Gilman Drive #0508 La Jolla, California 92093, U.S.A. Email: [email protected] : http://econweb.ucsd.edu/ bbeare/ ∼ Born: January 23, 1980 Citizenship: Australia & United States Current position 2015– Associate Professor, University of California – San Diego Prior appointments held 2008–2015 Assistant Professor, University of California – San Diego 2007–2008 Research Fellow, Nuffield College and University of Oxford Education 2007 PD in Economics, Yale University 2006 MA in Statistics, Yale University 2005 MP in Economics, Yale University 2004 MA in Economics, Yale University 2002 BE(H) in Econometrics, University of New South Wales Honors & awards 2011–2016 Sir Clive W. J. Granger Chair, University of California – San Diego 2008 George Trimis Prize for Distinguished Dissertation in Economics, Yale University 2007 MA by Resolution, University of Oxford 2007 Dissertation Fellowship, Yale University 2006 Carl Arvid Anderson Prize, Cowles Foundation for Research in Economics 2006 Cowles Summer Prize, Cowles Foundation for Research in Economics 2002–2006 Cowles Prize, Cowles Foundation for Research in Economics 2002–2006 University Fellowship, Yale University 2002 Economic Society of Australia Honours Prize 1 Publications 2019 Beare, Brendan K. and Shi, Xiaoxia. An improved bootstrap test of density ratio ordering. Econometrics and Statistics, 10: 9-26. 2019 Seo, Won-Ki and Beare, Brendan K. Cointegrated linear processes in Bayes Hilbert space. Statistics and Probability Letters, 147: 90-95. 2018 Beare, Brendan K. Unit root testing with unstable volatility. Journal of Time Series Analysis, 39(6): 816-835. 2018 Beare, Brendan K. and Dossani, Asad. Option augmented density forecasts of market returns with monotone pricing kernel.
    [Show full text]
  • Econometric Theory
    Econometric Theory John Stachurski January 10, 2014 Contents Preface v I Background Material1 1 Probability2 1.1 Probability Models.............................2 1.2 Distributions................................. 16 1.3 Dependence................................. 25 1.4 Asymptotics................................. 30 1.5 Exercises................................... 39 2 Linear Algebra 49 2.1 Vectors and Matrices............................ 49 2.2 Span, Dimension and Independence................... 59 2.3 Matrices and Equations........................... 66 2.4 Random Vectors and Matrices....................... 71 2.5 Convergence of Random Matrices.................... 74 2.6 Exercises................................... 79 i CONTENTS ii 3 Projections 84 3.1 Orthogonality and Projection....................... 84 3.2 Overdetermined Systems of Equations.................. 90 3.3 Conditioning................................. 93 3.4 Exercises................................... 103 II Foundations of Statistics 107 4 Statistical Learning 108 4.1 Inductive Learning............................. 108 4.2 Statistics................................... 112 4.3 Maximum Likelihood............................ 120 4.4 Parametric vs Nonparametric Estimation................ 125 4.5 Empirical Distributions........................... 134 4.6 Empirical Risk Minimization....................... 137 4.7 Exercises................................... 149 5 Methods of Inference 153 5.1 Making Inference about Theory...................... 153 5.2 Confidence Sets..............................
    [Show full text]
  • Miguel De Carvalho
    School of Mathematics Miguel de Carvalho Contact M. de Carvalho T: +44 (0) 0131 650 5054 Information The University of Edinburgh B: [email protected] School of Mathematics : mb.carvalho Edinburgh EH9 3FD, UK +: www.maths.ed.ac.uk/ mdecarv Personal Born September 20, 1980 in Montijo, Lisbon. Details Portuguese and EU citizenship. Interests Applied Statistics, Biostatistics, Econometrics, Risk Analysis, Statistics of Extremes. Education Universidade de Lisboa, Portugal Habilitation in Probability and Statistics, 2019 Thesis: Statistical Modeling of Extremes Universidade Nova de Lisboa, Portugal PhD in Mathematics with emphasis on Statistics, 2009 Thesis: Extremum Estimators and Stochastic Optimization Advisors: Manuel Esqu´ıvel and Tiago Mexia Advisors of Advisors: Jean-Pierre Kahane and Tiago de Oliveira. Nova School of Business and Economics (Triple Accreditation), Portugal MSc in Economics, 2009 Thesis: Mean Regression for Censored Length-Biased Data Advisors: Jos´eA. F. Machado and Pedro Portugal Advisors of Advisors: Roger Koenker and John Addison. Universidade Nova de Lisboa, Portugal `Licenciatura'y in Mathematics, 2004 Professional Probation Period: Statistics Portugal (Instituto Nacional de Estat´ıstica). Awards & ISBA (International Society for Bayesian Analysis) Honours Lindley Award, 2019. TWAS (Academy of Sciences for the Developing World) Young Scientist Prize, 2015. International Statistical Institute Elected Member, 2014. American Statistical Association Young Researcher Award, Section on Risk Analysis, 2011. National Institute of Statistical Sciences j American Statistical Association Honorary Mention as a Finalist NISS/ASA Best y-BIS Paper Award, 2010. Portuguese Statistical Society (Sociedade Portuguesa de Estat´ıstica) Young Researcher Award, 2009. International Association for Statistical Computing ERS IASC Young Researcher Award, 2008. 1 of 13 p l e t t si a o e igulctoson Applied Statistical ModelingPublications 1.
    [Show full text]
  • What Is in a Word Or Two?
    What Is in a Word or Two? Dale J. Poirier Department of Economics University of California, Irvine April 15, 2004 Abstract To measure the impact of Bayesian reasoning, this paper investigates the occurrence of two words, “Bayes” and “Bayesian,” over 1970-2003 in journal articles in a variety of disciplines, with a focus on economics and statistics. The growth in statistics is documented, but the growth in economics is largely confined to economic theory/mathematical economics rather than econometrics. Poirier (1989, 1992) described the penetration of Bayesian articles in econometrics and statistics journals. Data were collected by examination of individual articles and classifying each as “Bayesian” or “non-Bayesian.” Here the time period is expanded to 1970-2003 (when possible), and the number of journals is expanded to include journals in JSTOR (see Appendix) plus some from Elsevier [Journal of Econometrics (JE) and Economics Letters (EL)], the American Statistical Association [Journal of Business & Economic Statistics (JBES)], and Cambridge University Press [Econometric Theory (ET)]. Attention focuses (but not exclusively) on economics and statistics. The data collection exercise is “objectified” by using search engines to compute the annual proportion of journal “articles” containing in their text either the words “Bayes” or “Bayesian.” While not all such articles are “Bayesian,” their numbers provide an upper bound on the number of Bayesian articles, and they capture the impact of Bayesian thinking on authors. Two qualifiers should be kept in mind. First, what constitutes an “article” differs across journals. Some journals [e.g., Journal of the American Statistical Association (JASA)] count comments and replies separately, whereas other journals [e.g., Journal of the Royal Statistical Society, Series B (JRSSB)] count them as part of the original text.
    [Show full text]
  • Abbreviations of Names of Serials
    Abbreviations of Names of Serials This list gives the form of references used in Mathematical Reviews (MR). ∗ not previously listed E available electronically The abbreviation is followed by the complete title, the place of publication § journal reviewed cover-to-cover V videocassette series and other pertinent information. † monographic series ¶ bibliographic journal E 4OR 4OR. Quarterly Journal of the Belgian, French and Italian Operations Research ISSN 1211-4774. Societies. Springer, Berlin. ISSN 1619-4500. §Acta Math. Sci. Ser. A Chin. Ed. Acta Mathematica Scientia. Series A. Shuxue Wuli † 19o Col´oq. Bras. Mat. 19o Col´oquio Brasileiro de Matem´atica. [19th Brazilian Xuebao. Chinese Edition. Kexue Chubanshe (Science Press), Beijing. (See also Acta Mathematics Colloquium] Inst. Mat. Pura Apl. (IMPA), Rio de Janeiro. Math.Sci.Ser.BEngl.Ed.) ISSN 1003-3998. † 24o Col´oq. Bras. Mat. 24o Col´oquio Brasileiro de Matem´atica. [24th Brazilian §ActaMath.Sci.Ser.BEngl.Ed. Acta Mathematica Scientia. Series B. English Edition. Mathematics Colloquium] Inst. Mat. Pura Apl. (IMPA), Rio de Janeiro. Science Press, Beijing. (See also Acta Math. Sci. Ser. A Chin. Ed.) ISSN 0252- † 25o Col´oq. Bras. Mat. 25o Col´oquio Brasileiro de Matem´atica. [25th Brazilian 9602. Mathematics Colloquium] Inst. Nac. Mat. Pura Apl. (IMPA), Rio de Janeiro. § E Acta Math. Sin. (Engl. Ser.) Acta Mathematica Sinica (English Series). Springer, † Aastaraam. Eesti Mat. Selts Aastaraamat. Eesti Matemaatika Selts. [Annual. Estonian Heidelberg. ISSN 1439-8516. Mathematical Society] Eesti Mat. Selts, Tartu. ISSN 1406-4316. § E Acta Math. Sinica (Chin. Ser.) Acta Mathematica Sinica. Chinese Series. Chinese Math. Abh. Braunschw. Wiss. Ges. Abhandlungen der Braunschweigischen Wissenschaftlichen Soc., Acta Math.
    [Show full text]
  • A Nonparametric Estimator of Heterogeneity Variance with Applications to SMR- and Proportion-Data
    Biometrical Journal 42 )2000) 3, 321±334 A Nonparametric Estimator of Heterogeneity Variance with Applications to SMR- and Proportion-Data Dankmar BoÈhning Department of Epidemiology Institute for Social Medicine and Medical Psychology Free University Berlin Germany Jesus Sarol Jr. Department of Epidemiology and Biostatistics The College of Public Health University of the Philippines Manila Philippines Summary In this paper the situation of extra population heterogeneity is discussed from a analysis of variance point of view. We first provide a non-iterative way of estimating the variance of the heterogeneity distribution without estimating the heterogeneity distribution itself for Poisson and binomial counts. The consequences of the presence of heterogeneity in the estimation of the mean are discussed. We show that if the homogeneity assumption holds, the pooled mean is optimal while in the presence of strong heterogeneity, the simple )arithmetic) mean is an optimal estimator of the mean SMR or mean proportion. These results lead to the problem of finding an optimal estimator for situations not repre- sented by these two extreme cases. We propose an iterative solution to this problem. Illustrations for the application of these findings are provided with examples from various areas. Key words: Population heterogeneity; Random effects model; Moment estimator; Variance separation; Confidence interval estimation adjusted for unob- served heterogeneity. 1. Introduction In a variety of biometric applications the situation of extra-population heteroge- neity occurs. In particular, this is the case if there is good reason to model the variable of interest Y through a density of parametric form p)y, q) with a scalar parameter q.
    [Show full text]
  • A Simple Method for Correcting for the Will Rogers Phenomenon with Biometrical Applications
    University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Engineering, Computing and Mathematics 2020-01-20 A simple method for correcting for the Will Rogers phenomenon with biometrical applications Stander, M http://hdl.handle.net/10026.1/15227 10.1002/bimj.201900199 Biometrical Journal: journal of mathematical methods in biosciences Wiley-VCH Verlag All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. Biometrical Journal 52 (2010) 61, zzz–zzz / DOI: 10.1002/bimj.200100000 A simple method for correcting for the Will Rogers phenomenon with biometrical applications Mark Stander∗ 1 and Julian Stander 2 1 BAE Systems 6 Carlton Gardens LONDON SW1Y 5AD United Kingdom Email: [email protected] 2 School of Engineering, Computing and Mathematics University of Plymouth Drake Circus PLYMOUTH PL4 8AA United Kingdom Email: [email protected] Received zzz, revised zzz, accepted zzz In its basic form the Will Rogers phenomenon takes place when an increase in the average value of each of two sets is achieved by moving an element from one set to another. This leads to the conclusion that there has been an improvement, when in fact essentially nothing has changed. Extended versions of this phenomenon can occur in epidemiological studies, rendering their results unreliable.
    [Show full text]
  • Journal of Econometrics, 133, 97-126 (With R. Paap). • Testing, 2005, Forthcoming in the New Palgrave Dictionary of Economics
    Curriculum Vitae Personal data: Name Frank Roland Kleibergen Professional Experience: • September 2003-present: professor of Economics, Brown University • January 2002-December 2007: associate professor University of Amsterdam sponsored by a NWO Vernieuwingsimpuls research grant • April 99-December 2001: Postdoc in Econometrics at the University of Amsterdam • April 96-April 99: NWO-PPS Postdoc Erasmus University Rotterdam • August 95-April 96: Assistant professor, Econometrics Department, Tilburg University • September 94-August 95: Assistant professor, Finance Department, Erasmus University Rotterdam Grants and Prizes: • Fellow of the Journal of Econometrics, 2007 • 2003 Joint winner of the Tinbergen Prize for the most scientifically successful alumnus of the Tinbergen Institute • Acquired a DFL 1.5 million (680670 € = 680000 USD) NWO (Dutch NSF) Vernieuwingsimpuls research grant entitled “Empirical Comparison of Economic models” for the period 2002-2007 • Acquired a DFL 200.000 (90.756 € = 80000 USD) NWO PPS subsidy for 1995-1997 Editorships: Associate editor of Economics Letters Referee of the journals/conferences: Journal of Econometrics, Journal of Applied Econometrics, Journal of Empirical Finance, Econometric Theory, Econometrica, Journal of the American Statistical Association, International Journal of Forecasting, Journal of Business and Economic Statistics, Review of Economics and Statistics, Econometrics Journal, Economics Letters, Program committee member ESEM 2002 and Econometric Society World Meeting 2005. Scientific publications: • Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve, 2009, Journal of Business and Economic Statistics, (Invited Paper), 27, 293-311 (with S. Mavroeidis). • Rejoinder, 2009, Journal of Business and Economic Statistics, 27, 331-339. (with S. Mavroedis) • Tests of Risk Premia in Linear Factor Models , 2009, Journal of Econometrics, 149, 149- 173.
    [Show full text]
  • G023) 1 Autumn Term 2007/2008: Weeks 2 - 8 J´Erˆomeadda
    M.Sc. in Economics Department of Economics, University College London Econometric Theory and Methods (G023) 1 Autumn term 2007/2008: weeks 2 - 8 J¶er^omeAdda for an appointment, e-mail [email protected] Introduction This course provides students with a foundation in econometric theory and methods. Courses in Microeconometrics and Time Series Econometrics in the Spring Term provide further instruction on speci¯c econometric topics. Many of the other M.Sc. course in economics provide material essential in developing econometric models and a strong motivation for the use of econometric methods. Some of these courses will make frequent reference to the results of econo- metric analysis. Econometric methods are used by academic researchers to develop and test models of economic behaviour and interaction, by govern- ment in the design and evaluation of economic and social policy and by commercial users to understand aspects of the markets in which they oper- ate. Most practicing professional economists, and many academic economists, have to conduct, evaluate, or use the results of econometric analysis on a day to day basis. There has been recent rapid growth in the availability of information on economic phenomena, in the computing power necessary to access and analyse this information, and in the scope and power of econo- metric techniques. As a result there is rapidly increasing demand for people with good econometric skills. 1I am grateful to Andrew Chesher, the previous lecturer of this course to make the teaching material available. 1 Econometrics Econometrics is the collection of tools and methods used to understand and predict economic phenomena.
    [Show full text]
  • Area13 ‐ Riviste Di Classe A
    Area13 ‐ Riviste di classe A SETTORE CONCORSUALE / TITOLO 13/A1‐A2‐A3‐A4‐A5 ACADEMY OF MANAGEMENT ANNALS ACADEMY OF MANAGEMENT JOURNAL ACADEMY OF MANAGEMENT LEARNING & EDUCATION ACADEMY OF MANAGEMENT PERSPECTIVES ACADEMY OF MANAGEMENT REVIEW ACCOUNTING REVIEW ACCOUNTING, AUDITING & ACCOUNTABILITY JOURNAL ACCOUNTING, ORGANIZATIONS AND SOCIETY ADMINISTRATIVE SCIENCE QUARTERLY ADVANCES IN APPLIED PROBABILITY AGEING AND SOCIETY AMERICAN ECONOMIC JOURNAL. APPLIED ECONOMICS AMERICAN ECONOMIC JOURNAL. ECONOMIC POLICY AMERICAN ECONOMIC JOURNAL: MACROECONOMICS AMERICAN ECONOMIC JOURNAL: MICROECONOMICS AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS AMERICAN POLITICAL SCIENCE REVIEW AMERICAN REVIEW OF PUBLIC ADMINISTRATION ANNALES DE L'INSTITUT HENRI POINCARE‐PROBABILITES ET STATISTIQUES ANNALS OF PROBABILITY ANNALS OF STATISTICS ANNALS OF TOURISM RESEARCH ANNU. REV. FINANC. ECON. APPLIED FINANCIAL ECONOMICS APPLIED PSYCHOLOGICAL MEASUREMENT ASIA PACIFIC JOURNAL OF MANAGEMENT AUDITING BAYESIAN ANALYSIS BERNOULLI BIOMETRICS BIOMETRIKA BIOSTATISTICS BRITISH JOURNAL OF INDUSTRIAL RELATIONS BRITISH JOURNAL OF MANAGEMENT BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY BROOKINGS PAPERS ON ECONOMIC ACTIVITY BUSINESS ETHICS QUARTERLY BUSINESS HISTORY REVIEW BUSINESS HORIZONS BUSINESS PROCESS MANAGEMENT JOURNAL BUSINESS STRATEGY AND THE ENVIRONMENT CALIFORNIA MANAGEMENT REVIEW CAMBRIDGE JOURNAL OF ECONOMICS CANADIAN JOURNAL OF ECONOMICS CANADIAN JOURNAL OF FOREST RESEARCH CANADIAN JOURNAL OF STATISTICS‐REVUE CANADIENNE DE STATISTIQUE CHAOS CHAOS, SOLITONS
    [Show full text]
  • Rank Full Journal Title Journal Impact Factor 1 Journal of Statistical
    Journal Data Filtered By: Selected JCR Year: 2019 Selected Editions: SCIE Selected Categories: 'STATISTICS & PROBABILITY' Selected Category Scheme: WoS Rank Full Journal Title Journal Impact Eigenfactor Total Cites Factor Score Journal of Statistical Software 1 25,372 13.642 0.053040 Annual Review of Statistics and Its Application 2 515 5.095 0.004250 ECONOMETRICA 3 35,846 3.992 0.040750 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 4 36,843 3.989 0.032370 JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 5 25,492 3.965 0.018040 STATISTICAL SCIENCE 6 6,545 3.583 0.007500 R Journal 7 1,811 3.312 0.007320 FUZZY SETS AND SYSTEMS 8 17,605 3.305 0.008740 BIOSTATISTICS 9 4,048 3.098 0.006780 STATISTICS AND COMPUTING 10 4,519 3.035 0.011050 IEEE-ACM Transactions on Computational Biology and Bioinformatics 11 3,542 3.015 0.006930 JOURNAL OF BUSINESS & ECONOMIC STATISTICS 12 5,921 2.935 0.008680 CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 13 9,421 2.895 0.007790 MULTIVARIATE BEHAVIORAL RESEARCH 14 7,112 2.750 0.007880 INTERNATIONAL STATISTICAL REVIEW 15 1,807 2.740 0.002560 Bayesian Analysis 16 2,000 2.696 0.006600 ANNALS OF STATISTICS 17 21,466 2.650 0.027080 PROBABILISTIC ENGINEERING MECHANICS 18 2,689 2.411 0.002430 BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 19 1,965 2.388 0.003480 ANNALS OF PROBABILITY 20 5,892 2.377 0.017230 STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 21 4,272 2.351 0.006810 JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 22 4,369 2.319 0.008900 STATISTICAL METHODS IN
    [Show full text]
  • An Information Theoretical Method for Analyzing Unreplicated Designs with Binary Response
    REVSTAT – Statistical Journal Volume 17, Number 3, July 2019, 383–399 AN INFORMATION THEORETICAL METHOD FOR ANALYZING UNREPLICATED DESIGNS WITH BINARY RESPONSE Authors: Krystallenia Drosou – Department of Mathematics, National Technical University of Athens, Athens, Greece [email protected] Christos Koukouvinos – Department of Mathematics, National Technical University of Athens, Athens, Greece [email protected] Received: February 2017 Revised: March 2017 Accepted: April 2017 Abstract: • The analysis of unreplicated factorial designs constitutes a challenging but difficult issue since there are no degrees of freedom so as to estimate the error variance. In the present paper we propose a method for screening active effects in such designs, as- suming Bernoulli distributed data rather than linear; something that hasn’t received much attention yet. Specifically, we develop an innovating algorithm based on an information theoretical measure, the well-known symmetrical uncertainty, so that it can measure the relation between the response variable and each factor separately. The powerfulness of the proposed method is revealed via both, a thorough simulation study and a real data set analysis. Key-Words: • two-level factorial designs; unreplicated experiments; generalized linear models; symmetrical uncertainty. AMS Subject Classification: • 62-07, 62K15, 62J12. 384 Krystallenia Drosou and Christos Koukouvinos Analysis of Unreplicated Designs with Binary Response 385 1. INTRODUCTION Factorial designs constitute a powerful tool especially in screening experi- ments where the goal is to identify the factors with a significant impact on the response of interest. Although two-level factorial designs are commonly used as experimental plans, the number of runs grows exponentially as the number of factors increases; thus, in case when the replication of the experiment is pro- hibitive due to economical or technical issues, unreplicated designs constitute an appropriate choice.
    [Show full text]