DOCSLIB.ORG

Impact Factor, Scimago Journal Rank and QUALIS

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Impact Factor, Scimago Journal Rank and QUALIS Impact factor, scimago journal rank and QUALIS By Hedibert F. Lopes (www.hedibert.org) All the plots were based on data publicly available on the internet. Impact factor (2008-2016) - QUALIS classification in parenthesis 6 6 10 ● Journal of the Royal Statistical Society. Series B: Statistical Methodology (A1) ● Statistics and Computing (A1) ● Biometrika (A1) ● Journal of Statistical Software (B1) ● ● Entropy (B2) ● Bayesian Analysis (A1) ● Journal of Machine Learning Research (B1) ● Annals of Applied Statistics (A2) ● Journal of Statistical Physics (A2) ● Annals of Statistics (A1) 5 ● Environmetrics (B1) 5 ● Computational Statistics and Data Analysis (B1) 8 ● Biostatistics (NA) ● International Statistical Review (NA) ● Bernoulli (A1) ● Statistical Science (NA) ● Journal of Computational and Graphical Statistics (A1) ● Test (A2) ● Journal of the American Statistical Association (A1) ● Journal of the Royal Statistical Society. Series C: Applied Statistics (NA) ● Statistica Sinica (A1) 4 4 6 ● ● 3 3 ● ● ● ● ● ● ● 4 ● Impact Factor ● ● Impact Factor Impact Factor ● ● ● ● ● ● ● ● ● 2 ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 Years Years Years ● ● ● 3.0 Biometrical Journal (NA) 3.0 AStA Advances in Statistical Analysis (NA) 3.0 Journal of Official Statistics (B1) ● Statistical Modelling (B1) ● Statistical Papers (B2) ● Statistics and Probability Letters (NA) ● Journal of Multivariate Analysis (A2) ● Lifetime Data Analysis (B1) ● Statistica Neerlandica (B1) ● Scandinavian Journal of Statistics (A2) ● Journal of Statistical Computation and Simulation (B1) ● Journal of Applied Statistics (B1) 2.5 2.5 2.5 ● American Statistician (NA) ● Journal of Statistical Planning and Inference (B1) ● Brazilian Journal of Probability and Statistics (B1) ● Journal of Time Series Analysis (A2) ● Canadian Journal of Statistics (B1) ● Computational Statistics (B2) ● Technometrics (A1) ● Applied Stochastic Models in Business and Industry (B2) ● Communications in Statistics Part B: Simulation and Computation (B2) 2.0 2.0 2.0 ● Communications in Statistics − Theory and Methods (B2) 1.5 ● 1.5 1.5 ● ● ● ● ● ● ● ● Impact Factor ● ● Impact Factor ● Impact Factor ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● 1.0 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 0.5 ● 0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.0 0.0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 Years Years Years SJR (2008-2017) - QUALIS classification in parenthesis 5 5 15 ● Journal of the Royal Statistical Society. Series B: Statistical Methodology (A1) ● Statistical Science (NA) ● Scandinavian Journal of Statistics (A2) ● ● ● Annals of Statistics (A1) ● Statistica Sinica (A1) Bayesian Analysis (A1) ● Journal of Statistical Software (B1) ● Bernoulli (A1) ● Test (A2) ● Journal of the American Statistical Association (A1) ● Statistics and Computing (A1) ● Journal of Multivariate Analysis (A2) ● Biometrika (A1) 4 ● Biostatistics (NA) 4 ● Journal of the Royal Statistical Society. Series C: Applied Statistics (NA) ● Journal of Computational and Graphical Statistics (A1) ● Annals of Applied Statistics (A2) ● Computational Statistics and Data Analysis (B1) 10 ● ● 3 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SJR index ● ● ● ● SJR index ● ● ● ● ● SJR index ● 2 ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 Years Years Years 5 ● ● ● Technometrics (A1) 3.0 Biometrical Journal (NA) 3.0 Journal of Official Statistics (B1) ● Journal of Machine Learning Research (B1) ● Environmetrics (B1) ● AStA Advances in Statistical Analysis (NA) ● Journal of Time Series Analysis (A2) ● Statistical Modelling (B1) ● Statistics and Probability Letters (NA) ● Journal of Statistical Planning and Inference (B1) ● Journal of Statistical Physics (A2) ● Journal of Statistical Computation and Simulation (B1) 2.5 2.5 4 ● International Statistical Review (NA) ● Canadian Journal of Statistics (B1) ● American Statistician (NA) ● Statistical Papers (B2) ● Lifetime Data Analysis (B1) ● Computational Statistics (B2) 2.0 2.0 3 ● ● 1.5 1.5 ● ● ● SJR index SJR index ● ● SJR index 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● ● ● ● 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0.0 0.0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 Years Years Years Impact factor versus Scimago journal rank (SJR) (2016) - QUALIS classification by color A1/A2 (SJR>1.125) + B1/B2 (SJR<1.125) ● NA ● A1 ● A2 JRSSB AOS 7.15 ● B1 ● B2 JSS 3.85 JASA Btka JCGS SSCI BERN STCO 2.08 SINICA AOAS BSTA JMASJS TEST BA CSDAAS SJR 2016 JMLR JSPI TECH JTSA CJS SM 1.12 SP ISR ENVI JSP AdSA BJ JOS JSCS CS SPL ASMBI CSSC 0.6 TAS ENTR LDA CSTM BJPS JAS SNEER 0.32 0.31 0.62 1.22 2.41 4.77 9.44 Impact factor 2016 Correlation between IF and SJR - QUALIS classification by color Correlation between IF and SJR JRSSB ● NA AOS ● A1 JSS ● A2 40 JASA Btka ● B1 JCGS SSCI ● B2 SINICA BERN STCO BSTA SJS 30 BA TEST JMA AS CSDA TECH JMLR JTSA JSPI ISR Journals 20 SP BJ ENVI SM JSP CJS LDA JOS AdSA SPL 10 JSCS TAS CS ASMBI ENTR SNEER CSSC JAS CSTM BJPS 0 0.0 0.2 0.4 0.6 0.8 1.0 Correlation IF and SJR over the years - QUALIS classification by color ● A1 ● A2 ● B1 1.0 ● B2 1.0 ● NA 0.5 0.5 SJR index (log10) SJR index Impact factor (log10) Impact factor 0.0 0.0 0.5 0.5 − − 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 Years Years IF and SJR rankings (boxplot over the years) JRSSB JRSSB JSS AOS JMLR JSS AOS JASA BSTA Btka SSCI JCGS JASA SSCI STCO SINICA ENTR BERN AOAS STCO ENVI BSTA ISR AOAS JCGS SJS AS BA Btka TEST BA JMA JSP AS CSDA CSDA BERN TECH TEST JMLR SINICA JTSA BJ JSPI SM ISR JMA SP SJS BJ TAS ENVI JTSA SM TECH JSP AdSA CJS SP LDA LDA JOS JSCS AdSA JSPI SPL CJS JSCS ASMBI TAS JOS CS SPL ASMBI SNEER ENTR JAS SNEER BJPS CSSC CS JAS CSSC CSTM CSTM BJPS 0 10 20 30 40 0 10 20 30 40 IF rankings SJR rankings.
Load more

Recommended publications
  • BIOGRAPHICAL SKETCH Garrett M. Fitzmaurice Associate Professor Of
    Principal Investigator/Program Director (Last, First, Middle): BIOGRAPHICAL SKETCH Provide the following information for the key personnel and other significant contributors in the order listed on Form Page 2. Follow this format for each person. DO NOT EXCEED FOUR PAGES. NAME POSITION TITLE Garrett M. Fitzmaurice Associate Professor of Biostatistics eRA COMMONS USER NAME EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.) DEGREE INSTITUTION AND LOCATION YEAR(s) FIELD OF STUDY (if applicable) National University of Ireland BA, MA 1983, 1987 Psychology University of London MSc 1986 Quantitative Methods Harvard University ScD 1993 Biostatistics Professional Experience: 1986-1989 Statistician, Department of Psychology, New York University 1989-1990 Teaching Assistant, Department of Biostatistics, Harvard School of Public Health 1990-1993 Teaching Fellow in Biostatistics, Department of Biostatistics, Harvard SPH 1993-1994 Post-doctoral Research Fellow, Department of Biostatistics, Harvard SPH 1994-1997 Research Fellow, Nuffield College, Oxford University, United Kingdom 1997-1999 Assistant Professor of Biostatistics, Harvard School of Public Health 1999-present Associate Professor of Biostatistics, Harvard School of Public Health 2004-present Associate Professor of Medicine (Biostatistics), Harvard Medical School 2004-present Biostatistician, Division of General Medicine, Brigham and Women’s Hospital, Boston 2006-present Foreign Adjunct Professor of Biostatistics,
    [Show full text]
  • TUTORIAL in BIOSTATISTICS: the Self-Controlled Case Series Method
    STATISTICS IN MEDICINE Statist. Med. 2005; 0:1–31 Prepared using simauth.cls [Version: 2002/09/18 v1.11] TUTORIAL IN BIOSTATISTICS: The self-controlled case series method Heather J. Whitaker1, C. Paddy Farrington1, Bart Spiessens2 and Patrick Musonda1 1 Department of Statistics, The Open University, Milton Keynes, MK7 6AA, UK. 2 GlaxoSmithKline Biologicals, Rue de l’Institut 89, B-1330 Rixensart, Belgium. SUMMARY The self-controlled case series method was developed to investigate associations between acute outcomes and transient exposures, using only data on cases, that is, on individuals who have experienced the outcome of interest. Inference is within individuals, and hence fixed covariates effects are implicitly controlled for within a proportional incidence framework. We describe the origins, assumptions, limitations, and uses of the method. The rationale for the model and the derivation of the likelihood are explained in detail using a worked example on vaccine safety. Code for fitting the model in the statistical package STATA is described. Two further vaccine safety data sets are used to illustrate a range of modelling issues and extensions of the basic model. Some brief pointers on the design of case series studies are provided. The data sets, STATA code, and further implementation details in SAS, GENSTAT and GLIM are available from an associated website. key words: case series; conditional likelihood; control; epidemiology; modelling; proportional incidence Copyright c 2005 John Wiley & Sons, Ltd. 1. Introduction The self-controlled case series method, or case series method for short, provides an alternative to more established cohort or case-control methods for investigating the association between a time-varying exposure and an outcome event.
    [Show full text]
  • Alberto Abadie
    ALBERTO ABADIE Office Address Massachusetts Institute of Technology Department of Economics 50 Memorial Drive Building E52, Room 546 Cambridge, MA 02142 E-mail: [email protected] Academic Positions Massachusetts Institute of Technology Cambridge, MA Professor of Economics, 2016-present IDSS Associate Director, 2016-present Harvard University Cambridge, MA Professor of Public Policy, 2005-2016 Visiting Professor of Economics, 2013-2014 Associate Professor of Public Policy, 2004-2005 Assistant Professor of Public Policy, 1999-2004 University of Chicago Chicago, IL Visiting Assistant Professor of Economics, 2002-2003 National Bureau of Economic Research (NBER) Cambridge, MA Research Associate (Labor Studies), 2009-present Faculty Research Fellow (Labor Studies), 2002-2009 Non-Academic Positions Amazon.com, Inc. Seattle, WA Academic Research Consultant, 2020-present Education Massachusetts Institute of Technology Cambridge, MA Ph.D. in Economics, 1995-1999 Thesis title: \Semiparametric Instrumental Variable Methods for Causal Response Mod- els." Centro de Estudios Monetarios y Financieros (CEMFI) Madrid, Spain M.A. in Economics, 1993-1995 Masters Thesis title: \Changes in Spanish Labor Income Structure during the 1980's: A Quantile Regression Approach." 1 Universidad del Pa´ıs Vasco Bilbao, Spain B.A. in Economics, 1987-1992 Specialization Areas: Mathematical Economics and Econometrics. Honors and Awards Elected Fellow of the Econometric Society, 2016. NSF grant SES-1756692, \A General Synthetic Control Framework of Estimation and Inference," 2018-2021. NSF grant SES-0961707, \A General Theory of Matching Estimation," with G. Imbens, 2010-2012. NSF grant SES-0617810, \The Economic Impact of Terrorism: Lessons from the Real Estate Office Markets of New York and Chicago," with S. Dermisi, 2006-2008.
    [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]
  • Biometrics & Biostatistics
    Hanley and Moodie, J Biomet Biostat 2011, 2:5 Biometrics & Biostatistics http://dx.doi.org/10.4172/2155-6180.1000124 Research Article Article OpenOpen Access Access Sample Size, Precision and Power Calculations: A Unified Approach James A Hanley* and Erica EM Moodie Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada Abstract The sample size formulae given in elementary biostatistics textbooks deal only with simple situations: estimation of one, or a comparison of at most two, mean(s) or proportion(s). While many specialized textbooks give sample formulae/tables for analyses involving odds and rate ratios, few deal explicitly with statistical considera tions for slopes (regression coefficients), for analyses involving confounding variables or with the fact that most analyses rely on some type of generalized linear model. Thus, the investigator is typically forced to use “black-box” computer programs or tables, or to borrow from tables in the social sciences, where the emphasis is on cor- relation coefficients. The concern in the – usually very separate – modules or stand alone software programs is more with user friendly input and output. The emphasis on numerical exactness is particularly unfortunate, given the rough, prospective, and thus uncertain, nature of the exercise, and that different textbooks and software may give different sample sizes for the same design. In addition, some programs focus on required numbers per group, others on an overall number. We present users with a single universal (though sometimes approximate) formula that explicitly isolates the impacts of the various factors one from another, and gives some insight into the determinants for each factor.
    [Show full text]
  • The American Statistician
    This article was downloaded by: [T&F Internal Users], [Rob Calver] On: 01 September 2015, At: 02:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG The American Statistician Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/utas20 Reviews of Books and Teaching Materials Published online: 27 Aug 2015. Click for updates To cite this article: (2015) Reviews of Books and Teaching Materials, The American Statistician, 69:3, 244-252, DOI: 10.1080/00031305.2015.1068616 To link to this article: http://dx.doi.org/10.1080/00031305.2015.1068616 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes.
    [Show full text]
  • Econometrica 0012-9682 Biostatistics 1465-4644 J R
    ECONOMETRICA 0012-9682 BIOSTATISTICS 1465-4644 J R STAT SOC B 1369-7412 ANN APPL STAT 1932-6157 J AM STAT ASSOC 0162-1459 ANN STAT 0090-5364 STAT METHODS MED RES 0962-2802 STAT SCI 0883-4237 STAT MED 0277-6715 BIOMETRICS 0006-341X CHEMOMETR INTELL LAB 0169-7439 IEEE ACM T COMPUT BI 1545-5963 J BUS ECON STAT 0735-0015 J QUAL TECHNOL 0022-4065 FUZZY SET SYST 0165-0114 STAT APPL GENET MOL 1544-6115 PHARM STAT 1539-1604 MULTIVAR BEHAV RES 0027-3171 PROBAB THEORY REL 0178-8051 J COMPUT BIOL 1066-5277 STATA J 1536-867X J COMPUT GRAPH STAT 1061-8600 J R STAT SOC A STAT 0964-1998 INSUR MATH ECON 0167-6687 J CHEMOMETR 0886-9383 BIOMETRIKA 0006-3444 BRIT J MATH STAT PSY 0007-1102 STAT COMPUT 0960-3174 J AGR BIOL ENVIR ST 1085-7117 ANN APPL PROBAB 1050-5164 ANN PROBAB 0091-1798 SCAND J STAT 0303-6898 AM STAT 0003-1305 ECONOMET REV 0747-4938 FINANC STOCH 0949-2984 ELECTRON J PROBAB 1083-6489 OPEN SYST INF DYN 1230-1612 COMPUT STAT DATA AN 0167-9473 BIOMETRICAL J 0323-3847 PROBABILIST ENG MECH 0266-8920 TECHNOMETRICS 0040-1706 STOCH PROC APPL 0304-4149 J R STAT SOC C-APPL 0035-9254 ENVIRON ECOL STAT 1352-8505 J STAT SOFTW 1548-7660 BERNOULLI 1350-7265 INFIN DIMENS ANAL QU 0219-0257 J BIOPHARM STAT 1054-3406 STOCH ENV RES RISK A 1436-3240 TEST 1133-0686 ASTIN BULL 0515-0361 ADV APPL PROBAB 0001-8678 J TIME SER ANAL 0143-9782 MATH POPUL STUD 0889-8480 COMB PROBAB COMPUT 0963-5483 LIFETIME DATA ANAL 1380-7870 METHODOL COMPUT APPL 1387-5841 ECONOMET J 1368-4221 STATISTICS 0233-1888 J APPL PROBAB 0021-9002 J MULTIVARIATE ANAL 0047-259X ENVIRONMETRICS 1180-4009
    [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]
  • Kshitij Khare
    Kshitij Khare Basic Information Mailing Address: Telephone Numbers: Internet: Department of Statistics Office: (352) 273-2985 E-mail: [email protected]fl.edu 103 Griffin Floyd Hall FAX: (352) 392-5175 Web: http://www.stat.ufl.edu/˜kdkhare/ University of Florida Gainesville, FL 32611 Education PhD in Statistics, 2009, Stanford University (Advisor: Persi Diaconis) Masters in Mathematical Finance, 2009, Stanford University Masters in Statistics, 2004, Indian Statistical Institute, India Bachelors in Statistics, 2002, Indian Statistical Institute, India Academic Appointments University of Florida: Associate Professor of Statistics, 2015-present University of Florida: Assistant Professor of Statistics, 2009-2015 Stanford University: Research/Teaching Assistant, Department of Statistics, 2004-2009 Research Interests High-dimensional covariance/network estimation using graphical models High-dimensional inference for vector autoregressive models Markov chain Monte Carlo methods Kshitij Khare 2 Publications Core Statistics Research Ghosh, S., Khare, K. and Michailidis, G. (2019). “High dimensional posterior consistency in Bayesian vector autoregressive models”, Journal of the American Statistical Association 114, 735-748. Khare, K., Oh, S., Rahman, S. and Rajaratnam, B. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, Machine Learning 108, 2061-2086. Cao, X., Khare, K. and Ghosh, M. (2019). “High-dimensional posterior consistency for hierarchical non- local priors in regression”, Bayesian Analysis 15, 241-262. Chakraborty, S. and Khare, K. (2019). “Consistent estimation of the spectrum of trace class data augmen- tation algorithms”, Bernoulli 25, 3832-3863. Cao, X., Khare, K. and Ghosh, M. (2019). “Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models”, Annals of Statistics 47, 319-348.
    [Show full text]
  • Department of Statistics and Data Science Promotion and Tenure Guidelines
    Approved by Department 12/05/2013 Approved by Faculty Relations May 20, 2014 UFF Notified May 21, 2014 Effective Spring 2016 2016-17 Promotion Cycle Department of Statistics and Data Science Promotion and Tenure Guidelines The purpose of these guidelines is to give explicit definitions of what constitutes excellence in teaching, research and service for tenure-earning and tenured faculty. Research: The most common outlet for scholarly research in statistics is in journal articles appearing in refereed publications. Based on the five-year Impact Factor (IF) from the ISI Web of Knowledge Journal Citation Reports, the top 50 journals in Probability and Statistics are: 1. Journal of Statistical Software 26. Journal of Computational Biology 2. Econometrica 27. Annals of Probability 3. Journal of the Royal Statistical Society 28. Statistical Applications in Genetics and Series B – Statistical Methodology Molecular Biology 4. Annals of Statistics 29. Biometrical Journal 5. Statistical Science 30. Journal of Computational and Graphical Statistics 6. Stata Journal 31. Journal of Quality Technology 7. Biostatistics 32. Finance and Stochastics 8. Multivariate Behavioral Research 33. Probability Theory and Related Fields 9. Statistical Methods in Medical Research 34. British Journal of Mathematical & Statistical Psychology 10. Journal of the American Statistical 35. Econometric Theory Association 11. Annals of Applied Statistics 36. Environmental and Ecological Statistics 12. Statistics in Medicine 37. Journal of the Royal Statistical Society Series C – Applied Statistics 13. Statistics and Computing 38. Annals of Applied Probability 14. Biometrika 39. Computational Statistics & Data Analysis 15. Chemometrics and Intelligent Laboratory 40. Probabilistic Engineering Mechanics Systems 16. Journal of Business & Economic Statistics 41.
    [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]