Department of Statistics and Data Science Promotion and Tenure Guidelines
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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. -
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. -
Biostatistics Student Paper Competition the Department of Biostatistics at BUSPH Is Soliciting Applications for the 2013 Student Paper Competition
Biostatistics Student Paper Competition The Department of Biostatistics at BUSPH is soliciting applications for the 2013 student paper competition. The competition is open to all students enrolled in the Biostatistics Program at Boston University in the spring term of 2013. The winner of the competition will receive $750 as a travel award to attend the summer Joint Statistical Meetings. Travel to other conferences (i.e. ENAR, Society for Clinical Trials, American Society of Human Genetics, International Genetic Epidemiology Society) is possible in negotiation with the award committee. All students are strongly encouraged to submit the abstracts from their papers to the Joint Statistical Meetings, which has a deadline of February 4th. Students would also be encouraged to consider entering the competitions sponsored by sections of the Joint Statistical Meetings (e.g. The Biometrics section Byar Young Investigator award, for details see http://www.bio.ri.ccf.org/Biometrics/winner.html). TIMETABLE: January 11th, 2013 at noon: Deadline for students to submit pdf of manuscript January 25th, 2013: Announcement of awards REQUIREMENTS: The paper should be prepared double spaced, in manuscript format, using guidelines for authors typical of a biostatistical journal (e.g. Biometrics, Statistics in Medicine, or Biostatistics). A pdf copy of the manuscript should be submitted to the chair of the award committee, Ching-Ti Liu ([email protected]). The reported work should be relevant to biostatistics and must be the work of the student, although the manuscript can be co-authored with a faculty advisor or a small number of collaborators. The student must be the first author of the manuscript. -
Biometrical Applications in Biological Sciences-A Review on the Agony for Their Practical Efficiency- Problems and Perspectives
Biometrics & Biostatistics International Journal Review Article Open Access Biometrical applications in biological sciences-A review on the agony for their practical efficiency- Problems and perspectives Abstract Volume 7 Issue 5 - 2018 The effect of the three biological sciences- agriculture, environment and medicine S Tzortzios in the people’s life is of the greatest importance. The chain of the influence of the University of Thessaly, Greece environment to the form and quality of the agricultural production and the effect of both of them to the people’s health and welfare consists in an integrated system Correspondence: S Tzortzios, University of Thessaly, Greece, that is the basic substance of the human life. Agriculture has a great importance in Email [email protected] the World’s economy; however, the available resources for research and technology development are limited. Moreover, the environmental and productive conditions are Received: August 12, 2018 | Published: October 05, 2018 very different from one country to another, restricting the generalized transferring of technology. The statistical methods should play a paramount role to insure both the objectivity of the results of agricultural research as well as the optimum usage of the limited resources. An inadequate or improper use of statistical methods may result in wrong conclusions and in misuse of the available resources with important scientific and economic consequences. Many times, Statistics is used as a basis to justify conclusions of research work without considering in advance the suitability of the statistical methods to be used. The obvious question is: What importance do biological researchers give to the statistical methods? The answer is out of any doubt and the fact that most of the results published in specialized journals includes statistical considerations, confirms its importance. -
Visualizing Research Collaboration in Statistical Science: a Scientometric
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 9-13-2019 Visualizing Research Collaboration in Statistical Science: A Scientometric Perspective Prabir Kumar Das Library, Documentation & Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata - 700108, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons Das, Prabir Kumar, "Visualizing Research Collaboration in Statistical Science: A Scientometric Perspective" (2019). Library Philosophy and Practice (e-journal). 3039. https://digitalcommons.unl.edu/libphilprac/3039 Viisualliiziing Research Collllaborattiion iin Sttattiisttiicall Sciience:: A Sciienttomettriic Perspecttiive Prrabiirr Kumarr Das Sciienttiiffiic Assssiissttantt – A Liibrrarry,, Documenttattiion & IInfforrmattiion Sciience Diiviisiion,, IIndiian Sttattiisttiicall IInsttiittutte,, 203,, B.. T.. Road,, Kollkatta – 700108,, IIndiia,, Emaiill:: [email protected] Abstract Using Sankhyā – The Indian Journal of Statistics as a case, present study aims to identify scholarly collaboration pattern of statistical science based on research articles appeared during 2008 to 2017. This is an attempt to visualize and quantify statistical science research collaboration in multiple dimensions by exploring the co-authorship data. It investigates chronological variations of collaboration pattern, nodes and links established among the affiliated institutions and countries of all contributing authors. The study also examines the impact of research collaboration on citation scores. Findings reveal steady influx of statistical publications with clear tendency towards collaborative ventures, of which double-authored publications dominate. Small team of 2 to 3 authors is responsible for production of majority of collaborative research, whereas mega-authored communications are quite low. -
Curriculum Vitae
Daniele Durante: Curriculum Vitae Contact Department of Decision Sciences B: [email protected] Information Bocconi University web: https://danieledurante.github.io/web/ Via R¨ontgen, 1, 20136 Milan Research Network Science, Computational Social Science, Latent Variables Models, Complex Data, Bayesian Interests Methods & Computation, Demography, Statistical Learning in High Dimension, Categorical Data Current Assistant Professor Academic Bocconi University, Department of Decision Sciences 09/2017 | Present Positions Research Affiliate • Bocconi Institute for Data Science and Analytics [bidsa] • Dondena Centre for Research on Social Dynamics and Public Policy • Laboratory for Coronavirus Crisis Research [covid crisis lab] Past Post{Doctoral Research Fellow 02/2016 | 08/2017 Academic University of Padova, Department of Statistical Sciences Positions Adjunct Professor 12/2015 | 12/2016 Ca' Foscari University, Department of Economics and Management Visiting Research Scholar 03/2014 | 02/2015 Duke University, Department of Statistical Sciences Education University of Padova, Department of Statistical Sciences Ph.D. in Statistics. Department of Statistical Sciences 2013 | 2016 • Ph.D. Thesis Topic: Bayesian nonparametric modeling of network data • Advisor: Bruno Scarpa. Co-advisor: David B. Dunson M.Sc. in Statistical Sciences. Department of Statistical Sciences 2010 | 2012 B.Sc. in Statistics, Economics and Finance. Department of Statistics 2007 | 2010 Awards Early{Career Scholar Award for Contributions to Statistics. Italian Statistical Society 2021 Bocconi Research Excellence Award. Bocconi University 2021 Leonardo da Vinci Medal. Italian Ministry of University and Research 2020 Bocconi Research Excellence Award. Bocconi University 2020 Bocconi Innovation in Teaching Award. Bocconi University 2020 National Scientific Qualification for Associate Professor in Statistics [13/D1] 2018 Doctoral Thesis Award in Statistics. -
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 -
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 -
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 The abbreviation is followed by the complete title, the place of publication x journal indexed cover-to-cover and other pertinent information. y monographic series Update date: January 30, 2018 4OR 4OR. A Quarterly Journal of Operations Research. Springer, Berlin. ISSN xActa Math. Appl. Sin. Engl. Ser. Acta Mathematicae Applicatae Sinica. English 1619-4500. Series. Springer, Heidelberg. ISSN 0168-9673. y 30o Col´oq.Bras. Mat. 30o Col´oquioBrasileiro de Matem´atica. [30th Brazilian xActa Math. Hungar. Acta Mathematica Hungarica. Akad. Kiad´o,Budapest. Mathematics Colloquium] Inst. Nac. Mat. Pura Apl. (IMPA), Rio de Janeiro. ISSN 0236-5294. y Aastaraam. Eesti Mat. Selts Aastaraamat. Eesti Matemaatika Selts. [Annual. xActa Math. Sci. Ser. A Chin. Ed. Acta Mathematica Scientia. Series A. Shuxue Estonian Mathematical Society] Eesti Mat. Selts, Tartu. ISSN 1406-4316. Wuli Xuebao. Chinese Edition. Kexue Chubanshe (Science Press), Beijing. ISSN y Abel Symp. Abel Symposia. Springer, Heidelberg. ISSN 2193-2808. 1003-3998. y Abh. Akad. Wiss. G¨ottingenNeue Folge Abhandlungen der Akademie der xActa Math. Sci. Ser. B Engl. Ed. Acta Mathematica Scientia. Series B. English Wissenschaften zu G¨ottingen.Neue Folge. [Papers of the Academy of Sciences Edition. Sci. Press Beijing, Beijing. ISSN 0252-9602. in G¨ottingen.New Series] De Gruyter/Akademie Forschung, Berlin. ISSN 0930- xActa Math. Sin. (Engl. Ser.) Acta Mathematica Sinica (English Series). 4304. Springer, Berlin. ISSN 1439-8516. y Abh. Akad. Wiss. Hamburg Abhandlungen der Akademie der Wissenschaften xActa Math. Sinica (Chin. Ser.) Acta Mathematica Sinica. -
The Importance of Endogenous Peer Group Formation
Econometrica, Vol. 81, No. 3 (May, 2013), 855–882 FROM NATURAL VARIATION TO OPTIMAL POLICY? THE IMPORTANCE OF ENDOGENOUS PEER GROUP FORMATION BY SCOTT E. CARRELL,BRUCE I. SACERDOTE, AND JAMES E. WEST1 We take cohorts of entering freshmen at the United States Air Force Academy and assign half to peer groups designed to maximize the academic performance of the low- est ability students. Our assignment algorithm uses nonlinear peer effects estimates from the historical pre-treatment data, in which students were randomly assigned to peer groups. We find a negative and significant treatment effect for the students we in- tended to help. We provide evidence that within our “optimally” designed peer groups, students avoided the peers with whom we intended them to interact and instead formed more homogeneous subgroups. These results illustrate how policies that manipulate peer groups for a desired social outcome can be confounded by changes in the endoge- nous patterns of social interactions within the group. KEYWORDS: Peer effects, social network formation, homophily. 0. INTRODUCTION PEER EFFECTS HAVE BEEN widely studied in the economics literature due to the perceived importance peers play in workplace, educational, and behavioral outcomes. Previous studies in the economics literature have focused almost ex- clusively on the identification of peer effects and have only hinted at the poten- tial policy implications of the results.2 Recent econometric studies on assorta- tive matching by Graham, Imbens, and Ridder (2009) and Bhattacharya (2009) have theorized that individuals could be sorted into peer groups to maximize productivity.3 This study takes a first step in determining whether student academic per- formance can be improved through the systematic sorting of students into peer groups. -
Econometrics of Sampled Networks
ECONOMETRICS OF SAMPLED NETWORKS ARUN G. CHANDRASEKHAR‡ AND RANDALL LEWIS§ Abstract. A growing literature studies social networks and their implications for economic outcomes. This paper highlights, examines, and addresses econometric problems that arise when a researcher studies network effects using sampled network data. In applied work, researchers generally construct networks from data collected from a partial sample of nodes. For example, in a village, a researcher asks a random subset of households to report their social contacts. Treating this sampled network as the true network of interest, the researcher constructs statistics to describe the network or specific nodes and employs these statistics in regression or generalized method-of-moments (GMM) analysis. This paper shows that even if nodes are selected randomly, partial sampling leads to non-classical measurement error and therefore bias in estimates of the regression coefficients or GMM parameters. The paper presents the first in-depth look at the impact of missing network data on the estimation of economic parameters. We provide analytic and numeric examples to illustrate the severity of the biases in common applications. We then develop two new strategies to correct such biases: a set of analytic corrections for commonly used network statistics and a two-step estimation procedure using graphical reconstruction. Graphical reconstruction uses the available (partial) network data to predict what the full network would have been and uses these predictions to mitigate the biases. We derive asymptotic theory that allows for each network in the data set to be generated by a different network formation model. Our analysis of the sampling problem as well as the proposed solutions are applied to rich network data of Banerjee, Chandrasekhar, Duflo, and Jackson(2013) from 75 villages in Karnataka, India. -
V1603419 Discriminant Analysis When the Initial
TECHNOMETRICSO, VOL. 16, NO. 3, AUGUST 1974 Discriminant Analysis When the Initial Samples are Misclassified II: Non-Random Misclassification Models Peter A. Lachenbruch Department of Biostatistics, University of North Carolina Chapel Hill, North Carolina Two models of non-random initial misclassifications are studied. In these models, observations which are closer to the mean of the “wrong” population have a greater chance of being misclassified than others. Sampling st,udies show that (a) the actual error rates of the rules from samples with initial misclassification are only slightly affected; (b) the apparent error rates, obtained by resubstituting the observations into the calculated discriminant function, are drastically affected, and cannot be used; and (c) the Mahalanobis 02 is greatly inflated. KEYWORDS misclassified. This clearly is unrealistic. The present Discriminant Analysis study attempts to present two other models which Robustness Non-Random Initial Misclassification it is hoped are more realistic than the “equal chance” model. To do this, we sacrifice some mathematical rigor, as the equations involved are 1. INTR~DUOTI~N very difficult to set down. Thus, Monte Carlo Lachenbruch (1966) studied the effects of mis- experiments are used to evaluat’e the behavior of classification of the initial samples on the per- the LDF. formance of the linear discriminant function (LDF). 2. hlODELSAND EXPERIMENTS More recently, Mclachlan (1972) developed the asymptotic results for this model and found gen- We assume that observations x, from the ith erally good agreement with the earlier results. It population, 7~; , i = 1, 2 are normally distributed was found that if the total proportion was not with mean p, and covariance matrix I.