Computational Statistics (Journal)1 CSF0019 Wolfgang Karl Hardle¨ Correspondence To: [email protected] C.A.S.E
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Applications of Computational Statistics with Multiple Regressions
International Journal of Computational and Applied Mathematics. ISSN 1819-4966 Volume 12, Number 3 (2017), pp. 923-934 © Research India Publications http://www.ripublication.com Applications of Computational Statistics with Multiple Regressions S MD Riyaz Ahmed Research Scholar, Department of Mathematics, Rayalaseema University, A.P, India Abstract Applications of Computational Statistics with Multiple Regression through the use of Excel, MATLAB and SPSS. It has been widely used for a long time in various fields, such as operations research and analysis, automatic control, probability statistics, statistics, digital signal processing, dynamic system mutilation, etc. The regress function command in MATLAB toolbox provides a helpful tool for multiple linear regression analysis and model checking. Multiple regression analysis is additionally extremely helpful in experimental scenario wherever the experimenter will management the predictor variables. Keywords - Multiple Linear Regression, Excel, MATLAB, SPSS, least square, Matrix notations 1. INTRODUCTION Multiple linear regressions are one of the foremost wide used of all applied mathematics strategies. Multiple regression analysis is additionally extremely helpful in experimental scenario wherever the experimenter will management the predictor variables [1]. A single variable quantity within the model would have provided an inadequate description since a number of key variables have an effect on the response variable in necessary and distinctive ways that [2]. It makes an attempt to model the link between two or more variables and a response variable by fitting a equation to determined information. Each value of the independent variable x is related to a value of the dependent variable y. The population regression line for p informative variables 924 S MD Riyaz Ahmed x1, x 2 , x 3 ...... -
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. -
Isye 6416: Computational Statistics Lecture 1: Introduction
ISyE 6416: Computational Statistics Lecture 1: Introduction Prof. Yao Xie H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology What this course is about I Interface between statistics and computer science I Closely related to data mining, machine learning and data analytics I Aim at the design of algorithm for implementing statistical methods on computers I computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models. Statistics data: images, video, audio, text, etc. sensor networks, social networks, internet, genome. statistics provide tools to I model data e.g. distributions, Gaussian mixture models, hidden Markov models I formulate problems or ask questions e.g. maximum likelihood, Bayesian methods, point estimators, hypothesis tests, how to design experiments Computing I how do we solve these problems? I computing: find efficient algorithms to solve them e.g. maximum likelihood requires finding maximum of a cost function I \Before there were computers, there were algorithms. But now that there are computers, there are even more algorithms, and algorithms lie at the heart of computing. " I an algorithm: a tool for solving a well-specified computational problem I examples: I The Internet enables people all around the world to quickly access and retrieve large amounts of information. With the aid of clever algorithms, sites on the Internet are able to manage and manipulate this large volume of data. I The Human Genome Project has made great progress toward the goals of identifying all the 100,000 genes in human DNA, determining the sequences of the 3 billion chemical base pairs that make up human DNA, storing this information in databases, and developing tools for data analysis. -
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. -
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. -
Factor Dynamic Analysis with STATA
Dynamic Factor Analysis with STATA Alessandro Federici Department of Economic Sciences University of Rome La Sapienza [email protected] Abstract The aim of the paper is to develop a procedure able to implement Dynamic Factor Analysis (DFA henceforth) in STATA. DFA is a statistical multiway analysis technique1, where quantitative “units x variables x times” arrays are considered: X(I,J,T) = {xijt}, i=1…I, j=1…J, t=1…T, where i is the unit, j the variable and t the time. Broadly speaking, this kind of methodology manages to combine, from a descriptive point of view (not probabilistic), the cross-section analysis through Principal Component Analysis (PCA henceforth) and the time series dimension of data through linear regression model. The paper is organized as follows: firstly, a theoretical overview of the methodology will be provided in order to show the statistical and econometric framework that the procedure of STATA will implement. The DFA framework has been introduced and developed by Coppi and Zannella (1978), and then re-examined by Coppi et al. (1986) and Corazziari (1997): in this paper the original approach will be followed. The goal of the methodology is to decompose the variance and covariance matrix S relative to X(IT,J), where the role of the units is played by the pair “units-times”. The matrix S, concerning the JxT observations over the I units, may be decomposed into the sum of three distinct variance and covariance matrices: S = *SI + *ST + SIT, (1) where: 1 That is a methodology where three or more indexes are simultaneously analysed. -
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 -
The Stata Journal Publishes Reviewed Papers Together with Shorter Notes Or Comments, Regular Columns, Book Reviews, and Other Material of Interest to Stata Users
Editors H. Joseph Newton Nicholas J. Cox Department of Statistics Department of Geography Texas A&M University Durham University College Station, Texas Durham, UK [email protected] [email protected] Associate Editors Christopher F. Baum, Boston College Frauke Kreuter, Univ. of Maryland–College Park Nathaniel Beck, New York University Peter A. Lachenbruch, Oregon State University Rino Bellocco, Karolinska Institutet, Sweden, and Jens Lauritsen, Odense University Hospital University of Milano-Bicocca, Italy Stanley Lemeshow, Ohio State University Maarten L. Buis, University of Konstanz, Germany J. Scott Long, Indiana University A. Colin Cameron, University of California–Davis Roger Newson, Imperial College, London Mario A. Cleves, University of Arkansas for Austin Nichols, Urban Institute, Washington DC Medical Sciences Marcello Pagano, Harvard School of Public Health William D. Dupont , Vanderbilt University Sophia Rabe-Hesketh, Univ. of California–Berkeley Philip Ender , University of California–Los Angeles J. Patrick Royston, MRC Clinical Trials Unit, David Epstein, Columbia University London Allan Gregory, Queen’s University Philip Ryan, University of Adelaide James Hardin, University of South Carolina Mark E. Schaffer, Heriot-Watt Univ., Edinburgh Ben Jann, University of Bern, Switzerland Jeroen Weesie, Utrecht University Stephen Jenkins, London School of Economics and Ian White, MRC Biostatistics Unit, Cambridge Political Science Nicholas J. G. Winter, University of Virginia Ulrich Kohler , University of Potsdam, Germany Jeffrey Wooldridge, Michigan State University Stata Press Editorial Manager Stata Press Copy Editors Lisa Gilmore David Culwell, Shelbi Seiner, and Deirdre Skaggs The Stata Journal publishes reviewed papers together with shorter notes or comments, regular columns, book reviews, and other material of interest to Stata users. -
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 -
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.