Introduction À SAS
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Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi. -
Porta-SIMD: an Optimally Portable SIMD Programming Language Duke CS-1990-12 UNC CS TR90-021 May 1990
Porta-SIMD: An Optimally Portable SIMD Programming Language Duke CS-1990-12 UNC CS TR90-021 May 1990 Russ Tuck Duke University Deparment of Computer Science Durham, NC 27706 The University of North Carolina at Chapel Hill Department of Computer Science CB#3175, Sitterson Hall Chapel Hill, NC 27599-3175 Text (without appendix) of a Ph.D. dissertation submitted to Duke University. The research was performed at UNC. @ 1990 Russell R. Tuck, III UNC is an Equal Opportunity/Atlirmative Action Institution. PORTA-SIMD: AN OPTIMALLY PORTABLE SIMD PROGRAMMING LANGUAGE by Russell Raymond Tuck, III Department of Computer Science Duke University Dissertation submitte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science in the Graduate School of Duke University 1990 Copyright © 1990 by Russell Raymond Tuck, III All rights reserved Abstract Existing programming languages contain architectural assumptions which limit their porta bility. I submit optimal portability, a new concept which solves this language design problem. Optimal portability makes it possible to design languages which are portable across vari ous sets of diverse architectures. SIMD (Single-Instruction stream, Multiple-Data stream) computers represent an important and very diverse set of architectures for which to demon strate optimal portability. Porta-SIMD (pronounced "porta.-simm'd") is the first optimally portable language for SIMD computers. It was designed and implemented to demonstrate that optimal portability is a useful and achievable standard for language design. An optimally portable language allows each program to specify the architectural features it requires. The language then enables the compiled program to exploit exactly those fea. tures, and to run on all architectures that provide them. -
United States District Court Eastern District of Texas Marshall Division
Case 2:18-cv-00295-JRG Document 1 Filed 07/18/18 Page 1 of 65 PageID #: 1 UNITED STATES DISTRICT COURT EASTERN DISTRICT OF TEXAS MARSHALL DIVISION SAS INSTITUTE INC., Plaintiff, Civil Action No. _________________ vs. Jury Trial Demanded WORLD PROGRAMMING LIMITED, MINEQUEST BUSINESS ANALYTICS, LLC, MINEQUEST LLC, ANGOSS SOFTWARE CORPORATION, LUMINEX SOFTWARE, INC., YUM! BRANDS, INC., PIZZA HUT, INC., SHAW INDUSTRIES GROUP, INC., and HITACHI VANTARA CORPORATION, Defendants. ORIGINAL COMPLAINT Plaintiff SAS Institute Inc. (“SAS”) makes the following allegations against Defendants World Programming Limited (“WPL”), MineQuest Business Analytics, LLC and MineQuest, LLC (collectively, “MineQuest”), Angoss Software Corporation (“Angoss”), Luminex Software, Inc. (“Luminex”), Yum! Brands, Inc. (“Yum”), Pizza Hut, Inc. (“Pizza Hut”), Shaw Industries Group, Inc. (“Shaw”), and Hitachi Vantara Corporation (“Hitachi”), (collectively “Defendants”). SAS alleges that all Defendants are liable to SAS for copyright infringement of the SAS System and SAS Manuals, described below. SAS alleges that WPL, MineQuest, Angoss, and Luminex are liable to SAS for contributory and vicarious copyright infringement of the SAS System and SAS Manuals. SAS alleges that Defendants WPL, Angoss, Yum, and Pizza Hut are liable for infringement of U.S. Patent Nos. 7,170,519 (“the ’519 Patent”), 7,447,686 (“the ’686 Patent”), and 8,498,996 (“the ’996 Patent”) (collectively, the “Patents-in-Suit”). Case 2:18-cv-00295-JRG Document 1 Filed 07/18/18 Page 2 of 65 PageID #: 2 THE NATURE OF THE ACTION 1. This is an action for (a) copyright infringement arising out of Defendants’ willful infringement of various copyrighted SAS materials, and (b) the willful infringement of SAS’s Patents-in-Suit by WPL, Angoss, Yum, and Pizza Hut. -
R G M B.Ec. M.Sc. Ph.D
Rolando Kristiansand, Norway www.bayesgroup.org Gonzales Martinez +47 41224721 [email protected] JOBS & AWARDS M.Sc. B.Ec. Applied Ph.D. Economics candidate Statistics Jobs Events Awards Del Valle University University of Alcalá Universitetet i Agder 2018 Bolivia, 2000-2004 Spain, 2009-2010 Norway, 2018- 1/2018 Ph.D scholarship 8/2017 International Consultant, OPHI OTHER EDUCATION First prize, International Young 12/2016 J-PAL Course on Evaluating Social Programs Macro-prudential Policies Statistician Prize, IAOS Massachusetts Institute of Technology - MIT International Monetary Fund (IMF) 5/2016 Deputy manager, BCP bank Systematic Literature Reviews and Meta-analysis Theorizing and Theory Building Campbell foundation - Vrije Universiteit Halmstad University (HH, Sweden) 1/2016 Consultant, UNFPA Bayesian Modeling, Inference and Prediction Multidimensional Poverty Analysis University of Reading OPHI, Oxford University Researcher, data analyst & SKILLS AND TECHNOLOGIES 1/2015 project coordinator, PEP Academic Research & publishing Financial & Risk analysis 12 years of experience making economic/nancial analysis 3/2014 Director, BayesGroup.org and building mathemati- Consulting cal/statistical/econometric models for the government, private organizations and non- prot NGOs Research & project Mathematical 1/2013 modeling coordinator, UNFPA LANGUAGES 04/2012 First award, BCG competition Spanish (native) Statistical analysis English (TOEFL: 106) 9/2011 Financial Economist, UDAPE MatLab Macro-economic R, RStudio 11/2010 Currently, I work mainly analyst, MEFP Stata, SPSS with MatLab and R . SQL, SAS When needed, I use 3/2010 First award, BCB competition Eviews Stata, SAS or Eviews. I started working in LaTex, WinBugs Python recently. 8/2009 M.Sc. scholarship Python, Spyder RECENT PUBLICATIONS Disjunction between universality and targeting of social policies: Demographic opportunities for depolarized de- velopment in Paraguay. -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Kwame Nkrumah University of Science and Technology, Kumasi
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA Assessing the Social Impacts of Illegal Gold Mining Activities at Dunkwa-On-Offin by Judith Selassie Garr (B.A, Social Science) A Thesis submitted to the Department of Building Technology, College of Art and Built Environment in partial fulfilment of the requirement for a degree of MASTER OF SCIENCE NOVEMBER, 2018 DECLARATION I hereby declare that this work is the result of my own original research and this thesis has neither in whole nor in part been prescribed by another degree elsewhere. References to other people’s work have been duly cited. STUDENT: JUDITH S. GARR (PG1150417) Signature: ........................................................... Date: .................................................................. Certified by SUPERVISOR: PROF. EDWARD BADU Signature: ........................................................... Date: ................................................................... Certified by THE HEAD OF DEPARTMENT: PROF. B. K. BAIDEN Signature: ........................................................... Date: ................................................................... i ABSTRACT Mining activities are undertaken in many parts of the world where mineral deposits are found. In developing nations such as Ghana, the activity is done both legally and illegally, often with very little or no supervision, hence much damage is done to the water bodies where the activities are carried out. This study sought to assess the social impacts of illegal gold mining activities at Dunkwa-On-Offin, the capital town of Upper Denkyira East Municipality in the Central Region of Ghana. The main objectives of the research are to identify factors that trigger illegal mining; to identify social effects of illegal gold mining activities on inhabitants of Dunkwa-on-Offin; and to suggest effective ways in curbing illegal mining activities. Based on the approach to data collection, this study adopts both the quantitative and qualitative approach. -
Pnw 2020 Strunk001.Pdf
Remote Sensing of Environment 237 (2020) 111535 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse Evaluation of pushbroom DAP relative to frame camera DAP and lidar for forest modeling T ∗ Jacob L. Strunka, , Peter J. Gouldb, Petteri Packalenc, Demetrios Gatziolisd, Danuta Greblowskae, Caleb Makif, Robert J. McGaugheyg a USDA Forest Service Pacific Northwest Research Station, 3625 93rd Ave SW, Olympia, WA, 98512, USA b Washington State Department of Natural Resources, PO Box 47000, 1111 Washington Street, SE, Olympia, WA, 98504-7000, USA c School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland d USDA Forest Service Pacific Northwest Research Station, 620 Southwest Main, Suite 502, Portland, OR, 97205, USA e GeoTerra Inc., 60 McKinley St, Eugene, OR, 97402, USA f Washington State Department of Natural Resources, PO Box 47000, 1111 Washington Street SE, Olympia, WA, 98504-7000, USA g USDA Forest Service Pacific Northwest Research Station, University of Washington, PO Box 352100, Seattle, WA, 98195-2100, USA ARTICLE INFO ABSTRACT Keywords: There is growing interest in using Digital Aerial Photogrammetry (DAP) for forestry applications. However, the Lidar performance of pushbroom DAP relative to frame-based DAP and airborne lidar is not well documented. Interest Structure from motion in DAP stems largely from its low cost relative to lidar. Studies have demonstrated that frame-based DAP Photogrammetry generally performs slightly poorer than lidar, but still provides good value due to its reduced cost. In the USA Forestry pushbroom imagery can be dramatically less expensive than frame-camera imagery in part because of a na- DAP tionwide collection program. -
End-User Probabilistic Programming
End-User Probabilistic Programming Judith Borghouts, Andrew D. Gordon, Advait Sarkar, and Neil Toronto Microsoft Research Abstract. Probabilistic programming aims to help users make deci- sions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics. We consider probabilistic programming for end-users, in particular spread- sheet users, estimated to number in tens to hundreds of millions. We examine the sources of uncertainty actually encountered by spreadsheet users, and their coping mechanisms, via an interview study. We examine spreadsheet-based interfaces and technology to help reason under uncer- tainty, via probabilistic and other means. We show how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclu- sions about the promise and limitations of probabilistic programming for end-users. 1 Introduction In this paper, we discuss the potential of bringing together two rather distinct approaches to decision making under uncertainty: spreadsheets and probabilistic programming. We start by introducing these two approaches. 1.1 Background: Spreadsheets and End-User Programming The spreadsheet is the first \killer app" of the personal computer era, starting in 1979 with Dan Bricklin and Bob Frankston's VisiCalc for the Apple II [15]. The primary interface of a spreadsheet|then and now, four decades later|is the grid, a two-dimensional array of cells. Each cell may hold a literal data value, or a formula that computes a data value (and may depend on the data in other cells, which may themselves be computed by formulas). -
Admb Package
Using AD Model Builder and R together: getting started with the R2admb package Ben Bolker March 9, 2020 1 Introduction AD Model Builder (ADMB: http://admb-project.org) is a standalone program, developed by Dave Fournier continuously since the 1980s and re- leased as an open source project in 2007, that takes as input an objective function (typically a negative log-likelihood function) and outputs the co- efficients that minimize the objective function, along with various auxiliary information. AD Model Builder uses automatic differentiation (that's what \AD" stands for), a powerful algorithm for computing the derivatives of a specified objective function efficiently and without the typical errors due to finite differencing. Because of this algorithm, and because the objective function is compiled into machine code before optimization, ADMB can solve large, difficult likelihood problems efficiently. ADMB also has the capability to fit random-effects models (typically via Laplace approximation). To the average R user, however, ADMB represents a challenge. The first (unavoidable) challenge is that the objective function needs to be written in a superset of C++; the second is learning the particular sequence of steps that need to be followed in order to output data in a suitable format for ADMB; compile and run the ADMB model; and read the data into R for analysis. The R2admb package aims to eliminate the second challenge by automating the R{ADMB interface as much as possible. 2 Installation The R2admb package can be installed in R in the standard way (with install.packages() or via a Packages menu, depending on your platform. -
Automated Likelihood Based Inference for Stochastic Volatility Models H
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutional Knowledge at Singapore Management University Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Economics School of Economics 11-2009 Automated Likelihood Based Inference for Stochastic Volatility Models H. Skaug Jun YU Singapore Management University, [email protected] Follow this and additional works at: https://ink.library.smu.edu.sg/soe_research Part of the Econometrics Commons Citation Skaug, H. and YU, Jun. Automated Likelihood Based Inference for Stochastic Volatility Models. (2009). 1-28. Research Collection School Of Economics. Available at: https://ink.library.smu.edu.sg/soe_research/1151 This Working Paper is brought to you for free and open access by the School of Economics at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Economics by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]. Automated Likelihood Based Inference for Stochastic Volatility Models Hans J. SKAUG , Jun YU November 2009 Paper No. 15-2009 ANY OPINIONS EXPRESSED ARE THOSE OF THE AUTHOR(S) AND NOT NECESSARILY THOSE OF THE SCHOOL OF ECONOMICS, SMU Automated Likelihood Based Inference for Stochastic Volatility Models¤ Hans J. Skaug,y Jun Yuz October 7, 2008 Abstract: In this paper the Laplace approximation is used to perform classical and Bayesian analyses of univariate and multivariate stochastic volatility (SV) models. We show that imple- mentation of the Laplace approximation is greatly simpli¯ed by the use of a numerical technique known as automatic di®erentiation (AD). -
Used.Html#Sbm Mv
http://www.kdnuggets.com/2015/05/poll-analytics-data-mining-data-science-software- used.html#sbm_mv Vote in KDnuggets 16th Annual Poll: What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project?. We will clean and analyze the results and publish our trend analysis afterward What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project (not just evaluation)? 4 categories: Platforms, Languages, Deep Learning, Hadoop. Analytics / Data Mining Platforms / Suites: Actian Alpine Data Labs Alteryx Angoss Amazon Machine Learning Ayasdi BayesiaLab BigML Birst C4.5/C5.0/See5 Datameer Dataiku Dato (former Graphlab) Dell (including Statistica) FICO Model Builder Gnu Octave GoodData H2O (0xdata) IBM Cognos IBM SPSS Modeler IBM SPSS Statistics IBM Watson Analytics JMP KNIME Lavastorm Lexalytics MATLAB Mathematica Megaputer Polyanalyst/TextAnalyst MetaMind Microsoft Azure ML Microsoft SQL Server Microsoft Power BI MicroStrategy Miner3D Ontotext Oracle Data Miner Orange Pentaho Predixion Software QlikView RapidInsight/Veera RapidMiner Rattle Revolution Analytics (now part of Microsoft) SAP (including former KXEN) SAS Enterprise Miner Salford SPM/CART/Random Forests/MARS/TreeNet scikit-learn Skytree SiSense Splunk/ Hunk Stata TIBCO Spotfire Tableau Vowpal Wabbit Weka WordStat XLSTAT for Excel Zementis Other free analytics/data mining tools Other paid analytics/data mining/data science software Languages / low-level tools: C/C++ Clojure Excel F# Java Julia Lisp Perl Python R Ruby SAS base SQL Scala Unix shell/awk/gawk WPS: World Programming System Other programming languages Deep Learning tools Caffe Cuda-convnet Deeplearning4j Torch Theano Pylearn2 Other Deep Learning tools Hadoop-based Analytics-related tools Hadoop HBase Hive Pig Spark Mahout MLlib SQL on Hadoop tools Other Hadoop/HDFS-based tools. -
A Grid Algorithm for High Throughput Fitting of Dose-Response Curve Data Yuhong Wang*, Ajit Jadhav, Noel Southal, Ruili Huang and Dac-Trung Nguyen
Current Chemical Genomics, 2010, 4, 57-66 57 Open Access A Grid Algorithm for High Throughput Fitting of Dose-Response Curve Data Yuhong Wang*, Ajit Jadhav, Noel Southal, Ruili Huang and Dac-Trung Nguyen National Institutes of Health, NIH Chemical Genomics Center, 9800 Medical Center Drive, Rockville, MD 20850, USA Abstract: We describe a novel algorithm, Grid algorithm, and the corresponding computer program for high throughput fitting of dose-response curves that are described by the four-parameter symmetric logistic dose-response model. The Grid algorithm searches through all points in a grid of four dimensions (parameters) and finds the optimum one that corre- sponds to the best fit. Using simulated dose-response curves, we examined the Grid program’s performance in reproduc- ing the actual values that were used to generate the simulated data and compared it with the DRC package for the lan- guage and environment R and the XLfit add-in for Microsoft Excel. The Grid program was robust and consistently recov- ered the actual values for both complete and partial curves with or without noise. Both DRC and XLfit performed well on data without noise, but they were sensitive to and their performance degraded rapidly with increasing noise. The Grid program is automated and scalable to millions of dose-response curves, and it is able to process 100,000 dose-response curves from high throughput screening experiment per CPU hour. The Grid program has the potential of greatly increas- ing the productivity of large-scale dose-response data analysis and early drug discovery processes, and it is also applicable to many other curve fitting problems in chemical, biological, and medical sciences.