Introducing SPM® Infrastructure
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Curriculum Vitae
CURRICULUM VITAE Name Ankit Patras Address 111 Agricultural and Biotechnology Building, Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville TN 37209 Phone 615-963-6007, 615-963-6019/6018 Email [email protected], [email protected] EDUCATION 2005- 2009: Ph.D. Biosystems Engineering: School of Biosystems Engineering, College of Engineering & Architecture, Institute of Food and Health, University College Dublin, Ireland. 2005- 2006: Post-graduate certificate (Statistics & Computing): Department of Statistics and Actuarial Science, School of Mathematical Sciences, University College Dublin, Ireland 2003- 2004: Master of Science (Bioprocess Technology): UCD School of Biosystems Engineering, College of Engineering & Architecture, University College Dublin, Ireland 1998- 2002: Bachelor of Technology (Agricultural and Food Engineering): Allahabad Agriculture Institute, India ACADEMIC POSITIONS Assistant Professor, Food Biosciences: Department of Agricultural and Environmental Research, College of Agriculture, Human and Natural Sciences, Tennessee State University, Nashville, Tennessee 2nd Jan, 2014 - Present • Leading a team of scientist and graduate students in developing a world-class food research centre addressing current issues in human health, food safety specially virus, bacterial and mycotoxins contamination • Developing a world-class research program on improving safety of foods and pharmaceuticals • Develop cutting edge technologies (i.e. optical technologies, bioplasma, power Ultrasound, -
An Evaluation of Statistical Software for Research and Instruction
Behavior Research Methods, Instruments, & Computers 1985, 17(2),352-358 An evaluation of statistical software for research and instruction DARRELL L. BUTLER Ball State University, Muncie, Indiana and DOUGLAS B. EAMON University of Houston-Clear Lake, Houston, Texas A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruc tion and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages. Many psychologists use statistical programs to analyze ware packages available on the VAX computers at Ball research data or to teach statistics, and many are interested State University: BMDP, SPSSx, SCSS, and MINITAB. in using computer software for these purposes (e.g., Versions of these programs are widely available and are Butler, 1984; Butler & Kring, 1984). The present paper used here as points ofreference to judge the strengths and provides some program descriptions, benchmarks, and weaknesses of the microcomputer packages. Although evaluations of a wide variety of marketed statistical pro there are many programs distributed by individuals (e.g., grams that may be useful in research and/or instruction. see Academic Computer Center of Gettysburg College, This review differs in several respects from other re 1984, and Eamon, 1983), none are included in the present cent reviews of statistical programs (e. g., Carpenter, review. -
Full-Text (PDF)
Vol. 13(6), pp. 153-162, June 2019 DOI: 10.5897/AJPS2019.1785 Article Number: E69234960993 ISSN 1996-0824 Copyright © 2019 Author(s) retain the copyright of this article African Journal of Plant Science http://www.academicjournals.org/AJPS Full Length Research Paper Adaptability and yield stability of bread wheat (Triticum aestivum) varieties studied using GGE-biplot analysis in the highland environments of South-western Ethiopia Leta Tulu1* and Addishiwot Wondimu2 1National Agricultural Biotechnology Research Centre, P. O. Box 249, Holeta, Ethiopia. 2Department of Plant Sciences, College of Agriculture and Veterinary Science, Ambo University. P. O. Box 19, Ambo, Ethiopia. Received 13 February, 2019; Accepted 11 April, 2019 The objectives of this study were to evaluate released Ethiopian bread wheat varieties for yield stability using the GGE biplot method and identify well adapted and high-yielding genotypes for the highland environments of South-western Ethiopia. Twenty five varieties were evaluated in a randomized complete block design with three replications at Dedo and Gomma during the main cropping season of 2016 and at Dedo, Bedelle, Gomma and Manna during the main cropping season of 2017, generating a total of six environments in location-by-year combinations. Combined analyses of variance for grain yield indicated highly significant (p<0.001) mean squares due to environments, genotypes and genotype-by- environment interaction. Yield data were also analyzed using the GGE (that is, G, genotype + GEI, genotype-by-environment interaction) biplot method. Environment explained 73.2% of the total sum of squares, and genotype and genotype X environment interaction explained 7.16 and 15.8%, correspondingly. -
Multivariate Data Analysis in Sensory and Consumer Science
MULTIVARIATE DATA ANALYSIS IN SENSORY AND CONSUMER SCIENCE Garmt B. Dijksterhuis, Ph. D. ID-DLO, Institute for Animal Science and Health Food Science Department Lely stad The Netherlands FOOD & NUTRITION PRESS, INC. TRUMBULL, CONNECTICUT 06611 USA MULTIVARIATE DATA ANALYSIS IN SENSORY AND CONSUMER SCIENCE MULTIVARIATE DATA ANALYSIS IN SENSORY AND CONSUMER SCIENCE F NP PUBLICATIONS FOOD SCIENCE AND NUTRITIONIN Books MULTIVARIATE DATA ANALYSIS, G.B. Dijksterhuis NUTRACEUTICALS: DESIGNER FOODS 111, P.A. Lachance DESCRIPTIVE SENSORY ANALYSIS IN PRACTICE, M.C. Gacula, Jr. APPETITE FOR LIFE: AN AUTOBIOGRAPHY, S.A. Goldblith HACCP: MICROBIOLOGICAL SAFETY OF MEAT, J.J. Sheridan er al. OF MICROBES AND MOLECULES: FOOD TECHNOLOGY AT M.I.T., S.A. Goldblith MEAT PRESERVATION, R.G. Cassens S.C. PRESCOlT, PIONEER FOOD TECHNOLOGIST, S.A. Goldblith FOOD CONCEPTS AND PRODUCTS: JUST-IN-TIME DEVELOPMENT, H.R.Moskowitz MICROWAVE FOODS: NEW PRODUCT DEVELOPMENT, R.V. Decareau DESIGN AND ANALYSIS OF SENSORY OPTIMIZATION, M.C. Gacula, Jr. NUTRIENT ADDITIONS TO FOOD, J.C. Bauernfeind and P.A. Lachance NITRITE-CURED MEAT, R.G. Cassens POTENTIAL FOR NUTRITIONAL MODULATION OF AGING, D.K. Ingram ef al. CONTROLLEDlMODIFIED ATMOSPHERENACUUM PACKAGING, A. L. Brody NUTRITIONAL STATUS ASSESSMENT OF THE INDIVIDUAL, G.E. Livingston QUALITY ASSURANCE OF FOODS, J.E. Stauffer SCIENCE OF MEAT & MEAT PRODUCTS, 3RD ED., J.F. Price and B.S. Schweigert HANDBOOK OF FOOD COLORANT PATENTS, F.J. Francis ROLE OF CHEMISTRY IN PROCESSED FOODS, O.R. Fennema et al. NEW DIRECTIONS FOR PRODUCT TESTING OF FOODS, H.R. Moskowitz ENVIRONMENTAL ASPECTS OF CANCER: ROLE OF FOODS, E.L. Wynder et al. -
Relevance of Eating Pattern for Selection of Growing Pigs
RELEVANCE OF EATING PATTERN FOR SELECTION OF GROWING PIGS CENTRALE LANDB OUW CA TA LO GU S 0000 0489 0824 Promotoren: Dr. ir. E.W. Brascamp Hoogleraar in de Veefokkerij Dr. ir. M.W.A. Verstegen Buitengewoon hoogleraar op het vakgebied van de Veevoeding, in het bijzonder de voeding van de eenmagigen L.C.M, de Haer RELEVANCE OFEATIN G PATTERN FOR SELECTION OFGROWIN G PIGS Proefschrift ter verkrijging van de graad van doctor in de landbouw- en milieuwetenschappen, op gezag van de rector magnificus, dr. H.C. van der Plas, in het openbaar te verdedigen op maandag 13 april 1992 des namiddags te vier uur in de aula van de Landbouwuniversiteit te Wageningen m st/eic/, u<*p' Cover design: L.J.A. de Haer BIBLIOTHEEK! LANDBOUWUNIVERSIIBtt WAGENINGEM De Haer, L.C.M., 1992. Relevance of eating pattern for selection of growing pigs (Belang van het voeropnamepatroon voor de selektie van groeiende varkens). In this thesis investigations were directed at the consequences of testing future breeding pigs in group housing, with individual feed intake recording. Subjects to be addressed were: the effect of housing system on feed intake pattern and performance, relationships between feed intake pattern and performance and genetic aspects of the feed intake pattern. Housing system significantly influenced feed intake pattern, digestibility of feed, growth rate and feed conversion. Through effects on level of activity and digestibility, frequency of eating and daily eating time were negatively related with efficiency of production. Meal size and rate of feed intake were positively related with growth rate and backfat thickness. -
Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source -
General Linear Models - Part II
Introduction to General and Generalized Linear Models General Linear Models - part II Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 1 / 32 Today Test for model reduction Type I/III SSQ Collinearity Inference on individual parameters Confidence intervals Prediction intervals Residual analysis Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 2 / 32 Tests for model reduction Assume that a rather comprehensive model (a sufficient model) H1 has been formulated. Initial investigation has demonstrated that at least some of the terms in the model are needed to explain the variation in the response. The next step is to investigate whether the model may be reduced to a simpler model (corresponding to a smaller subspace),. That is we need to test whether all the terms are necessary. Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 3 / 32 Successive testing, type I partition Sometimes the practical problem to be solved by itself suggests a chain of hypothesis, one being a sub-hypothesis of the other. In other cases, the statistician will establish the chain using the general rule that more complicated terms (e.g. interactions) should be removed before simpler terms. In the case of a classical GLM, such a chain of hypotheses n corresponds to a sequence of linear parameter-spaces, Ωi ⊂ R , one being a subspace of the other. n R ⊆ ΩM ⊂ ::: ⊂ Ω2 ⊂ Ω1 ⊂ R ; where Hi : µ 2 -
Statistical Software
Statistical Software A. Grant Schissler1;2;∗ Hung Nguyen1;3 Tin Nguyen1;3 Juli Petereit1;4 Vincent Gardeux5 Keywords: statistical software, open source, Big Data, visualization, parallel computing, machine learning, Bayesian modeling Abstract Abstract: This article discusses selected statistical software, aiming to help readers find the right tool for their needs. We categorize software into three classes: Statisti- cal Packages, Analysis Packages with Statistical Libraries, and General Programming Languages with Statistical Libraries. Statistical and analysis packages often provide interactive, easy-to-use workflows while general programming languages are built for speed and optimization. We emphasize each software's defining characteristics and discuss trends in popularity. The concluding sections muse on the future of statistical software including the impact of Big Data and the advantages of open-source languages. This article discusses selected statistical software, aiming to help readers find the right tool for their needs (not provide an exhaustive list). Also, we acknowledge our experiences bias the discussion towards software employed in scholarly work. Throughout, we emphasize the software's capacity to analyze large, complex data sets (\Big Data"). The concluding sections muse on the future of statistical software. To aid in the discussion, we classify software into three groups: (1) Statistical Packages, (2) Analysis Packages with Statistical Libraries, and (3) General Programming Languages with Statistical Libraries. This structure -
STAT 3304/5304 Introduction to Statistical Computing
STAT 3304/5304 Introduction to Statistical Computing Statistical Packages Some Statistical Packages • BMDP • GLIM • HIL • JMP • LISREL • MATLAB • MINITAB 1 Some Statistical Packages • R • S-PLUS • SAS • SPSS • STATA • STATISTICA • STATXACT • . and many more 2 BMDP • BMDP is a comprehensive library of statistical routines from simple data description to advanced multivariate analysis, and is backed by extensive documentation. • Each individual BMDP sub-program is based on the most competitive algorithms available and has been rigorously field-tested. • BMDP has been known for the quality of it’s programs such as Survival Analysis, Logistic Regression, Time Series, ANOVA and many more. • The BMDP vendor was purchased by SPSS Inc. of Chicago in 1995. SPSS Inc. has stopped all develop- ment work on BMDP, choosing to incorporate some of its capabilities into other products, primarily SY- STAT, instead of providing further updates to the BMDP product. • BMDP is now developed by Statistical Solutions and the latest version (BMDP 2009) features a new mod- ern user-interface with all the statistical functionality of the classic program, running in the latest MS Win- dows environments. 3 LISREL • LISREL is software for confirmatory factor analysis and structural equation modeling. • LISREL is particularly designed to accommodate models that include latent variables, measurement errors in both dependent and independent variables, reciprocal causation, simultaneity, and interdependence. • Vendor information: Scientific Software International http://www.ssicentral.com/ 4 MATLAB • Matlab is an interactive, matrix-based language for technical computing, which allows easy implementation of statistical algorithms and numerical simulations. • Highlights of Matlab include the number of toolboxes (collections of programs to address specific sets of problems) available. -
Software Packages – Overview There Are Many Software Packages Available for Performing Statistical Analyses. the Ones Highlig
APPENDIX C SOFTWARE OPTIONS Software Packages – Overview There are many software packages available for performing statistical analyses. The ones highlighted here are selected because they provide comprehensive statistical design and analysis tools and a user friendly interface. SAS SAS is arguably one of the most comprehensive packages in terms of statistical analyses. However, the individual license cost is expensive. Primarily, SAS is a macro based scripting language, making it challenging for new users. However, it provides a comprehensive analysis ranging from the simple t-test to Generalized Linear mixed models. SAS also provides limited Design of Experiments capability. SAS supports factorial designs and optimal designs. However, it does not handle design constraints well. SAS also has a GUI available: SAS Enterprise, however, it has limited capability. SAS is a good program to purchase if your organization conducts advanced statistical analyses or works with very large datasets. R R, similar to SAS, provides a comprehensive analysis toolbox. R is an open source software package that has been approved for use on some DoD computer (contact AFFTC for details). Because R is open source its price is very attractive (free). However, the help guides are not user friendly. R is primarily a scripting software with notation similar to MatLab. R’s GUI: R Commander provides limited DOE capabilities. R is a good language to download if you can get it approved for use and you have analysts with a strong programming background. MatLab w/ Statistical Toolbox MatLab has a statistical toolbox that provides a wide variety of statistical analyses. The analyses range from simple hypotheses test to complex models and the ability to use likelihood maximization. -
On Reproducible Econometric Research
On Reproducible Econometric Research Roger Koenkera Achim Zeileisb∗ a Department of Economics, University of Illinois at Urbana-Champaign, United States of America b Department of Statistics and Mathematics, WU Wirtschaftsuniversit¨at Wien, Austria SUMMARY Recent software developments are reviewed from the vantage point of reproducible econo- metric research. We argue that the emergence of new tools, particularly in the open-source community, have greatly eased the burden of documenting and archiving both empirical and simulation work in econometrics. Some of these tools are highlighted in the discussion of two small replication exercises. Keywords: reproducibility, replication, software, literate programming. 1. INTRODUCTION The renowned dispute between Gauss and Legendre over priority for the invention of the method of least squares might have been resolved by Stigler(1981). A calculation of the earth's ellipticity reported by Gauss in 1799 alluded to the use of meine Methode; had Stigler been able to show that Gauss's estimate was consistent with the least squares solution using the four observations available to Gauss, his claim that he had been using the method since 1795 would have been strongly vindicated. Unfortunately, no such computation could be reproduced leaving the dispute in that limbo all too familiar in the computational sciences. The question that we would like to address here is this: 200 years after Gauss, can we do better? What can be done to improve our ability to reproduce computational results in econometrics and related fields? Our main contention is that recent software developments, notably in the open- source community, make it much easier to achieve and distribute reproducible research. -
Measuring the Outcome of Psychiatric Care Using Clinical Case Notes
MEASURING THE OUTCOME OF PSYCHIATRIC CARE USING CLINICAL CASE NOTES Marco Akerman MD, MSc A report submitted as part of the requirements for the degree of Doctor of Philosophy in the Faculty of Medicine, University of London Department of Epidemiology and Public Health University College London 1993 ProQuest Number: 10016721 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. uest. ProQuest 10016721 Published by ProQuest LLC(2016). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. Microform Edition © ProQuest LLC. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 ABSTRACT This thesis addresses the question: ‘Can case notes be used to measure the outcome of psychiatric treatment?’ Patient’s case notes from three psychiatric units (two in-patient and one day hospital) serving Camden, London (N=225) and two units (one in-patient and one day- hospital) in Manchester (N=34) were used to assess outcome information of psychiatric care in tenns of availability (are comparable data present for both admission and discharge?), reliability (can this data be reliably extracted?), and validity (are the data true measures?). Differences in outcome between units and between diagnostic groups were considered in order to explore the possibility of auditing the outcome of routine psychiatric treatment using case notes.