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Real-Time Wavelet Compression and Self-Modeling Curve
REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Guoxiang Chen March 2003 This dissertation entitled REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY BY GUOXIANG CHEN has been approved for the Department of Chemistry and Biochemistry and the College of Arts and Sciences by Peter de B. Harrington Associate Professor of Chemistry and Biochemistry Leslie A. Flemming Dean, College of Arts and Sciences CHEN, GUOXIANG. Ph.D. March 2003. Analytical Chemistry Real-Time Wavelet Compression and Self-Modeling Curve Resolution for Ion Mobility Spectrometry (203 pp.) Director of Dissertation: Peter de B. Harrington Chemometrics has proven useful for solving chemistry problems. Most of the chemometric methods are applied in post-run analyses, for which data are processed after being collected and archived. However, in many applications, real-time processing is required to obtain knowledge underlying complex chemical systems instantly. Moreover, real-time chemometrics can eliminate the storage burden for large amounts of raw data that occurs in post-run analyses. These attributes are important for the construction of portable intelligent instruments. Ion mobility spectrometry (IMS) furnishes inexpensive, sensitive, fast, and portable sensors that afford a wide variety of potential applications. SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) is a self-modeling curve resolution method that has been demonstrated as an effective tool for enhancing IMS measurements. However, all of the previously reported studies have applied SIMPLISMA as a post-run tool. -
Happy 80Th, Ulf!
From SIAM News, Volume 37, Number 7, September 2004 Happy 80th, Ulf! My Grenander Number is two, a distinction that goes all the way back to 1958. In 1951, I wrote a paper on an aspect of the Bergman kernel function with Henry O. Pollak (who later became head of mathematics at the Bell Telephone Laboratories). In 1958, Ulf Grenander, Henry Pollak, and David Slepian wrote on the distribution of quadratic forms in normal varieties. Through my friendship with Henry, I learned of Ulf’s existence, and when Ulf came to Brown in 1966, I felt that this friendship would be passed on to me. And so it turned out. I had already read and appreciated the second of Ulf’s many books: On Toeplitz Forms and Their Applications (1958), written jointly with Gabor Szegö. Over the years, my wife and I have lived a block away from Ulf and “Pi” Grenander. (Pi = π = 3.14159...) We have known their children and their grandchildren. And I shouldn’t forget their many dogs. We have enjoyed their hospitality on numerous occasions, including graduations, weddings, and Lucia Day (December 13). We have visited with them in their summer home in Vastervik, Sweden. We have sailed with them around the nearby islands in the Baltic. And yet, despite the fact that our offices are separated by only a few feet, despite the fact that I have often pumped him for mathematical information and received mathematical wisdom in return, my Grenander Number has never been reduced to one. On May 7, 2004, about fifty people gathered in Warwick, Rhode Island, for an all-day celebration, Ulf Grenander from breakfast through dinner, in honor of Ulf’s 80th birthday. -
Curriculum Vitae
Jeff Goldsmith 722 W 168th Street, 6th floor New York, NY 10032 jeff[email protected] Date of Preparation April 20, 2021 Academic Appointments / Work Experience 06/2018{Present Department of Biostatistics Mailman School of Public Health, Columbia University Associate Professor 06/2012{05/2018 Department of Biostatistics Mailman School of Public Health, Columbia University Assistant Professor 01/2009{12/2010 Department of Biostatistics Bloomberg School of Public Health, Johns Hopkins University Research Assistant (R01NS060910) 01/2008{12/2009 Department of Biostatistics Bloomberg School of Public Health, Johns Hopkins University Research Assistant (U19 AI060614 and U19 AI082637) Education 08/2007{05/2012 Johns Hopkins University PhD in Biostatistics, May 2012 Thesis: Statistical Methods for Cross-sectional and Longitudinal Functional Observations Advisors: Ciprian Crainiceanu and Brian Caffo 08/2003{05/2007 Dickinson College BS in Mathematics, May 2007 Jeff Goldsmith 2 Honors 04/2021 Dean's Excellence in Leadership Award 03/2021 COPSS Leadership Academy For Emerging Leaders in Statistics 06/2017 Tow Faculty Scholar 01/2016 Public Voices Fellow 10/2013 Calderone Junior Faculty Prize 05/2012 ASA Biometrics Section Travel Award 12/2011 Invited Paper in \Highlights of JCGS" Session at Interface 05/2011 Margaret Merrell Award for Outstanding Research by a Biostatistics Doc- toral Student 05/2011 School-wide Teaching Assistant Recognition Award 05/2011 Helen Abbey Award for Excellence in Teaching 03/2011 ENAR Distinguished Student Paper Award 05/2010 Jane and Steve Dykacz Award for Outstanding Paper in Medical Statistics 05/2009 Nominated for School-wide Teaching Assistant Recognition Award 08/2007{05/2012 Sommer Scholar 05/2007 James Fowler Rusling Prize 05/2007 Lance E. -
Actuarial Science and Financial Mathematics Faqs
Department of Mathematics 250 Mathematics Building 231 W. 18th Avenue Columbus, OH 43210-1174 Actuarial Science and Financial Mathematics FAQs 1. What is the difference between a major in Actuarial Science and a major in Financial Math? Actuarial Science is the mathematical and statistical underpinning of the design, financing, and operation of all types of insurance, pension plans and benefit plans. Actuarial science prepares students for a career in the actuarial side of the insurance business and in actuarial consulting. Please see http://www.beanactuary.org/about/ for more information. Financial math track provides students with a foundation of the mathematics used in financial markets. It focuses on the pricing of the financial instruments (futures, options and other derivatives) and portfolio selection. Though there are job opportunities in financial institutions for students with an undergraduate degree, students in this track are encouraged to continue with graduate study in financial math, financial engineering, computational finance, or quantitative finance (they essentially mean the same thing). There are many professional master degree programs in this area. 2. How many students are currently majoring in actuarial science at Ohio State? We have close to 350 students in our program. 3. Are there any requirements to my actuarial major? If so, what are they? We place students into a pre-major when they switch or add this major. The major prerequisites and requirements can be found at https://math.osu.edu/undergrad/current- majors/requirements/actuarial. Students will also need to complete the College of Arts and Sciences General Education requirements. Students should meet with their academic advisor in the Math Advising Office or review the requirements online, https://math.osu.edu/undergrad/current-majors/requirements. -
Recent Challenges in Actuarial Science
ST09CH01_Embrechts ARjats.cls July 30, 2021 15:51 Annual Review of Statistics and Its Application Recent Challenges in Actuarial Science Paul Embrechts and Mario V. Wüthrich RiskLab, Department of Mathematics, ETH Zurich, Zurich, Switzerland, CH-8092; email: [email protected], [email protected] Annu. Rev. Stat. Appl. 2022. 9:1.1–1.22 Keywords The Annual Review of Statistics and Its Application is actuarial science, generalized linear models, life and non-life insurance, online at statistics.annualreviews.org neural networks, risk management, telematics data https://doi.org/10.1146/annurev-statistics-040120- 030244 Abstract Copyright © 2022 by Annual Reviews. For centuries, mathematicians and, later, statisticians, have found natural All rights reserved research and employment opportunities in the realm of insurance. By defini- tion, insurance offers financial cover against unforeseen events that involve an important component of randomness, and consequently, probability the- ory and mathematical statistics enter insurance modeling in a fundamental way. In recent years, a data deluge, coupled with ever-advancing information technology and the birth of data science, has revolutionized or is about to revolutionize most areas of actuarial science as well as insurance practice. We discuss parts of this evolution and, in the case of non-life insurance, show how a combination of classical tools from statistics, such as generalized lin- ear models and, e.g., neural networks contribute to better understanding and analysis of actuarial data. We further review areas of actuarial science where the cross fertilization between stochastics and insurance holds promise for both sides. Of course, the vastness of the field of insurance limits our choice of topics; we mainly focus on topics closer to our main areas of research. -
Department of Statistics and Actuarial Science Three Year Academic Plan 2004-2006 Approved by Department on 27 June 2006
Department of Statistics and Actuarial Science Three Year Academic Plan 2004-2006 Approved by Department on 27 June 2006 Chronology of Department Planning A. Achievement of Previous and Evaluation: Objectives and Recommendations: • May 2001: Formation of Department • Nov 2003: First Three-Year Plan (2003-6) The Department has achieved most of the • Mar 2005: External Review of Department objectives set out in its previous three-year • Mar 2006: Senate Approval of Recommen- plan, and has made a solid start at dations implementing the recommendations stemming • Jun 2006: Second Three-Year Plan (2007- from the 2005 external review. 9) Full, point-by-point assessments are Since its inception, the Department has provided in Appendices A and B. Of particular grown from 9 faculty positions to 20. Of these note, are the following: positions, 7 come with external or endowed New positions: The three-year plan called for funding. All faculty members are active in several academic appointments. As planned, research, and their collective record of we were able to hire two additional actuaries, external awards and funding is very strong. but the other target areas (a theoretician and a Over the last few years, the Department has lecturer) were not addressed. Instead, we took emerged as a leading Canadian center for advantage of our recognizable strengths to statistical teaching and research. A recruit very strong applicants for externally comparative analysis of NSERC grant funded and endowed positions. funding conducted for the recent external The plan also called for further half-positions in review ranked the Department very close to the general office and in the Statistical the best in the country. -
Multivariate Chemometrics As a Strategy to Predict the Allergenic Nature of Food Proteins
S S symmetry Article Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins Miroslava Nedyalkova 1 and Vasil Simeonov 2,* 1 Department of Inorganic Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria; [email protected]fia.bg 2 Department of Analytical Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria * Correspondence: [email protected]fia.bg Received: 3 September 2020; Accepted: 21 September 2020; Published: 29 September 2020 Abstract: The purpose of the present study is to develop a simple method for the classification of food proteins with respect to their allerginicity. The methods applied to solve the problem are well-known multivariate statistical approaches (hierarchical and non-hierarchical cluster analysis, two-way clustering, principal components and factor analysis) being a substantial part of modern exploratory data analysis (chemometrics). The methods were applied to a data set consisting of 18 food proteins (allergenic and non-allergenic). The results obtained convincingly showed that a successful separation of the two types of food proteins could be easily achieved with the selection of simple and accessible physicochemical and structural descriptors. The results from the present study could be of significant importance for distinguishing allergenic from non-allergenic food proteins without engaging complicated software methods and resources. The present study corresponds entirely to the concept of the journal and of the Special issue for searching of advanced chemometric strategies in solving structural problems of biomolecules. Keywords: food proteins; allergenicity; multivariate statistics; structural and physicochemical descriptors; classification 1. -
Copula-Based Analysis of Dependent Data with Censoring and Zero Inflation
Copula-Based Analysis of Dependent Data with Censoring and Zero Inflation by Fuyuan Li B.S. in Telecommunication Engineering, May 2012, Beijing University of Technology M.S. in Statistics, May 2014, The George Washington University A Dissertation submitted to The Faculty of The Columbian College of Arts and Sciences of The George Washington University in partial satisfaction of the requirements for the degree of Doctor of Philosophy January 10, 2019 Dissertation directed by Huixia J. Wang Professor of Statistics The Columbian College of Arts and Sciences of The George Washington University certifies that Fuyuan Li has passed the Final Examination for the degree of Doctor of Philosophy as of December 7, 2018. This is the final and approved form of the dissertation. Copula-Based Analysis of Dependent Data with Censoring and Zero Inflation Fuyuan Li Dissertation Research Committee: Huixia J. Wang, Professor of Statistics, Dissertation Director Tapan K. Nayak, Professor of Statistics, Committee Member Reza Modarres, Professor of Statistics, Committee Member ii c Copyright 2019 by Fuyuan Li All rights reserved iii Acknowledgments This work would not have been possible without the financial support of the National Science Foundation grant DMS-1525692, and the King Abdullah University of Science and Technology office of Sponsored Research award OSR-2015-CRG4-2582. I am grateful to all of those with whom I have had the pleasure to work during this and other related projects. Each of the members of my Dissertation Committee has provided me extensive personal and professional guidance and taught me a great deal about both scientific research and life in general. -
A Pattern Theoretic Approach
The Modelling of Biological Growth: a Pattern Theoretic Approach Nataliya Portman, Postdoctoral fellow McConnell Brain Imaging Centre Montreal Neurological Institute McGill University June 21, 2011 The Modelling of Biological Growth: a Pattern Theoretic Approach 2/ 47 Dedication This research work is dedicated to my PhD co-supervisor Dr. Ulf Grenander, the founder of Pattern Theory Division of Applied Mathematics, Brown University Providence, Rhode Island, USA. http://www.dam.brown.edu/ptg/ The Modelling of Biological Growth: a Pattern Theoretic Approach 3/ 47 Outline 1 Mathematical foundations of computational anatomy 2 Growth models in computational anatomy 3 A link between anatomical models and the GRID model 4 GRID view of growth on a fine time scale 5 GRID equation of growth on a coarse time scale (macroscopic growth law) 6 Image inference of growth properties of the Drosophila wing disc 7 Summary, concluding remarks and future perspectives 8 Current work The Modelling of Biological Growth: a Pattern Theoretic Approach 4/ 47 Mathematical foundations of computational anatomy Mathematical foundations of computational anatomy Computational anatomy1 focuses on the precise study of the biological variability of brain anatomy. D'Arcy Thompson laid out the vision of this discipline in his treatise \On Growth and Form" 2. In 1917 he wrote \In a very large part of morphology, our essential task lies in the comparison of related forms rather than in the precise definition of each; and the deformation of a complicated figure may be a phenomenon easy of comprehension, though the figure itself may be left unanalyzed and undefined." D'Arcy Thompson introduced the Method of Coordinates to accomplish the process of comparison. -
Actuarial Studies Within the Biometry and Statistics and Statistical Science Majors
Actuarial Studies within the Biometry and Statistics and Statistical Science majors An actuary is a business professional who analyzes the financial consequences of risk. Actuaries use mathematics, statistics and financial theory to study uncertain future events, especially those of concern to insurance and pension programs. They evaluate the likelihood of those events, design creative ways to reduce the likelihood and decrease the impact of adverse events that actually do occur. To be more specific, actuaries improve financial decision making by developing models to evaluate the current financial implications of uncertain future events. Actuaries are an important part of the management team of the companies that employ them. Their work requires a combination of strong analytical skills, business knowledge and understanding of human behavior to design and manage programs that control risk. No matter the source, actuary is consistently rated as one of the best jobs in America. US News and World Report, the Jobs Rated Almanac, CNN Money, and others all agree: few other occupations offer the combination of benefits that an actuarial career can offer. In almost every job satisfaction category, such as work environment, employment outlook, job security, growth opportunity, and salary (especially starting salary), a career as an actuary is hard to beat. Required courses for the Biometry and Statistics and Statistical Science majors Calculus I and II: MATH 1110 & 1120 Multivariable Calculus and Linear Algebra: MATH 2210 & 2220 or 2230 & 2240 -
Principal Component Analysis
Tutorial n Chemometrics and Intelligent Laboratory Systems, 2 (1987) 37-52 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands Principal Component Analysis SVANTE WOLD * Research Group for Chemometrics, Institute of Chemistry, Umei University, S 901 87 Urned (Sweden) KIM ESBENSEN and PAUL GELADI Norwegian Computing Center, P.B. 335 Blindern, N 0314 Oslo 3 (Norway) and Research Group for Chemometrics, Institute of Chemistry, Umed University, S 901 87 Umeci (Sweden) CONTENTS Intr~uction: history of principal component analysis ...... ............... ....... 37 Problem definition for multivariate data ................ ............... ....... 38 A chemical example .............................. ............... ....... 40 Geometric interpretation of principal component analysis ... ............... ....... 41 Majestical defi~tion of principal component analysis .... ............... ....... 41 Statistics; how to use the residuals .................... ............... ....... 43 Plots ......................................... ............... ....... 44 Applications of principal component analysis ............ ............... ....... 46 8.1 Overview (plots) of any data table ................. ............... ..*... 46 8.2 Dimensionality reduction ....................... ............... 46 8.3 Similarity models ............................. ............... , . 47 Data pre-treatment .............................. ............... *....s. 47 10 Rank, or dim~sion~ty, of a principal components model. .......................... 48 -
Statistics and Actuarial Science 1
Statistics and Actuarial Science 1 STAT:3510/IGPI:3510 Biostatistics and STAT:4143/PSQF:4143 Statistics and Introduction to Statistical Methods. Students may not take STAT:3101/IGPI:3101 Introduction Actuarial Science to Mathematical Statistics II and STAT:4101/IGPI:4101 Mathematical Statistics II at the same time and get credit for both (nor go back to STAT:3101/IGPI:3101 after taking Chair STAT:4101/IGPI:4101). • Kung-Sik Chan Statistics Courses Director of Graduate Studies STAT:1000 First-Year Seminar 1 s.h. • Aixin Tan Small discussion class taught by a faculty member; topics Director of Undergraduate Studies, chosen by instructor; may include outside activities (e.g., films, lectures, performances, readings, visits to research Actuarial Science facilities). Requirements: first- or second-semester standing. • Elias S. Shiu STAT:1010 Statistics and Society 3 s.h. Director of Undergraduate Studies, Data Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical Science sciences; focus on critical approach to statistical evidence. • Rhonda R. DeCook Requirements: one year of high school algebra or MATH:0100. GE: Quantitative or Formal Reasoning. Director of Undergraduate Studies, STAT:1015 Introduction to Data Science 3 s.h. Statistics Data collection, visualization, and wrangling; basics of • Rhonda R. DeCook probability and statistical inference; fundamentals of data learning including regression, classification, prediction, and Undergraduate majors: actuarial science (B.S.); statistics cross-validation; computing, learning, and reporting in the R (B.S.) environment; literate programming; reproducible research. Undergraduate minor: statistics Requirements: one year of high school algebra or MATH:0100. Graduate degrees: M.S.