From Fourier Transforms to Wavelet Analysis: Mathematical Concepts and Examples
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IMS Presidential Address(Es) Each Year, at the End of Their Term, the IMS CONTENTS President Gives an Address at the Annual Meeting
Volume 50 • Issue 6 IMS Bulletin September 2021 IMS Presidential Address(es) Each year, at the end of their term, the IMS CONTENTS President gives an address at the Annual Meeting. 1 IMS Presidential Addresses: Because of the pandemic, the Annual Meeting, Regina Liu and Susan Murphy which would have been at the World Congress in Seoul, didn’t happen, and Susan Murphy’s 2–3 Members’ news : Bin Yu, Marloes Maathuis, Jeff Wu, Presidential Address was postponed to this year. Ivan Corwin, Regina Liu, Anru The 2020–21 President, Regina Liu, gave her Zhang, Michael I. Jordan Address by video at the virtual JSM, which incorpo- 6 Profile: Michael Klass rated Susan’s video Address. The transcript of the two addresses is below. 8 Recent papers: Annals of 2020–21 IMS President Regina Liu Probability, Annals of Applied Probability, ALEA, Probability Regina Liu: It’s a great honor to be the IMS President, and to present this IMS and Mathematical Statistics Presidential address in the 2021 JSM. Like many other things in this pandemic time, this year’s IMS Presidential Address will be also somewhat different from those in the 10 IMS Awards past. Besides being virtual, it will be also given jointly by the IMS Past President Susan 11 IMS London meeting; Olga Murphy and myself. The pandemic prevented Susan from giving her IMS Presidential Taussky-Todd Lecture; Address last year. In keeping with the IMS tradition, I invited Susan to join me today, Observational Studies and I am very pleased she accepted my invitation. Now, Susan will go first with her 12 Sound the Gong: Going 2020 IMS Presidential Address. -
STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
STATISTICS IN THE DATA SCIENCE ERA: A Symposium to Celebrate 50 Years of Statistics at the University of Michigan September 20-21, 2019 Rackham Graduate School 915 E. Washington Street Ann Arbor, MI 48109 A Symposium to Celebrate 50 Years of Statistics at the University of Michigan 1 PROGRAM Friday, September 20, 2019 Saturday, September 21, 2019 9:30-10:30 ...............Coffee and Registration 8:30-9:00 ................ Coffee 10:30-10:50 ..............Opening Remarks Invited Session 2 Chair: Snigdha Panigrahi, University of Michigan Xuming He and Dean Anne Curzan 9:00-9:30 ................ Adam Rothman, University of Minnesota Shrinking Characteristics of Precision Matrix Keynote Session 1 Chair: Ambuj Tewari, University of Michigan Estimators 10:50-11:50 ...............Susan Murphy, Harvard University Online Experimentation with Learning Algorithms 9:30-10:00 ............... Bodhisattva Sen, Columbia University in a Clinical Trial Multivariate Rank-based Distribution-free Nonparametric Testing using Measure 11:50-12:00 ...............Group Photo Transportation PROGRAM 12:00-1:30 ................Lunch and Poster Session 10:00-10:30 ............. Min Qian, Columbia University Personalized Policy Learning using Longitudinal University of Michigan Invited Session 1 Chair: Ziwei Zhu, Mobile Health Data 1:30-2:00 .................Sumanta Basu, Cornell University Large Spectral Density Matrix Estimation by 10:30-11:00 .............. Coffee Break Thresholding Invited Session 3 Chair: Kean Ming Tan, University of Michigan 2:00-2:30 .................Anindya Bhadra, Purdue University 11:00-11:30 ............... Ali Shojaie, University of Washington Horseshoe Regularization for Machine Learning in Incorporating Auxiliary Information into Learning Complex and Deep Models Directed Acyclic Graphs 2:30-3:00 .................Coffee Break 11:30-12:00 .............. -
The Completely Sufficient Statistician (Css)
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 2002 - 14th Annual Conference Proceedings THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation O'Brien, Ralph G. (2002). "THE COMPLETELY SUFFICIENT STATISTICIAN (CSS)," Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1196 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 1 THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Department ofBiostatistics and Epidemiology Cleveland Clinic Foundation, Cleveland, OH 44195 Today's ideal statistical scientist develops and maintains a broad range of technical skills and personal qualities in four domains: (1) numeracy, in mathematics and numerical computing; (2) articulacy and people skills; (3) literacy, in technical writing and in programm,ing; and (4) graphicacy. Yet, many of these components are given short shrift in university statistics programs. Can even the best statistician today really be "completely sufficient?" Introduction Let me start by saying that I was absolutely delighted, grateful, and humbled to be invited to give the workshop and keynote address at this meeting. -
A R Software for Wavelets and Statistics
A R Software for Wavelets and Statistics Here is a list and a brief description of some important R packages related to/that make use of wavelets. Such packages are important as they enable ideas to be reproduced, checked, and modified by the scientific community. This is probably just as important as the advertisement of the ideas through scientific papers and books. This is the ‘reproducible research’ view expressed by Buckheit and Donoho (1995). Making software packages freely available is an extremely valuable service to the community and so we choose to acclaim the author(s) of each package by displaying their names! The descriptions are extracted from each package description from CRAN. The dates refer to the latest updates on CRAN, not the original publication date. Please let me know of any omissions. adlift performs adaptive lifting (generalized wavelet transform) for irregu- larly spaced data, see Nunes et al. (2006). Written by Matt Nunes and Marina Knight, University of Bristol, UK, 2006. brainwaver computes a set of scale-dependent correlation matrices between sets of preprocessed functional magnetic resonance imaging data and then performs a ‘small-world’ analysis of these matrices, see Achard et al. (2006). Written by Sophie Achard, Brain Mapping Unit, University of Cambridge, UK, 2007. CVThresh carries out level-dependent cross-validation for threshold selection in wavelet shrinkage, see Kim and Oh (2006). Written by Donghoh Kim, Hongik University and Hee-Seok Oh, Seoul National University, Korea, 2006. DDHFm implements the data-driven Haar–Fisz transform as described in 6.5, see references therein. Written by Efthimios Motakis, Piotr Fryzlewicz and Guy Nason, University of Bristol, Bristol, UK. -
An Overview of Wavelet Transform Concepts and Applications
An overview of wavelet transform concepts and applications Christopher Liner, University of Houston February 26, 2010 Abstract The continuous wavelet transform utilizing a complex Morlet analyzing wavelet has a close connection to the Fourier transform and is a powerful analysis tool for decomposing broadband wavefield data. A wide range of seismic wavelet applications have been reported over the last three decades, and the free Seismic Unix processing system now contains a code (succwt) based on the work reported here. Introduction The continuous wavelet transform (CWT) is one method of investigating the time-frequency details of data whose spectral content varies with time (non-stationary time series). Moti- vation for the CWT can be found in Goupillaud et al. [12], along with a discussion of its relationship to the Fourier and Gabor transforms. As a brief overview, we note that French geophysicist J. Morlet worked with non- stationary time series in the late 1970's to find an alternative to the short-time Fourier transform (STFT). The STFT was known to have poor localization in both time and fre- quency, although it was a first step beyond the standard Fourier transform in the analysis of such data. Morlet's original wavelet transform idea was developed in collaboration with the- oretical physicist A. Grossmann, whose contributions included an exact inversion formula. A series of fundamental papers flowed from this collaboration [16, 12, 13], and connections were soon recognized between Morlet's wavelet transform and earlier methods, including harmonic analysis, scale-space representations, and conjugated quadrature filters. For fur- ther details, the interested reader is referred to Daubechies' [7] account of the early history of the wavelet transform. -
LORI MCLEOD, Phd
LORI MCLEOD, PhD Head, Psychometrics Education PhD, Quantitative Psychology, University of North Carolina, Chapel Hill, NC MA, Quantitative Psychology, University of North Carolina, Chapel Hill, NC BS, Statistics, Mathematics Education (graduated summa cum laude), North Carolina State University, Raleigh, NC Summary of Professional Experience Lori McLeod, PhD, is Head of Psychometrics at RTI-HS. Dr. McLeod is a psychometrician with more than 15 years of experience in instrument development and validation, as well as experience conducting systematic assessments of clinical and economic literature and developing appropriate health outcome strategies. In her Psychometrics role, she has conducted many psychometric evaluations of both paper-and-pencil and computer-administered instruments. These investigations have included the assessment of scale reliability, validity, responsiveness, and work to identify PRO responders. In addition, Dr. McLeod has experience conducting and analyzing data from clinical trials and observational studies, including data to document the burden of disease. Dr. McLeod has published numerous related manuscripts in Applied Psychological Measurement, Pharmacoeconomics, Quality of Life Research, and Psychometrika. She has experience in a wide variety of therapeutic areas, including chronic pain, dermatology, oncology, psychiatry, respiratory, sleep disorders, urology, and sexual dysfunction. Dr. McLeod currently serves as the co-leader of the Psychometric Special Interest Group for the International Society on Quality -
A Low Bit Rate Audio Codec Using Wavelet Transform
Navpreet Singh, Mandeep Kaur,Rajveer Kaur / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2222-2228 An Enhanced Low Bit Rate Audio Codec Using Discrete Wavelet Transform Navpreet Singh1, Mandeep Kaur2, Rajveer Kaur3 1,2(M. tech Students, Department of ECE, Guru kashi University, Talwandi Sabo(BTI.), Punjab, INDIA 3(Asst. Prof. Department of ECE, Guru Kashi University, Talwandi Sabo (BTI.), Punjab,INDIA Abstract Audio coding is the technology to represent information and perceptually irrelevant signal audio in digital form with as few bits as possible components can be separated and later removed. This while maintaining the intelligibility and quality class includes techniques such as subband coding; required for particular application. Interest in audio transform coding, critical band analysis, and masking coding is motivated by the evolution to digital effects. The second class takes advantage of the communications and the requirement to minimize statistical redundancy in audio signal and applies bit rate, and hence conserve bandwidth. There is some form of digital encoding. Examples of this class always a tradeoff between lowering the bit rate and include entropy coding in lossless compression and maintaining the delivered audio quality and scalar/vector quantization in lossy compression [1]. intelligibility. The wavelet transform has proven to Digital audio compression allows the be a valuable tool in many application areas for efficient storage and transmission of audio data. The analysis of nonstationary signals such as image and various audio compression techniques offer different audio signals. In this paper a low bit rate audio levels of complexity, compressed audio quality, and codec algorithm using wavelet transform has been amount of data compression. -
Careers for Math Majors
CAREERS FOR MATH MAJORS Cheryl A. Hallman Asst. Dean / Director Career Center #1 YOU’VE GOT WHAT EMPLOYERS WANT WHAT EMPLOYERS LOOK FOR IN A CANDIDATE? ANALYTICAL & CRITICAL THINKING • Defining and formulating problems • Interpreting data and evaluating results • Understanding patterns and structures • Critical analysis and quantitative thinking RESEARCH • Applying Theoretical Approaches to research problems • Analyzing and interpreting statistics COMMUNICATIONS • Computer modeling • Writing • Explaining complex concepts THE #2 TOP CAREERS BELONG TO MATH MAJORS TOP CAREERS Careercast.com 2019 1. Data Scientist Projected Growth:19.00% 2. Statistician Projected Growth:33.00% TOP CAREERS Careercast.com 2019 3. University Professor Projected Growth:15.00% 7. Information Security Analyst Projected Growth:28.00% TOP CAREERS Careercast.com 2019 8. Mathematician Overall Rating:127 Projected Growth:33.00% 9. Operations Research Analyst Projected Growth:27.00% TOP CAREERS Careercast.com 2019 10. Actuary Projected Growth:22.00% #3 SALARIES AREN’T BAD SALARIES • 15 Most Valuable College Majors – Forbes • 8 College Degrees That Will Earn Your Money Back – Salary.com DATA SCIENTIST Median Salary: $114,520 STATISTICIAN Median Salary: $84,760 UNIVERSITY PROFESSORMedian Salary: $76,000 MATHEMATICIAN Median Salary: $84,760 OPERATIONS RESEARCH ANALYST Median Salary: $81,390 ACTUARY – Median Salary: $101,560 #4 AWESOME AT STANDARDIZED TESTS STANDARDIZED TESTS CAREERS FOR MATH MAJORS BUSINESS & FINANCE – Help determine a company’s financial performance by analyzing balance sheets and income and cash flow statements Actuary Underwriting Accounting Economist Financial Analyst Top Employers PwC, Deloitte, KPMG, EY, Bank of America, Cigna, Ernst & Young, Merrill Lynch, TRW, Goldman Sachs, TD Waterhouse, Charles Schwab, JP Morgan Chase, Citigroup, Morgan Stanley CAREERS FOR MATH MAJORS Data Analytics– Collect, organize and analyze raw data in order to effect positive change and increase efficiency. -
Image Compression Techniques by Using Wavelet Transform
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by International Institute for Science, Technology and Education (IISTE): E-Journals Journal of Information Engineering and Applications www.iiste.org ISSN 2224-5782 (print) ISSN 2225-0506 (online) Vol 2, No.5, 2012 Image Compression Techniques by using Wavelet Transform V. V. Sunil Kumar 1* M. Indra Sena Reddy 2 1. Dept. of CSE, PBR Visvodaya Institute of Tech & Science, Kavali, Nellore (Dt), AP, INDIA 2. School of Computer science & Enginering, RGM College of Engineering & Tech. Nandyal, A.P, India * E-mail of the corresponding author: [email protected] Abstract This paper is concerned with a certain type of compression techniques by using wavelet transforms. Wavelets are used to characterize a complex pattern as a series of simple patterns and coefficients that, when multiplied and summed, reproduce the original pattern. The data compression schemes can be divided into lossless and lossy compression. Lossy compression generally provides much higher compression than lossless compression. Wavelets are a class of functions used to localize a given signal in both space and scaling domains. A MinImage was originally created to test one type of wavelet and the additional functionality was added to Image to support other wavelet types, and the EZW coding algorithm was implemented to achieve better compression. Keywords: Wavelet Transforms, Image Compression, Lossless Compression, Lossy Compression 1. Introduction Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia based web applications. -
JPEG2000: Wavelets in Image Compression
EE678 WAVELETS APPLICATION ASSIGNMENT 1 JPEG2000: Wavelets In Image Compression Group Members: Qutubuddin Saifee [email protected] 01d07009 Ankur Gupta [email protected] 01d070013 Nishant Singh [email protected] 01d07019 Abstract During the past decade, with the birth of wavelet theory and multiresolution analysis, image processing techniques based on wavelet transform have been extensively studied and tremendously improved. JPEG 2000 uses wavelet transform and provides an integrated toolbox to better address increasing needs for compression. In this report, we study the basic concepts of JPEG2000, the LeGall 5/3 and Daubechies 9/7 wavelets used in it and finally Embedded zerotree wavelet coding and Set Partitioning in Hierarchical Trees. Index Terms Wavelets, JPEG2000, Image Compression, LeGall, Daubechies, EZW, SPIHT. I. INTRODUCTION I NCE the mid 1980s, members from both the International Telecommunications Union (ITU) and the International SOrganization for Standardization (ISO) have been working together to establish a joint international standard for the compression of grayscale and color still images. This effort has been known as JPEG, the Joint Photographic Experts Group. The process was such that, after evaluating a number of coding schemes, the JPEG members selected a discrete cosine transform( DCT)-based method in 1988. From 1988 to 1990, the JPEG group continued its work by simulating, testing and documenting the algorithm. JPEG became Draft International Standard (DIS) in 1991 and International Standard (IS) in 1992. With the continual expansion of multimedia and Internet applications, the needs and requirements of the technologies used grew and evolved. In March 1997, a new call for contributions was launched for the development of a new standard for the compression of still images, the JPEG2000 standard. -
When You Consult a Statistician … What to Expect
WHEN YOU CONSULT A STATISTICIAN . WHAT TO EXPECT SECTION ON STATISTICAL CONSULTING AMERICAN STATISTICAL ASSOCIATION 2003, 2007, 2013 When you consult a statistician, you enlist the help of a professional who is particularly skilled at solving research problems. The statistician will try to guide you to the best way to obtain an answer to your research questions. The aim of this brochure is to provide you, as a potential client or employer of a statistical consultant, with an overview of what is involved in a consultation. The sections address such topics as when it is useful to consult a statistician, the responsibilities of statistician and client, and what to expect as you initiate and continue a working relationship with a statistician, including a brief summary of the ethical principles that guide statistical work CONTENTS I. WHAT IS STATISTICAL CONSULTATION? A. Do you need a statistical consultant? B. Roles of a Statistical Consultant C. How to Involve a Statistical Consultant Prior to data collection After data have been collected Once the data are analyzed. D. What To Look For in a Statistical Consultant II. THE FIRST CONSULTING SESSION A. Introducing the consultant to the problem B. What materials are needed? C. What do you expect from the statistician? D. Preserving confidentiality is important E. Financial terms F. Schedule for payment G. Other considerations III. RESPONSIBILITIES OF A STATISTICAL CONSULTANT IV. RESPONSIBILITIES OF A CLIENT V. ETHICS IN STATISTICAL CONSULTING I. What is Statistical Consultation? In a statistical consultation, you seek the help of a statistician to select and use the best methods for obtaining and analyzing data for some data-related objective. -
The Inimitable F. N. David: a Renaissance Statistician Amanda L
The Inimitable F. N. David: A Renaissance Statistician Amanda L. Golbeck and Craig A. Molgaard 1. Highlights Florence Nightingale David (1909–1993) was known to readers of her scholarly publications as “F. N. David” and to her colleagues as “David” or “FND.” David has been recognized as the leading, most accomplished and most memorable British woman statistician of the mid-20th cen- tury ([11], [14]). She was a professor at University College London (UCL), and then at the University of California (UC). When we were graduate students in the early 1980s at UC-Berkeley, where David had an affiliation before and after her retirement from UC-Riverside, she was already a legend in statistics. “Enormous energy” and “prolific output.” These are words that statistician D. E. Barton wrote in an obituary to describe his close UCL colleague. Barton added that these qualities were part of what made David “an exciting colleague to work with” [1]. These qualities are also what make it difficult to pigeonhole David’s illustrious 60-year- long career, which was packed with probability and statis- tical ideas. The numbers alone make clear the extent of her immense energy and output: She wrote nine books, over one-hundred published articles, over fifteen secret (classi- fied) war reports, and various forest service white papers. She was working on a tenth book and other articles and papers when she died. David’s life as a statistician began at age 22, when she walked into Karl Pearson’s office at UCL in 1931 and asked him for a job. Pearson (1857–1936) was in his mid 70s and an enormous intellectual figure, a protégé of Sir Francis Galton (1822–1911).