Mixture Models for Spherical Data with Applications to Protein Bioinformatics
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Essex County, Massachusetts, 1630-1768 Harold Arthur Pinkham Jr
University of New Hampshire University of New Hampshire Scholars' Repository Doctoral Dissertations Student Scholarship Winter 1980 THE TRANSPLANTATION AND TRANSFORMATION OF THE ENGLISH SHIRE IN AMERICA: ESSEX COUNTY, MASSACHUSETTS, 1630-1768 HAROLD ARTHUR PINKHAM JR. University of New Hampshire, Durham Follow this and additional works at: https://scholars.unh.edu/dissertation Recommended Citation PINKHAM, HAROLD ARTHUR JR., "THE TRANSPLANTATION AND TRANSFORMATION OF THE ENGLISH SHIRE IN AMERICA: ESSEX COUNTY, MASSACHUSETTS, 1630-1768" (1980). Doctoral Dissertations. 2327. https://scholars.unh.edu/dissertation/2327 This Dissertation is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. INFORMATION TO USERS This was produced from a copy of a document sent to us for microfilming. Whfle the most advanced technological means to photograph and reproduce this document have been used, the quality is heavily dependent upon the quality of the material submitted. The following explanation of techniques is provided to help you understand markings or notations vhich may appear on this reproduction. 1. The sign or “target” for pages apparently lacking from the document photographed is “Missing Page(s)”. If it was possible to obtain the missing page(s) or section, they are spliced into the film along with adjacent pages. This may have necessitated cutting through an image and duplicating adjacent pages to assure you of complete continuity. 2. When an image on the film is obliterated with a round black mark it is an indication that the film inspector noticed either blurred copy because of movement during exposure, or duplicate copy. -
Contributions to Directional Statistics Based Clustering Methods
University of California Santa Barbara Contributions to Directional Statistics Based Clustering Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics & Applied Probability by Brian Dru Wainwright Committee in charge: Professor S. Rao Jammalamadaka, Chair Professor Alexander Petersen Professor John Hsu September 2019 The Dissertation of Brian Dru Wainwright is approved. Professor Alexander Petersen Professor John Hsu Professor S. Rao Jammalamadaka, Committee Chair June 2019 Contributions to Directional Statistics Based Clustering Methods Copyright © 2019 by Brian Dru Wainwright iii Dedicated to my loving wife, Carmen Rhodes, without whom none of this would have been possible, and to my sons, Max, Gus, and Judah, who have yet to know a time when their dad didn’t need to work from early till late. And finally, to my mother, Judith Moyer, without your tireless love and support from the beginning, I quite literally wouldn’t be here today. iv Acknowledgements I would like to offer my humble and grateful acknowledgement to all of the wonderful col- laborators I have had the fortune to work with during my graduate education. Much of the impetus for the ideas presented in this dissertation were derived from our work together. In particular, I would like to thank Professor György Terdik, University of Debrecen, Faculty of Informatics, Department of Information Technology. I would also like to thank Professor Saumyadipta Pyne, School of Public Health, University of Pittsburgh, and Mr. Hasnat Ali, L.V. Prasad Eye Institute, Hyderabad, India. I would like to extend a special thank you to Dr Alexander Petersen, who has held the dual role of serving on my Doctoral supervisory commit- tee as well as wearing the hat of collaborator. -
A Guide on Probability Distributions
powered project A guide on probability distributions R-forge distributions Core Team University Year 2008-2009 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic ditribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 132 13 Misc 134 Conclusion 135 Bibliography 135 A Mathematical tools 138 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
Recent Advances in Directional Statistics
Recent advances in directional statistics Arthur Pewsey1;3 and Eduardo García-Portugués2 Abstract Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio- temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed. Keywords: Classification; Clustering; Dimension reduction; Distributional -
Multivariate Statistical Functions in R
Multivariate statistical functions in R Michail T. Tsagris [email protected] College of engineering and technology, American university of the middle east, Egaila, Kuwait Version 6.1 Athens, Nottingham and Abu Halifa (Kuwait) 31 October 2014 Contents 1 Mean vectors 1 1.1 Hotelling’s one-sample T2 test ............................. 1 1.2 Hotelling’s two-sample T2 test ............................ 2 1.3 Two two-sample tests without assuming equality of the covariance matrices . 4 1.4 MANOVA without assuming equality of the covariance matrices . 6 2 Covariance matrices 9 2.1 One sample covariance test .............................. 9 2.2 Multi-sample covariance matrices .......................... 10 2.2.1 Log-likelihood ratio test ............................ 10 2.2.2 Box’s M test ................................... 11 3 Regression, correlation and discriminant analysis 13 3.1 Correlation ........................................ 13 3.1.1 Correlation coefficient confidence intervals and hypothesis testing us- ing Fisher’s transformation .......................... 13 3.1.2 Non-parametric bootstrap hypothesis testing for a zero correlation co- efficient ..................................... 14 3.1.3 Hypothesis testing for two correlation coefficients . 15 3.2 Regression ........................................ 15 3.2.1 Classical multivariate regression ....................... 15 3.2.2 k-NN regression ................................ 17 3.2.3 Kernel regression ................................ 20 3.2.4 Choosing the bandwidth in kernel regression in a very simple way . 23 3.2.5 Principal components regression ....................... 24 3.2.6 Choosing the number of components in principal component regression 26 3.2.7 The spatial median and spatial median regression . 27 3.2.8 Multivariate ridge regression ......................... 29 3.3 Discriminant analysis .................................. 31 3.3.1 Fisher’s linear discriminant function .................... -
Handbook on Probability Distributions
R powered R-forge project Handbook on probability distributions R-forge distributions Core Team University Year 2009-2010 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic distribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 133 13 Misc 135 Conclusion 137 Bibliography 137 A Mathematical tools 141 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
Divisions 2019-2020
COUNTY CHAMPIONSHIPS - DIVISIONS 2019-2020 SENIORS 2018-2019 44 teams Teams Premier Berkshire Derbyshire Middlesex 1 Northumberland Nottinghamshire Surrey 1 Sussex 1 Yorkshire 8 1A Bedfordshire Buckinghamshire Cheshire Lancashire Leicestershire 1 Lincolnshire Norfolk 1 Warwickshire 1 8 1B Avon Devonshire Dorset Essex Gloucestershire Hampshire Kent 1 Middlesex 2 8 2A Durham Leicestershire 2 Northamptonshire South Yorkshire Staffordshire Warwickshire 2 Worcestershire 7 2B Cambridgeshire 1 Cambridgeshire 2 Hertfordshire Kent 2 Norfolk 2 Suffolk Surrey 3 7 2C Cornwall Gwent Somerset Surrey 2 Sussex 2 Sussex 3 Wiltshire 7 VETERANS 2018 – 2019 78 teams Teams Premier Berkshire 1 Hertfordshire 1 Kent 1 Leicestershire 1 Nottinghamshire 1 Sussex 1 Worcestershire 1 Yorkshire 1 8 1A Avon Derbyshire Gloucestershire Northumberland South Yorkshire Warwickshire 1 Yorkshire 2 7 1B Cambridgeshire 1 Devonshire 1 Essex 1 Middlesex 1 Middlesex 2 Norfolk 1 Sussex 2 7 2A Cheshire Lancashire Lincolnshire Northamptonshire 1 Northamptonshire 2 Staffordshire Yorkshire 3 7 2B Bedfordshire 1 Berkshire 2 Buckinghamshire 1 Buckinghamshire 2 Cambridgeshire 2 Hertfordshire 2 Suffolk 7 2C Essex 2 Kent 2 Kent 3 Middlesex 3 Surrey 1 Surrey 2 Sussex 3 7 2D Hampshire 1 Herefordshire Oxfordshire 1 Somerset 1 Warwickshire 2 Wiltshire 1 Wiltshire 2 7 3A Durham Leicestershire 2 Norfolk 2 North Wales Shropshire Warwickshire 3 Worcestershire 2 7 3B Berkshire 3 Berkshire 4 Hertfordshire 3 Kent 4 Kent 5 Middlesex 4 Middlesex 5 7 3C Bedfordshire 2 Cambridgeshire 3 Norfolk 3 Kent 6 -
Kent County Rugby Football Union Limited
Kent County Rugby Football Union Limited 2019-2020 Handbook President John Nunn Contents The Rules of Kent County Rugby Football Union Ltd 3 Kent County RFU Structure 5 Officers & Executive Committee 6 Sub Committees 7 RFU Staff 10 Representatives on Other Committees 11 County Committee Meetings 11 Kent Society of Rugby Football Union Referees Limited 12 Important Dates 13 Men’s Leagues 14 Women’s Leagues 16 RFU Principal Fixtures for Season 17 County Fixtures 18 County Mini & Youth Festivals 19 Shepherd Neame Kent Competitions 20 Competition Rules & Regulations 21 Member Clubs 25 Club Internet Directory 26 Associates of KCRFU 27 Sponsors 34 Partners 37 2 The Rules of Kent County Rugby Football Union Ltd Registered under the Co-operative and Community Benefit Societies Act 2014 (Register Number: 29080 R) are printed separately and each Member and Member of Committee has copy available for inspection. Reg. Office, Leonard House, 5-7 Newman Road, Bromley, Kent BR1 1RJ CONSTITUENT BODY RESOLUTIONS 1. The Annual Subscription of this Union shall become due on the 31st October in every year and shall be: £50 for Members £10 for Individual Associates £20 for Associated Clubs £250 for Corporate Associates (any member failing to pay the subscription by 1st November shall cease to have a vote for any purpose whatsoever in accordance with Rule 5.8.1.) 2. The County uniform shall consist of a dark blue jersey with the County Badge in white on the left breast, dark blue shorts and dark blue stockings with light blue tops and shall be worn by players in County Match- es. -
Kent County Rugby Football Union Limited
Kent County Rugby Football Union Limited www.kent-rugby.org President Hon. Secretary Hon. Treasurer J Nunn Mrs. S C Taylor P.J. Dessent [email protected] Notice is hereby given that the Annual General Meeting will be held virtually, by a Zoom meeting on Wednesday 2nd September 2020, 7.00 pm registration for a 7.30 pm start. The members of the County Committee hope that you will be able to attend in this altered format. Agenda 1. Apologies for absence. 6. Election of Officers & Vice Presidents for 2020/21. 2. Minutes of the last AGM, held on 26th June 2019. 7. Election of Committee for season 2020/21. 3. Address of the President, who will propose the adoption of the Report. 8. Appointment of Auditors. 4. Minutes of the AFGM of 12th December 2019. 9. Any other business. 5. Proposed Rule Amendments – Paper attached. Report Executive Committee The work of the County Executive Committee in overseeing the finances and work programmes of the Kent RFU has - as with all of us - been disrupted by the season effectively concluding towards the end of March. This put paid to at least half of our programme of maintaining contact with our clubs which had been initiated with close to 50 clubs physically through our insight evenings in season 18-19. Despite this, communication we hope has continued to improve with our 70 clubs in full or associate membership in the County and we thank our Administration Manager, Tracy Pettingale, for the discipline around contributions and the presentation and content of our website which several other Counties have complimented us on and sought help to build similar sites. -
A New Unified Approach for the Simulation of a Wide Class of Directional Distributions
This is a repository copy of A New Unified Approach for the Simulation of a Wide Class of Directional Distributions. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/123206/ Version: Accepted Version Article: Kent, JT orcid.org/0000-0002-1861-8349, Ganeiber, AM and Mardia, KV orcid.org/0000-0003-0090-6235 (2018) A New Unified Approach for the Simulation of a Wide Class of Directional Distributions. Journal of Computational and Graphical Statistics, 27 (2). pp. 291-301. ISSN 1061-8600 https://doi.org/10.1080/10618600.2017.1390468 © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. This is an author produced version of a paper published in Journal of Computational and Graphical Statistics. Uploaded in accordance with the publisher's self-archiving policy. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ A new unified approach for the simulation of a wide class of directional distributions John T. -
A Logistic Normal Mixture Model for Compositions with Essential Zeros
A Logistic Normal Mixture Model for Compositions with Essential Zeros Item Type text; Electronic Dissertation Authors Bear, John Stanley Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 28/09/2021 15:07:32 Link to Item http://hdl.handle.net/10150/621758 A Logistic Normal Mixture Model for Compositions with Essential Zeros by John S. Bear A Dissertation Submitted to the Faculty of the Graduate Interdisciplinary Program in Statistics In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy In the Graduate College The University of Arizona 2 0 1 6 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the disserta- tion prepared by John Bear, titled A Logistic Normal Mixture Model for Compositions with Essential Zeros and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Date: August 18, 2016 Dean Billheimer Date: August 18, 2016 Joe Watkins Date: August 18, 2016 Bonnie LaFleur Date: August 18, 2016 Keisuke Hirano Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. -
High-Precision Computation of Uniform Asymptotic Expansions for Special Functions Guillermo Navas-Palencia
UNIVERSITAT POLITÈCNICA DE CATALUNYA Department of Computer Science High-precision Computation of Uniform Asymptotic Expansions for Special Functions Guillermo Navas-Palencia Supervisor: Argimiro Arratia Quesada A dissertation submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Computing May 7, 2019 i Abstract In this dissertation, we investigate new methods to obtain uniform asymptotic ex- pansions for the numerical evaluation of special functions to high-precision. We shall first present the theoretical and computational fundamental aspects required for the development and ultimately implementation of such methods. Applying some of these methods, we obtain efficient new convergent and uniform expan- sions for numerically evaluating the confluent hypergeometric functions 1F1(a; b; z) and U(a, b, z), and the Lerch transcendent F(z, s, a) at high-precision. In addition, we also investigate a new scheme of computation for the generalized exponential integral En(z), obtaining one of the fastest and most robust implementations in double-precision arithmetic. In this work, we aim to combine new developments in asymptotic analysis with fast and effective open-source implementations. These implementations are com- parable and often faster than current open-source and commercial state-of-the-art software for the evaluation of special functions. ii Acknowledgements First, I would like to express my gratitude to my supervisor Argimiro Arratia for his support, encourage and guidance throughout this work, and for letting me choose my research path with full freedom. I also thank his assistance with admin- istrative matters, especially in periods abroad. I am grateful to Javier Segura and Amparo Gil from Universidad de Cantabria for inviting me for a research stay and for their inspirational work in special func- tions, and the Ministerio de Economía, Industria y Competitividad for the financial support, project APCOM (TIN2014-57226-P), during the stay.