DOCSLIB.ORG

Desctools.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Desctools.Pdf Package ‘DescTools’ September 9, 2021 Type Package Title Tools for Descriptive Statistics Version 0.99.43 Date 2021-09-08 Description A collection of miscellaneous basic statistic functions and convenience wrappers for effi- ciently describing data. The author's intention was to create a toolbox, which facilitates the (no- toriously time consuming) first descriptive tasks in data analysis, consisting of calculating de- scriptive statistics, drawing graphical summaries and reporting the results. The package con- tains furthermore functions to produce documents using MS Word (or PowerPoint) and func- tions to import data from Excel. Many of the included functions can be found scat- tered in other packages and other sources written partly by Titans of R. The reason for collect- ing them here, was primarily to have them consolidated in ONE instead of dozens of pack- ages (which themselves might depend on other packages which are not needed at all), and to pro- vide a common and consistent interface as far as function and arguments naming, NA han- dling, recycling rules etc. are concerned. Google style guides were used as naming rules (in ab- sence of convincing alternatives). The 'BigCamelCase' style was consequently applied to func- tions borrowed from contributed R packages as well. Suggests RDCOMClient, tcltk, VGAM, R.rsp Depends base, stats, R (>= 3.6.0) Imports graphics, grDevices, methods, MASS, utils, boot, mvtnorm, expm, Rcpp (>= 0.12.10), rstudioapi, Exact, gld, data.table LinkingTo Rcpp, BH License GPL (>= 2) LazyLoad yes LazyData yes Additional_repositories http://www.omegahat.net/R URL https://andrisignorell.github.io/DescTools/, https://github.com/AndriSignorell/DescTools/ BugReports https://github.com/AndriSignorell/DescTools/issues SystemRequirements C++11 1 2 RoxygenNote 6.1.1 Encoding UTF-8 NeedsCompilation yes VignetteBuilder R.rsp Author Andri Signorell [aut, cre], Ken Aho [ctb], Andreas Alfons [ctb], Nanina Anderegg [ctb], Tomas Aragon [ctb], Chandima Arachchige [ctb], Antti Arppe [ctb], Adrian Baddeley [ctb], Kamil Barton [ctb], Ben Bolker [ctb], Hans W. Borchers [ctb], Frederico Caeiro [ctb], Stephane Champely [ctb], Daniel Chessel [ctb], Leanne Chhay [ctb], Nicholas Cooper [ctb], Clint Cummins [ctb], Michael Dewey [ctb], Harold C. Doran [ctb], Stephane Dray [ctb], Charles Dupont [ctb], Dirk Eddelbuettel [ctb], Claus Ekstrom [ctb], Martin Elff [ctb], Jeff Enos [ctb], Richard W. Farebrother [ctb], John Fox [ctb], Romain Francois [ctb], Michael Friendly [ctb], Tal Galili [ctb], Matthias Gamer [ctb], Joseph L. Gastwirth [ctb], Vilmantas Gegzna [ctb], Yulia R. Gel [ctb], Sereina Graber [ctb], Juergen Gross [ctb], Gabor Grothendieck [ctb], Frank E. Harrell Jr [ctb], Richard Heiberger [ctb], Michael Hoehle [ctb], Christian W. Hoffmann [ctb], Soeren Hojsgaard [ctb], Torsten Hothorn [ctb], 3 Markus Huerzeler [ctb], Wallace W. Hui [ctb], Pete Hurd [ctb], Rob J. Hyndman [ctb], Christopher Jackson [ctb], Matthias Kohl [ctb], Mikko Korpela [ctb], Max Kuhn [ctb], Detlew Labes [ctb], Friederich Leisch [ctb], Jim Lemon [ctb], Dong Li [ctb], Martin Maechler [ctb], Arni Magnusson [ctb], Ben Mainwaring [ctb], Daniel Malter [ctb], George Marsaglia [ctb], John Marsaglia [ctb], Alina Matei [ctb], David Meyer [ctb], Weiwen Miao [ctb], Giovanni Millo [ctb], Yongyi Min [ctb], David Mitchell [ctb], Franziska Mueller [ctb], Markus Naepflin [ctb], Daniel Navarro [ctb], Henric Nilsson [ctb], Klaus Nordhausen [ctb], Derek Ogle [ctb], Hong Ooi [ctb], Nick Parsons [ctb], Sandrine Pavoine [ctb], Tony Plate [ctb], Luke Prendergast [ctb], Roland Rapold [ctb], William Revelle [ctb], Tyler Rinker [ctb], Brian D. Ripley [ctb], Caroline Rodriguez [ctb], Nathan Russell [ctb], Nick Sabbe [ctb], Ralph Scherer [ctb], Venkatraman E. Seshan [ctb], Michael Smithson [ctb], Greg Snow [ctb], Karline Soetaert [ctb], Werner A. Stahel [ctb], 4 R topics documented: Alec Stephenson [ctb], Mark Stevenson [ctb], Ralf Stubner [ctb], Matthias Templ [ctb], Duncan Temple Lang [ctb], Terry Therneau [ctb], Yves Tille [ctb], Luis Torgo [ctb], Adrian Trapletti [ctb], Joshua Ulrich [ctb], Kevin Ushey [ctb], Jeremy VanDerWal [ctb], Bill Venables [ctb], John Verzani [ctb], Pablo J. Villacorta Iglesias [ctb], Gregory R. Warnes [ctb], Stefan Wellek [ctb], Hadley Wickham [ctb], Rand R. Wilcox [ctb], Peter Wolf [ctb], Daniel Wollschlaeger [ctb], Joseph Wood [ctb], Ying Wu [ctb], Thomas Yee [ctb], Achim Zeileis [ctb] Maintainer Andri Signorell <[email protected]> Repository CRAN Date/Publication 2021-09-09 07:10:09 UTC R topics documented: DescTools-package . 13 ABCCoords . 24 Abind . 25 Abstract . 29 AddMonths . 30 AddMonthsYM . 31 Agree . 32 AllDuplicated . 33 AllIdentical . 35 AndersonDarlingTest . 36 Append . 37 Arrow............................................ 39 as.matrix.xtabs . 40 AscToChar . 41 Asp ............................................. 42 Association measures . 42 R topics documented: 5 Assocs . 45 Atkinson . 47 AUC............................................. 48 AxisBreak . 49 axTicks.POSIXct . 50 BarnardTest . 52 BartelsRankTest . 55 BarText . 57 Base Conversions . 59 Benford . 60 Between, Outside . 62 Bg.............................................. 64 BhapkarTest . 65 BinomCI . 66 BinomCIn . 70 BinomDiffCI . 71 BinomRatioCI . 74 BinTree . 76 BootCI . 78 BoxCox . 79 BoxCoxLambda . 80 BoxedText . 81 BreslowDayTest . 83 BreuschGodfreyTest . 85 BrierScore . 87 BubbleLegend . 89 Canvas............................................ 90 CartToPol . 91 CatTable . 92 CCC............................................. 93 Clockwise . 96 Closest . 97 Coalesce . 98 CochranArmitageTest . 99 CochranQTest . 101 CoefVar . 103 CohenD . 105 CohenKappa . 106 CollapseTable . 108 ColorLegend . 110 ColToGrey . 113 ColToHex . 114 ColToHsv . 115 ColToOpaque . 116 ColToRgb . 117 ColumnWrap . 118 CombPairs . 119 CompleteColumns . 120 6 R topics documented: ConDisPairs . 120 Conf............................................. 121 ConnLines . 125 ConoverTest . 126 Contrasts . 129 ConvUnit . 130 Cor ............................................. 132 CorPart . 135 CorPolychor . 136 CountCompCases . 138 CountWorkDays . 139 CourseData . 140 CramerVonMisesTest . ..
Load more

Recommended publications
  • Navigating the R Package Universe by Julia Silge, John C
    CONTRIBUTED RESEARCH ARTICLES 558 Navigating the R Package Universe by Julia Silge, John C. Nash, and Spencer Graves Abstract Today, the enormous number of contributed packages available to R users outstrips any given user’s ability to understand how these packages work, their relative merits, or how they are related to each other. We organized a plenary session at useR!2017 in Brussels for the R community to think through these issues and ways forward. This session considered three key points of discussion. Users can navigate the universe of R packages with (1) capabilities for directly searching for R packages, (2) guidance for which packages to use, e.g., from CRAN Task Views and other sources, and (3) access to common interfaces for alternative approaches to essentially the same problem. Introduction As of our writing, there are over 13,000 packages on CRAN. R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. At useR!2017 in Brussels, we organized a plenary session on this issue, with three themes: search, guidance, and unification. Here, we summarize these important themes, the discussion in our community both at useR!2017 and in the intervening months, and where we can go from here. Users need options to search R packages, perhaps the content of DESCRIPTION files, documenta- tion files, or other components of R packages. One author (SG) has worked on the issue of searching for R functions from within R itself in the sos package (Graves et al., 2017).
    [Show full text]
  • Computational Statistics in Practice
    Computational Statistics in Practice Some Observations Dirk Eddelbuettel 10 November 2016 Invited Presentation Department of Statistics University of Illinois Urbana-Champaign, IL UIUC Nov 2016 1/147 Motivation UIUC Nov 2016 2/147 Almost all topics in twenty-first-century statistics are now computer-dependent [...] Here and in all our examples we are employing the language R, itself one of the key developments in computer-based statistical methodology. Efron and Hastie, 2016, pages xv and 6 (footnote 3) UIUC Nov 2016 3/147 A View of the World Computational Statistics in Practice • Statistics is now computational (Efron & Hastie, 2016) • Within (computational) statistics, reigning tool is R • Given R, Rcpp key for two angles: • Performance always matters, ease of use a sweetspot • “Extending R” (Chambers, 2016) • Time permitting • Being nice to other (languages) • an underdiscussed angle in industry UIUC Nov 2016 4/147 Overview / Outline / Plan Drawing on three Talks • Rcpp Introduction (from recent workshops / talks / courses) • [if time] If You Can’t Beat ’em (from JSS session at JSM) • [if time] Open Source Finance (from an industry conference) UIUC Nov 2016 5/147 About Me Brief Bio • PhD, MA Econometrics; MSc Ind.Eng. (Comp.Sci./OR) • Finance Quant for 20 years • Open Source for 22 years • Debian developer • R package author / contributor • R Foundation Board member, R Consortium ISC member • JSS Associate Editor UIUC Nov 2016 6/147 Rcpp: Introduction via Tweets UIUC Nov 2016 7/147 UIUC Nov 2016 8/147 UIUC Nov 2016 9/147 UIUC Nov 2016 10/147 UIUC Nov 2016 11/147 UIUC Nov 2016 12/147 UIUC Nov 2016 13/147 UIUC Nov 2016 14/147 UIUC Nov 2016 15/147 UIUC Nov 2016 16/147 Extending R UIUC Nov 2016 17/147 Why R? : Programming with Data from 1977 to 2016 Thanks to John Chambers for high-resolution cover images.
    [Show full text]
  • Frequently Asked Questions About Rcpp
    Frequently Asked Questions about Rcpp Dirk Eddelbuettel Romain François Rcpp version 0.12.9 as of January 14, 2017 Abstract This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, François, Allaire, Ushey, Kou, Russel, Chambers, and Bates, 2017; Eddelbuettel and François, 2011; Eddelbuettel, 2013) package. Contents 1 Getting started 2 1.1 How do I get started ?.....................................................2 1.2 What do I need ?........................................................2 1.3 What compiler can I use ?...................................................3 1.4 What other packages are useful ?..............................................3 1.5 What licenses can I choose for my code?..........................................3 2 Compiling and Linking 4 2.1 How do I use Rcpp in my package ?............................................4 2.2 How do I quickly prototype my code?............................................4 2.2.1 Using inline.......................................................4 2.2.2 Using Rcpp Attributes.................................................4 2.3 How do I convert my prototyped code to a package ?..................................5 2.4 How do I quickly prototype my code in a package?...................................5 2.5 But I want to compile my code with R CMD SHLIB !...................................5 2.6 But R CMD SHLIB still does not work !...........................................6 2.7 What about LinkingTo ?...................................................6
    [Show full text]
  • International Journal of Applied Science and Research
    International Journal of Applied Science and Research FLOOD FREQUENCY ANALYSIS USING GUMBEL DISTRIBUTION EQUATION IN PART OF PORT HARCOURT METROPOLIS Eke, Stanley N. & Hart, Lawrence Department of Surveying and Geomatics, Rivers State University, Port Harcourt, Nigeria IJASR 2020 VOLUME 3 ISSUE 3 MAY – JUNE ISSN: 2581-7876 Abstract – The adequacy of the Gumbel distribution equation for hydrological extremes, with regards to rainfall extreme, is very paramount in hydrologic studies and infrastructural development of any region. This study investigates how the Gumbel distribution equation model and rainfall data set can be used to analyse flood frequency and flood extreme ratio of any given spatial domain and underscore its significance in the application of the model in geo-analysis of varying environmental phenomena. The classical approach of periodic observation of flood heights was deployed over a consistent number of rainfall days in addition to the determination of rainfall intensity and flow rate using relevant hydrological models over a period of time from available rainfall information. The geospatial height data of the western part of the Port Harcourt city metropolis being the study area was also provided. The result showed that a flood peak of 82cm was determined to have a sample mode of 0.532 in relation to sample size of 30 with an associated standard deviation of 1.1124. The result showed that from the frequency curve, the occurrence of smaller floods with a flood peak height of 90cm will be symmetrical and skewed. We assert that the Gumbel distribution equation model serves as a veritable tool for quick and efficient flood analysis and prediction for the study area.
    [Show full text]
  • Bab 1 Pendahuluan
    BAB 1 PENDAHULUAN Bab ini akan membahas pengertian dasar statistik dengan sub-sub pokok bahasan sebagai berikut : Sub Bab Pokok Bahasan A. Sejarah dan Perkembangan Statistik B. Tokoh-tokoh Kontributor Statistika C. Definisi dan Konsep Statistik Modern D. Kegunaan Statistik E. Pembagian Statistik F. Statistik dan Komputer G. Soal Latihan A. Sejarah dan Perkembangan Statistik Penggunaan istilah statistika berakar dari istilah-istilah dalam bahasa latin modern statisticum collegium (“dewan negara”) dan bahasa Italia statista (“negarawan” atau “politikus”). Istilah statistik pertama kali digunakan oleh Gottfried Achenwall (1719-1772), seorang guru besar dari Universitas Marlborough dan Gottingen. Gottfried Achenwall (1749) menggunakan Statistik dalam bahasa Jerman untuk pertama kalinya sebagai nama bagi kegiatan analisis data kenegaraan, dengan mengartikannya sebagai “ilmu tentang negara/state”. Pada awal abad ke- 19 telah terjadi pergeseran arti menjadi “ilmu mengenai pengumpulan dan klasifikasi data”. Sir John Sinclair memperkenalkan nama dan pengertian statistics ini ke dalam bahasa Inggris. E.A.W. Zimmerman mengenalkan kata statistics ke negeri Inggris. Kata statistics dipopulerkan di Inggris oleh Sir John Sinclair dalam karyanya: Statistical Account of Scotland 1791-1799. Namun demikian, jauh sebelum abad XVIII masyarakat telah mencatat dan menggunakan data untuk keperluan mereka. Pada awalnya statistika hanya mengurus data yang dipakai lembaga- lembaga administratif dan pemerintahan. Pengumpulan data terus berlanjut, khususnya melalui sensus yang dilakukan secara teratur untuk memberi informasi kependudukan yang selalu berubah. Dalam bidang pemerintahan, statistik telah digunakan seiring dengan perjalanan sejarah sejak jaman dahulu. Kitab perjanjian lama (old testament) mencatat adanya kegiatan sensus penduduk. Pemerintah kuno Babilonia, Mesir, dan Roma mengumpulkan data lengkap tentang penduduk dan kekayaan alam yang dimilikinya.
    [Show full text]
  • An Estimation of the Probability Distribution of Wadi Bana Flow in the Abyan Delta of Yemen
    Journal of Agricultural Science; Vol. 4, No. 6; 2012 ISSN 1916-9752 E-ISSN 1916-9760 Published by Canadian Center of Science and Education An Estimation of the Probability Distribution of Wadi Bana Flow in the Abyan Delta of Yemen Khader B. Atroosh (Corresponding author) AREA, Elkod Agricultural Research Station, Abyan, Yemen P.O. Box 6035, Kormaksar, Aden Tel: 967-773-229-056 E-mail: [email protected] Ahmed T. Moustafa International Center for Agricultural Research in the Dry Areas (ICARDA) Dubai Office, P.O. Box 13979, Dubai, UAE Tel: 971-50-636-7156 E-mail: [email protected] Received: January 5, 2012 Accepted: January 29, 2011 Online Published: April 17, 2012 doi:10.5539/jas.v4n6p80 URL: http://dx.doi.org/10.5539/jas.v4n6p80 Abstract Wadi Bana is one of the main Wadies in Yemen, where different quantities of water reach the Delta and depends on the amounts of rainfall in the wadi watershed. In order to estimate the spate water flow distribution probability, data on water flow were collected during the period from 1948 to 2008. Seven probability distributions, namely, Gamma, Weibull, Pearson 6, Rayleigh, Beta, Kumaraswamy and Exponential distributions were tested to model the distribution of the Wadi Bana flows and Kolmogorov-Smirnov, Anderson-Darling and Chi- Squared goodness-of-fit tests were used to evaluate the best fit at 5 % level of significance. It suggests that the best frequency distribution obtained for the Wadi Bana flows in the Abyan Delta of Yemen is Gamma followed by weibull distribution for both summer and autumn seasons.
    [Show full text]
  • Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models
    San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-20-2019 STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS Pratik Patil San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Other Computer Sciences Commons Recommended Citation Patil, Pratik, "STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS" (2019). Master's Projects. 692. DOI: https://doi.org/10.31979/etd.38nc-j52r https://scholarworks.sjsu.edu/etd_projects/692 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS A Project Report Presented to Dr. Ching seh Wu Department of Computer Science San José State University In Partial Fulfillment Of the Requirements for the Class CS 298 By Pratik Patil May 2019 © 2019 Pratik Patil ALL RIGHTS RESERVED The Designated Thesis Committee Approves the Thesis Titled STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS by Pratik Patil APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE SAN JOSÉ STATE UNIVERSITY May 2019 Dr. Ching seh Wu Department of Computer Science Dr. Katerina Potika Department of Computer Science Dr. Marjan Orang Department of Economics ACKNOWLEDGEMENT This has been one long and arduous journey, but nevertheless a worthwhile life experience because of the many great Professors at SJSU and beloved friends.
    [Show full text]
  • The Rockerverse: Packages and Applications for Containerisation
    PREPRINT 1 The Rockerverse: Packages and Applications for Containerisation with R by Daniel Nüst, Dirk Eddelbuettel, Dom Bennett, Robrecht Cannoodt, Dav Clark, Gergely Daróczi, Mark Edmondson, Colin Fay, Ellis Hughes, Lars Kjeldgaard, Sean Lopp, Ben Marwick, Heather Nolis, Jacqueline Nolis, Hong Ooi, Karthik Ram, Noam Ross, Lori Shepherd, Péter Sólymos, Tyson Lee Swetnam, Nitesh Turaga, Charlotte Van Petegem, Jason Williams, Craig Willis, Nan Xiao Abstract The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software. Introduction The R community continues to grow. This can be seen in the number of new packages on CRAN, which is still on growing exponentially (Hornik et al., 2019), but also in the numbers of conferences, open educational resources, meetups, unconferences, and companies that are adopting R, as exemplified by the useR! conference series1, the global growth of the R and R-Ladies user groups2, or the foundation and impact of the R Consortium3.
    [Show full text]
  • Logistic Distribution Becomes Skew to the Right and When P>1 It Becomes Skew to the Left
    FITTING THE VERSATILE LINEARIZED, COMPOSITE, AND GENERALIZED LOGISTIC PROBABILITY DISTRIBUTION TO A DATA SET R.J. Oosterbaan, 06-08-2019. On www.waterlog.info public domain. Abstract The logistic probability distribution can be linearized for easy fitting to a data set using a linear regression to determine the parameters. The logistic probability distribution can also be generalized by raising the data values to the power P that is to be optimized by an iterative method based on the minimization of the squares of the deviations of calculated and observed data values (the least squares or LS method). This enhances its applicability. When P<1 the logistic distribution becomes skew to the right and when P>1 it becomes skew to the left. When P=1, the distribution is symmetrical and quite like the normal probability distribution. In addition, the logistic distribution can be made composite, that is: split into two parts with different parameters. Composite distributions can be used favorably when the data set is obtained under different external conditions influencing its probability characteristics. Contents 1. The standard logistic cumulative distribution function (CDF) and its linearization 2. The logistic cumulative distribution function (CDF) generalized 3. The composite generalized logistic distribution 4. Fitting the standard, generalized, and composite logistic distribution to a data set, available software 5. Construction of confidence belts 6. Constructing histograms and the probability density function (PDF) 7. Ranking according to goodness of fit 8. Conclusion 9. References 1. The standard cumulative distribution function (CDF) and its linearization The cumulative logistic distribution function (CDF) can be written as: Fc = 1 / {1 + e (A*X+B)} where Fc = cumulative logistic distribution function or cumulative frequency e = base of the natural logarithm (Ln), e = 2.71 .
    [Show full text]
  • Thomas Haslwanter with Applications in the Life Sciences
    Statistics and Computing Thomas Haslwanter An Introduction to Statistics with Python With Applications in the Life Sciences Statistics and Computing Series editor W.K. Härdle More information about this series at http://www.springer.com/series/3022 Thomas Haslwanter An Introduction to Statistics with Python With Applications in the Life Sciences 123 Thomas Haslwanter School of Applied Health and Social Sciences University of Applied Sciences Upper Austria Linz, Austria Series Editor: W.K. Härdle C.A.S.E. Centre for Applied Statistics and Economics School of Business and Economics Humboldt-Universität zu Berlin Unter den Linden 6 10099 Berlin Germany The Python code samples accompanying the book are available at www.quantlet.de. All Python programs and data sets can be found on GitHub: https://github.com/thomas- haslwanter/statsintro_python.git. Links to all material are available at http://www.springer. com/de/book/9783319283159. The Python solution codes in the appendix are published under the Creative Commons Attribution-ShareAlike 4.0 International License. ISSN 1431-8784 ISSN 2197-1706 (electronic) Statistics and Computing ISBN 978-3-319-28315-9 ISBN 978-3-319-28316-6 (eBook) DOI 10.1007/978-3-319-28316-6 Library of Congress Control Number: 2016939946 © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
    [Show full text]
  • Recent Outlier Detection Methods with Illustrations Loss Reserving Context
    Recent outlier detection methods with illustrations in loss reserving Benjamin Avanzi, Mark Lavender, Greg Taylor, Bernard Wong School of Risk and Actuarial Studies, UNSW Sydney Insights, 18 September 2017 Recent outlier detection methods with illustrations loss reserving Context Context Reserving Robustness and Outliers Robust Statistical Techniques Robustness criteria Heuristic Tools Robust M-estimation Outlier Detection Techniques Robust Reserving Overview Illustration - Robust Bivariate Chain Ladder Robust N-Dimensional Chain-Ladder Summary and Conclusions References 1/46 Recent outlier detection methods with illustrations loss reserving Context Reserving Context Reserving Robustness and Outliers Robust Statistical Techniques Robustness criteria Heuristic Tools Robust M-estimation Outlier Detection Techniques Robust Reserving Overview Illustration - Robust Bivariate Chain Ladder Robust N-Dimensional Chain-Ladder Summary and Conclusions References 1/46 Recent outlier detection methods with illustrations loss reserving Context Reserving The Reserving Problem i/j 1 2 ··· j ··· I 1 X1;1 X1;2 ··· X1;j ··· X1;J 2 X2;1 X2;2 ··· X2;j ··· . i Xi;1 Xi;2 ··· Xi;j . I XI ;1 Figure: Aggregate claims run-off triangle I Complete the square (or rectangle) I Also - multivariate extensions. 1/46 Recent outlier detection methods with illustrations loss reserving Context Reserving Common Reserving Techniques I Deterministic Chain-Ladder I Stochastic Chain-Ladder (Hachmeister and Stanard, 1975; England and Verrall, 2002) I Mack’s Model (Mack, 1993) I GLMs
    [Show full text]
  • UNIT 1 INTRODUCTION to STATISTICS Introduction to Statistics
    UNIT 1 INTRODUCTION TO STATISTICS Introduction to Statistics Structure 1.0 Introduction 1.1 Objectives 1.2 Meaning of Statistics 1.2.1 Statistics in Singular Sense 1.2.2 Statistics in Plural Sense 1.2.3 Definition of Statistics 1.3 Types of Statistics 1.3.1 On the Basis of Function 1.3.2 On the Basis of Distribution of Data 1.4 Scope and Use of Statistics 1.5 Limitations of Statistics 1.6 Distrust and Misuse of Statistics 1.7 Let Us Sum Up 1.8 Unit End Questions 1.9 Glossary 1.10 Suggested Readings 1.0 INTRODUCTION The word statistics has different meaning to different persons. Knowledge of statistics is applicable in day to day life in different ways. In daily life it means general calculation of items, in railway statistics means the number of trains operating, number of passenger’s freight etc. and so on. Thus statistics is used by people to take decision about the problems on the basis of different type of quantitative and qualitative information available to them. However, in behavioural sciences, the word ‘statistics’ means something different from the common concern of it. Prime function of statistic is to draw statistical inference about population on the basis of available quantitative information. Overall, statistical methods deal with reduction of data to convenient descriptive terms and drawing some inferences from them. This unit focuses on the above aspects of statistics. 1.1 OBJECTIVES After going through this unit, you will be able to: Define the term statistics; Explain the status of statistics; Describe the nature of statistics; State basic concepts used in statistics; and Analyse the uses and misuses of statistics.
    [Show full text]