Skewness Into the Product of Two Normally Distributed Variables and the Risk Consequences
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Use of Proc Iml to Calculate L-Moments for the Univariate Distributional Shape Parameters Skewness and Kurtosis
Statistics 573 USE OF PROC IML TO CALCULATE L-MOMENTS FOR THE UNIVARIATE DISTRIBUTIONAL SHAPE PARAMETERS SKEWNESS AND KURTOSIS Michael A. Walega Berlex Laboratories, Wayne, New Jersey Introduction Exploratory data analysis statistics, such as those Gaussian. Bickel (1988) and Van Oer Laan and generated by the sp,ge procedure PROC Verdooren (1987) discuss the concept of robustness UNIVARIATE (1990), are useful tools to characterize and how it pertains to the assumption of normality. the underlying distribution of data prior to more rigorous statistical analyses. Assessment of the As discussed by Glass et al. (1972), incorrect distributional shape of data is usually accomplished conclusions may be reached when the normality by careful examination of the values of the third and assumption is not valid, especially when one-tail tests fourth central moments, skewness and kurtosis. are employed or the sample size or significance level However, when the sample size is small or the are very small. Hopkins and Weeks (1990) also underlying distribution is non-normal, the information discuss the effects of highly non-normal data on obtained from the sample skewness and kurtosis can hypothesis testing of variances. Thus, it is apparent be misleading. that examination of the skewness (departure from symmetry) and kurtosis (deviation from a normal One alternative to the central moment shape statistics curve) is an important component of exploratory data is the use of linear combinations of order statistics (L analyses. moments) to examine the distributional shape characteristics of data. L-moments have several Various methods to estimate skewness and kurtosis theoretical advantages over the central moment have been proposed (MacGillivray and Salanela, shape statistics: Characterization of a wider range of 1988). -
A Skew Extension of the T-Distribution, with Applications
J. R. Statist. Soc. B (2003) 65, Part 1, pp. 159–174 A skew extension of the t-distribution, with applications M. C. Jones The Open University, Milton Keynes, UK and M. J. Faddy University of Birmingham, UK [Received March 2000. Final revision July 2002] Summary. A tractable skew t-distribution on the real line is proposed.This includes as a special case the symmetric t-distribution, and otherwise provides skew extensions thereof.The distribu- tion is potentially useful both for modelling data and in robustness studies. Properties of the new distribution are presented. Likelihood inference for the parameters of this skew t-distribution is developed. Application is made to two data modelling examples. Keywords: Beta distribution; Likelihood inference; Robustness; Skewness; Student’s t-distribution 1. Introduction Student’s t-distribution occurs frequently in statistics. Its usual derivation and use is as the sam- pling distribution of certain test statistics under normality, but increasingly the t-distribution is being used in both frequentist and Bayesian statistics as a heavy-tailed alternative to the nor- mal distribution when robustness to possible outliers is a concern. See Lange et al. (1989) and Gelman et al. (1995) and references therein. It will often be useful to consider a further alternative to the normal or t-distribution which is both heavy tailed and skew. To this end, we propose a family of distributions which includes the symmetric t-distributions as special cases, and also includes extensions of the t-distribution, still taking values on the whole real line, with non-zero skewness. Let a>0 and b>0be parameters. -
A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess
S S symmetry Article A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess Guillermo Martínez-Flórez 1, Víctor Leiva 2,* , Emilio Gómez-Déniz 3 and Carolina Marchant 4 1 Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 14014, Colombia; [email protected] 2 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile 3 Facultad de Economía, Empresa y Turismo, Universidad de Las Palmas de Gran Canaria and TIDES Institute, 35001 Canarias, Spain; [email protected] 4 Facultad de Ciencias Básicas, Universidad Católica del Maule, 3466706 Talca, Chile; [email protected] * Correspondence: [email protected] or [email protected] Received: 30 June 2020; Accepted: 19 August 2020; Published: 1 September 2020 Abstract: In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal. Keywords: beta distribution; centered skew-normal distribution; maximum-likelihood methods; Monte Carlo simulations; proportions; R software; rates; zero/one inflated data 1. -
Chapter 6: Random Errors in Chemical Analysis
Chapter 6: Random Errors in Chemical Analysis Source slideplayer.com/Fundamentals of Analytical Chemistry, F.J. Holler, S.R.Crouch Random errors are present in every measurement no matter how careful the experimenter. Random, or indeterminate, errors can never be totally eliminated and are often the major source of uncertainty in a determination. Random errors are caused by the many uncontrollable variables that accompany every measurement. The accumulated effect of the individual uncertainties causes replicate results to fluctuate randomly around the mean of the set. In this chapter, we consider the sources of random errors, the determination of their magnitude, and their effects on computed results of chemical analyses. We also introduce the significant figure convention and illustrate its use in reporting analytical results. 6A The nature of random errors - random error in the results of analysts 2 and 4 is much larger than that seen in the results of analysts 1 and 3. - The results of analyst 3 show outstanding precision but poor accuracy. The results of analyst 1 show excellent precision and good accuracy. Figure 6-1 A three-dimensional plot showing absolute error in Kjeldahl nitrogen determinations for four different analysts. Random Error Sources - Small undetectable uncertainties produce a detectable random error in the following way. - Imagine a situation in which just four small random errors combine to give an overall error. We will assume that each error has an equal probability of occurring and that each can cause the final result to be high or low by a fixed amount ±U. - Table 6.1 gives all the possible ways in which four errors can combine to give the indicated deviations from the mean value. -
An Approach for Appraising the Accuracy of Suspended-Sediment Data
An Approach for Appraising the Accuracy of Suspended-sediment Data U.S. GEOLOGICAL SURVEY PROFESSIONAL PAl>£R 1383 An Approach for Appraising the Accuracy of Suspended-sediment Data By D. E. BURKHAM U.S. GEOLOGICAL SURVEY PROFESSIONAL PAPER 1333 UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON, 1985 DEPARTMENT OF THE INTERIOR DONALD PAUL MODEL, Secretary U.S. GEOLOGICAL SURVEY Dallas L. Peck, Director First printing 1985 Second printing 1987 For sale by the Books and Open-File Reports Section, U.S. Geological Survey, Federal Center, Box 25425, Denver, CO 80225 CONTENTS Page Page Abstract ........... 1 Spatial error Continued Introduction ....... 1 Application of method ................................ 11 Problem ......... 1 Basic data ......................................... 11 Purpose and scope 2 Standard spatial error for multivertical procedure .... 11 Sampling error .......................................... 2 Standard spatial error for single-vertical procedure ... 13 Discussion of error .................................... 2 Temporal error ......................................... 13 Approach to solution .................................. 3 Discussion of error ................................... 13 Application of method ................................. 4 Approach to solution ................................. 14 Basic data .......................................... 4 Application of method ................................ 14 Standard sampling error ............................. 4 Basic data ........................................ -
Approximating the Distribution of the Product of Two Normally Distributed Random Variables
S S symmetry Article Approximating the Distribution of the Product of Two Normally Distributed Random Variables Antonio Seijas-Macías 1,2 , Amílcar Oliveira 2,3 , Teresa A. Oliveira 2,3 and Víctor Leiva 4,* 1 Departamento de Economía, Universidade da Coruña, 15071 A Coruña, Spain; [email protected] 2 CEAUL, Faculdade de Ciências, Universidade de Lisboa, 1649-014 Lisboa, Portugal; [email protected] (A.O.); [email protected] (T.A.O.) 3 Departamento de Ciências e Tecnologia, Universidade Aberta, 1269-001 Lisboa, Portugal 4 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile * Correspondence: [email protected] or [email protected] Received: 21 June 2020; Accepted: 18 July 2020; Published: 22 July 2020 Abstract: The distribution of the product of two normally distributed random variables has been an open problem from the early years in the XXth century. First approaches tried to determinate the mathematical and statistical properties of the distribution of such a product using different types of functions. Recently, an improvement in computational techniques has performed new approaches for calculating related integrals by using numerical integration. Another approach is to adopt any other distribution to approximate the probability density function of this product. The skew-normal distribution is a generalization of the normal distribution which considers skewness making it flexible. In this work, we approximate the distribution of the product of two normally distributed random variables using a type of skew-normal distribution. The influence of the parameters of the two normal distributions on the approximation is explored. When one of the normally distributed variables has an inverse coefficient of variation greater than one, our approximation performs better than when both normally distributed variables have inverse coefficients of variation less than one. -
Analysis of the Coefficient of Variation in Shear and Tensile Bond Strength Tests
J Appl Oral Sci 2005; 13(3): 243-6 www.fob.usp.br/revista or www.scielo.br/jaos ANALYSIS OF THE COEFFICIENT OF VARIATION IN SHEAR AND TENSILE BOND STRENGTH TESTS ANÁLISE DO COEFICIENTE DE VARIAÇÃO EM TESTES DE RESISTÊNCIA DA UNIÃO AO CISALHAMENTO E TRAÇÃO Fábio Lourenço ROMANO1, Gláucia Maria Bovi AMBROSANO1, Maria Beatriz Borges de Araújo MAGNANI1, Darcy Flávio NOUER1 1- MSc, Assistant Professor, Department of Orthodontics, Alfenas Pharmacy and Dental School – Efoa/Ceufe, Minas Gerais, Brazil. 2- DDS, MSc, Associate Professor of Biostatistics, Department of Social Dentistry, Piracicaba Dental School – UNICAMP, São Paulo, Brazil; CNPq researcher. 3- DDS, MSc, Assistant Professor of Orthodontics, Department of Child Dentistry, Piracicaba Dental School – UNICAMP, São Paulo, Brazil. 4- DDS, MSc, Full Professor of Orthodontics, Department of Child Dentistry, Piracicada Dental School – UNICAMP, São Paulo, Brazil. Corresponding address: Fábio Lourenço Romano - Avenida do Café, 131 Bloco E, Apartamento 16 - Vila Amélia - Ribeirão Preto - SP Cep.: 14050-230 - Phone: (16) 636 6648 - e-mail: [email protected] Received: July 29, 2004 - Modification: September 09, 2004 - Accepted: June 07, 2005 ABSTRACT The coefficient of variation is a dispersion measurement that does not depend on the unit scales, thus allowing the comparison of experimental results involving different variables. Its calculation is crucial for the adhesive experiments performed in laboratories because both precision and reliability can be verified. The aim of this study was to evaluate and to suggest a classification of the coefficient variation (CV) for in vitro experiments on shear and tensile strengths. The experiments were performed in laboratory by fifty international and national studies on adhesion materials. -
Convergence in Distribution Central Limit Theorem
Convergence in Distribution Central Limit Theorem Statistics 110 Summer 2006 Copyright °c 2006 by Mark E. Irwin Convergence in Distribution Theorem. Let X » Bin(n; p) and let ¸ = np, Then µ ¶ n e¡¸¸x lim P [X = x] = lim px(1 ¡ p)n¡x = n!1 n!1 x x! So when n gets large, we can approximate binomial probabilities with Poisson probabilities. Proof. µ ¶ µ ¶ µ ¶ µ ¶ n n ¸ x ¸ n¡x lim px(1 ¡ p)n¡x = lim 1 ¡ n!1 x n!1 x n n µ ¶ µ ¶ µ ¶ n! 1 ¸ ¡x ¸ n = ¸x 1 ¡ 1 ¡ x!(n ¡ x)! nx n n Convergence in Distribution 1 µ ¶ µ ¶ µ ¶ n! 1 ¸ ¡x ¸ n = ¸x 1 ¡ 1 ¡ x!(n ¡ x)! nx n n µ ¶ ¸x n! 1 ¸ n = lim 1 ¡ x! n!1 (n ¡ x)!(n ¡ ¸)x n | {z } | {z } !1 !e¡¸ e¡¸¸x = x! 2 Note that approximation works better when n is large and p is small as can been seen in the following plot. If p is relatively large, a di®erent approximation should be used. This is coming later. (Note in the plot, bars correspond to the true binomial probabilities and the red circles correspond to the Poisson approximation.) Convergence in Distribution 2 lambda = 1 n = 10 p = 0.1 lambda = 1 n = 50 p = 0.02 lambda = 1 n = 200 p = 0.005 p(x) p(x) p(x) 0.0 0.1 0.2 0.3 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 x x x lambda = 5 n = 10 p = 0.5 lambda = 5 n = 50 p = 0.1 lambda = 5 n = 200 p = 0.025 p(x) p(x) p(x) 0.00 0.05 0.10 0.15 0.20 0.00 0.05 0.10 0.15 0.00 0.05 0.10 0.15 0 1 2 3 4 5 6 7 8 9 0 2 4 6 8 10 12 0 2 4 6 8 10 12 x x x Convergence in Distribution 3 Example: Let Y1;Y2;::: be iid Exp(1). -
"The Distribution of Mckay's Approximation for the Coefficient Of
ARTICLE IN PRESS Statistics & Probability Letters 78 (2008) 10–14 www.elsevier.com/locate/stapro The distribution of McKay’s approximation for the coefficient of variation Johannes Forkmana,Ã, Steve Verrillb aDepartment of Biometry and Engineering, Swedish University of Agricultural Sciences, P.O. Box 7032, SE-750 07 Uppsala, Sweden bU.S.D.A., Forest Products Laboratory, 1 Gifford Pinchot Drive, Madison, WI 53726, USA Received 12 July 2006; received in revised form 21 February 2007; accepted 10 April 2007 Available online 7 May 2007 Abstract McKay’s chi-square approximation for the coefficient of variation is type II noncentral beta distributed and asymptotically normal with mean n À 1 and variance smaller than 2ðn À 1Þ. r 2007 Elsevier B.V. All rights reserved. Keywords: Coefficient of variation; McKay’s approximation; Noncentral beta distribution 1. Introduction The coefficient of variation is defined as the standard deviation divided by the mean. This measure, which is commonly expressed as a percentage, is widely used since it is often necessary to relate the size of the variation to the level of the observations. McKay (1932) introduced a w2 approximation for the coefficient of variation calculated on normally distributed observations. It can be defined in the following way. Definition 1. Let yj, j ¼ 1; ...; n; be n independent observations from a normal distribution with expected value m and variance s2. Let g denote the population coefficient of variation, i.e. g ¼ s=m, and let c denote the sample coefficient of variation, i.e. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 1 u 1 Xn 1 Xn c ¼ t ðy À mÞ2; m ¼ y . -
Robust Statistics in BD Facsdiva™ Software
BD Biosciences Tech Note June 2012 Robust Statistics in BD FACSDiva™ Software Robust Statistics in BD FACSDiva™ Software Robust statistics defined Robust statistics provide an alternative approach to classical statistical estimators such as mean, standard deviation (SD), and percent coefficient of variation (%CV). These alternative procedures are more resistant to the statistical influences of outlying events in a sample population—hence the term “robust.” Real data sets often contain gross outliers, and it is impractical to systematically attempt to remove all outliers by gating procedures or other rule sets. The robust equivalent of the mean statistic is the median. The robust SD is designated rSD and the percent robust CV is designated %rCV. For perfectly normal distributions, classical and robust statistics give the same results. How robust statistics are calculated in BD FACSDiva™ software Median The mean, or average, is the sum of all the values divided by the number of values. For example, the mean of the values of [13, 10, 11, 12, 114] is 160 ÷ 5 = 32. If the outlier value of 114 is excluded, the mean is 11.5. The median is defined as the midpoint, or the 50th percentile of the values. It is the statistical center of the population. Half the values should be above the median and half below. In the previous example, the median is 12 (13 and 114 are above; 10 and 11 are below). Note that this is close to the mean with the outlier excluded. Robust standard deviation (rSD) The classical SD is a function of the deviation of individual data points to the mean of the population. -
Correct Use of Percent Coefficient of Variation (%CV) Formula for Log-Transformed Data
MOJ Proteomics & Bioinformatics Short Communication Open Access Correct use of percent coefficient of variation (%CV) formula for log-transformed data Abstract Volume 6 Issue 4 - 2017 The coefficient of variation (CV) is a unit less measure typically used to evaluate the Jesse A Canchola, Shaowu Tang, Pari Hemyari, variability of a population relative to its standard deviation and is normally presented as a percentage.1 When considering the percent coefficient of variation (%CV) for Ellen Paxinos, Ed Marins Roche Molecular Systems, Inc., USA log-transformed data, we have discovered the incorrect application of the standard %CV form in obtaining the %CV for log-transformed data. Upon review of various Correspondence: Jesse A Canchola, Roche Molecular Systems, journals, we have noted the formula for the %CV for log-transformed data was not Inc., 4300 Hacienda Drive, Pleasanton, CA 94588, USA, being applied correctly. This communication provides a framework from which the Email [email protected] correct mathematical formula for the %CV can be applied to log-transformed data. Received: October 30, 2017 | Published: November 16, 2017 Keywords: coefficient of variation, log-transformation, variances, statistical technique Abbreviations: CV, coefficient of variation; %CV, CV x 100% and variance of the transformation. If the untransformed %CV is used on log-normal data, the resulting Introduction %CV will be too small and give an overly optimistic, but incorrect, i. The percent coefficient of variation, %CV, is a unit less measure of view of the performance of the measured device. variation and can be considered as a “relative standard deviation” For example, Hatzakis et al.,1 Table 1, showed an assessment of since it is defined as the standard deviation divided by the mean inter-instrument, inter-operator, inter-day, inter-run, intra-run and multiplied by 100 percent: total variability of the Aptima HIV-1 Quant Dx in various HIV-1 RNA σ concentrations. -
A Class of Mean Field Interaction Models for Computer and Communication Systems
Technical Report LCA-REPORT-2008-010, May 2009 Version with bug fixes of full text published in Performance Evaluation, Vol. 65, Nr. 11-12, pp. 823-838, 2008. A Class Of Mean Field Interaction Models for Computer and Communication Systems Michel Bena¨ım and Jean-Yves Le Boudec Abstract We consider models of N interacting objects, where the interaction is via a common re- source and the distribution of states of all objects. We introduce the key scaling concept of intensity; informally, the expected number of transitions per object per time slot is of the order of the intensity. We consider the case of vanishing intensity, i.e. the expected number of object transitions per time slot is o(N). We show that, under mild assumptions and for large N, the occupancy measure converges, in mean square (and thus in probability) over any finite horizon, to a deterministic dynamical system. The mild assumption is essentially that the coefficient of variation of the number of object transitions per time slot remains bounded with N. No independence assumption is needed anywhere. The convergence re- sults allow us to derive properties valid in the stationary regime. We discuss when one can assure that a stationary point of the ODE is the large N limit of the stationary probability distribution of the state of one object for the system with N objects. We use this to develop a critique of the fixed point method sometimes used in conjunction with the decoupling assumption. Key words: Mean Field Interaction Model, Markov Chain, Dynamical System, MAC protocol, Reputation Systems, Game Theory, Decoupling Assumption, Fixed Point, Bianchi’s Formula 1 Introduction We consider models of objects interacting in discrete time, where objects are ob- servable only through their state.