Procedures for Estimation of Weibull Parameters James W
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A New Parameter Estimator for the Generalized Pareto Distribution Under the Peaks Over Threshold Framework
mathematics Article A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework Xu Zhao 1,*, Zhongxian Zhang 1, Weihu Cheng 1 and Pengyue Zhang 2 1 College of Applied Sciences, Beijing University of Technology, Beijing 100124, China; [email protected] (Z.Z.); [email protected] (W.C.) 2 Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA; [email protected] * Correspondence: [email protected] Received: 1 April 2019; Accepted: 30 April 2019 ; Published: 7 May 2019 Abstract: Techniques used to analyze exceedances over a high threshold are in great demand for research in economics, environmental science, and other fields. The generalized Pareto distribution (GPD) has been widely used to fit observations exceeding the tail threshold in the peaks over threshold (POT) framework. Parameter estimation and threshold selection are two critical issues for threshold-based GPD inference. In this work, we propose a new GPD-based estimation approach by combining the method of moments and likelihood moment techniques based on the least squares concept, in which the shape and scale parameters of the GPD can be simultaneously estimated. To analyze extreme data, the proposed approach estimates the parameters by minimizing the sum of squared deviations between the theoretical GPD function and its expectation. Additionally, we introduce a recently developed stopping rule to choose the suitable threshold above which the GPD asymptotically fits the exceedances. Simulation studies show that the proposed approach performs better or similar to existing approaches, in terms of bias and the mean square error, in estimating the shape parameter. -
On the Scale Parameter of Exponential Distribution
Review of the Air Force Academy No.2 (34)/2017 ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION Anca Ileana LUPAŞ Military Technical Academy, Bucharest, Romania ([email protected]) DOI: 10.19062/1842-9238.2017.15.2.16 Abstract: Exponential distribution is one of the widely used continuous distributions in various fields for statistical applications. In this paper we study the exact and asymptotical distribution of the scale parameter for this distribution. We will also define the confidence intervals for the studied parameter as well as the fixed length confidence intervals. 1. INTRODUCTION Exponential distribution is used in various statistical applications. Therefore, we often encounter exponential distribution in applications such as: life tables, reliability studies, extreme values analysis and others. In the following paper, we focus our attention on the exact and asymptotical repartition of the exponential distribution scale parameter estimator. 2. SCALE PARAMETER ESTIMATOR OF THE EXPONENTIAL DISTRIBUTION We will consider the random variable X with the following cumulative distribution function: x F(x ; ) 1 e ( x 0 , 0) (1) where is an unknown scale parameter Using the relationships between MXXX( ) ; 22( ) ; ( ) , we obtain ()X a theoretical variation coefficient 1. This is a useful indicator, especially if MX() you have observational data which seems to be exponential and with variation coefficient of the selection closed to 1. If we consider x12, x ,... xn as a part of a population that follows an exponential distribution, then by using the maximum likelihood estimation method we obtain the following estimate n ˆ 1 xi (2) n i1 119 On the Scale Parameter of Exponential Distribution Since M ˆ , it follows that ˆ is an unbiased estimator for . -
Estimation of Common Location and Scale Parameters in Nonregular Cases Ahmad Razmpour Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1982 Estimation of common location and scale parameters in nonregular cases Ahmad Razmpour Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Statistics and Probability Commons Recommended Citation Razmpour, Ahmad, "Estimation of common location and scale parameters in nonregular cases " (1982). Retrospective Theses and Dissertations. 7528. https://lib.dr.iastate.edu/rtd/7528 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This reproduction was made from a copy of a document sent to us for microfilming. While the most advanced technology has been used to photograph and reproduce this document, the quality of the reproduction is heavily dependent upon the quality of the material submitted. The following explanation of techniques is provided to help clarify markings or notations which 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 complete continuity. 2. When an image on the film is obliterated with a round black mark, it is an indication of either blurred copy because of movement during exposure, duplicate copy, or copyrighted materials that should not have been filmed. -
A Comparison of Unbiased and Plottingposition Estimators of L
WATER RESOURCES RESEARCH, VOL. 31, NO. 8, PAGES 2019-2025, AUGUST 1995 A comparison of unbiased and plotting-position estimators of L moments J. R. M. Hosking and J. R. Wallis IBM ResearchDivision, T. J. Watson ResearchCenter, Yorktown Heights, New York Abstract. Plotting-positionestimators of L momentsand L moment ratios have several disadvantagescompared with the "unbiased"estimators. For generaluse, the "unbiased'? estimatorsshould be preferred. Plotting-positionestimators may still be usefulfor estimatingextreme upper tail quantilesin regional frequencyanalysis. Probability-Weighted Moments and L Moments •r+l-" (--1)r • P*r,k Olk '- E p *r,!•[J!•. Probability-weightedmoments of a randomvariable X with k=0 k=0 cumulativedistribution function F( ) and quantile function It is convenient to define dimensionless versions of L mo- x( ) were definedby Greenwoodet al. [1979]to be the quan- tities ments;this is achievedby dividingthe higher-orderL moments by the scale measure h2. The L moment ratios •'r, r = 3, Mp,ra= E[XP{F(X)}r{1- F(X)} s] 4, '", are definedby ßr-" •r/•2 ß {X(u)}PUr(1 -- U)s du. L momentratios measure the shapeof a distributionindepen- dently of its scaleof measurement.The ratios *3 ("L skew- ness")and *4 ("L kurtosis")are nowwidely used as measures Particularlyuseful specialcases are the probability-weighted of skewnessand kurtosis,respectively [e.g., Schaefer,1990; moments Pilon and Adamowski,1992; Royston,1992; Stedingeret al., 1992; Vogeland Fennessey,1993]. 12•r= M1,0, r = •01 (1 - u)rx(u) du, Estimators Given an ordered sample of size n, Xl: n • X2:n • ''' • urx(u) du. X.... there are two establishedways of estimatingthe proba- /3r--- Ml,r, 0 =f01 bility-weightedmoments and L moments of the distribution from whichthe samplewas drawn. -
A Study of Non-Central Skew T Distributions and Their Applications in Data Analysis and Change Point Detection
A STUDY OF NON-CENTRAL SKEW T DISTRIBUTIONS AND THEIR APPLICATIONS IN DATA ANALYSIS AND CHANGE POINT DETECTION Abeer M. Hasan A Dissertation Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY August 2013 Committee: Arjun K. Gupta, Co-advisor Wei Ning, Advisor Mark Earley, Graduate Faculty Representative Junfeng Shang. Copyright c August 2013 Abeer M. Hasan All rights reserved iii ABSTRACT Arjun K. Gupta, Co-advisor Wei Ning, Advisor Over the past three decades there has been a growing interest in searching for distribution families that are suitable to analyze skewed data with excess kurtosis. The search started by numerous papers on the skew normal distribution. Multivariate t distributions started to catch attention shortly after the development of the multivariate skew normal distribution. Many researchers proposed alternative methods to generalize the univariate t distribution to the multivariate case. Recently, skew t distribution started to become popular in research. Skew t distributions provide more flexibility and better ability to accommodate long-tailed data than skew normal distributions. In this dissertation, a new non-central skew t distribution is studied and its theoretical properties are explored. Applications of the proposed non-central skew t distribution in data analysis and model comparisons are studied. An extension of our distribution to the multivariate case is presented and properties of the multivariate non-central skew t distri- bution are discussed. We also discuss the distribution of quadratic forms of the non-central skew t distribution. In the last chapter, the change point problem of the non-central skew t distribution is discussed under different settings. -
The Asymmetric T-Copula with Individual Degrees of Freedom
The Asymmetric t-Copula with Individual Degrees of Freedom d-fine GmbH Christ Church University of Oxford A thesis submitted for the degree of MSc in Mathematical Finance Michaelmas 2012 Abstract This thesis investigates asymmetric dependence structures of multivariate asset returns. Evidence of such asymmetry for equity returns has been reported in the literature. In order to model the dependence structure, a new t-copula approach is proposed called the skewed t-copula with individ- ual degrees of freedom (SID t-copula). This copula provides the flexibility to assign an individual degree-of-freedom parameter and an individual skewness parameter to each asset in a multivariate setting. Applying this approach to GARCH residuals of bivariate equity index return data and using maximum likelihood estimation, we find significant asymmetry. By means of the Akaike information criterion, it is demonstrated that the SID t-copula provides the best model for the market data compared to other copula approaches without explicit asymmetry parameters. In addition, it yields a better fit than the conventional skewed t-copula with a single degree-of-freedom parameter. In a model impact study, we analyse the errors which can occur when mod- elling asymmetric multivariate SID-t returns with the symmetric multi- variate Gauss or standard t-distribution. The comparison is done in terms of the risk measures value-at-risk and expected shortfall. We find large deviations between the modelled and the true VaR/ES of a spread posi- tion composed of asymmetrically distributed risk factors. Going from the bivariate case to a larger number of risk factors, the model errors increase. -
On the Meaning and Use of Kurtosis
Psychological Methods Copyright 1997 by the American Psychological Association, Inc. 1997, Vol. 2, No. 3,292-307 1082-989X/97/$3.00 On the Meaning and Use of Kurtosis Lawrence T. DeCarlo Fordham University For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurtosis is illustrated with well-known distributions, and aspects of its interpretation and misinterpretation are discussed. The role of kurtosis in testing univariate and multivariate normality; as a measure of departures from normality; in issues of robustness, outliers, and bimodality; in generalized tests and estimators, as well as limitations of and alternatives to the kurtosis measure [32, are discussed. It is typically noted in introductory statistics standard deviation. The normal distribution has a kur- courses that distributions can be characterized in tosis of 3, and 132 - 3 is often used so that the refer- terms of central tendency, variability, and shape. With ence normal distribution has a kurtosis of zero (132 - respect to shape, virtually every textbook defines and 3 is sometimes denoted as Y2)- A sample counterpart illustrates skewness. On the other hand, another as- to 132 can be obtained by replacing the population pect of shape, which is kurtosis, is either not discussed moments with the sample moments, which gives or, worse yet, is often described or illustrated incor- rectly. Kurtosis is also frequently not reported in re- ~(X i -- S)4/n search articles, in spite of the fact that virtually every b2 (•(X i - ~')2/n)2' statistical package provides a measure of kurtosis. -
A Multivariate Student's T-Distribution
Open Journal of Statistics, 2016, 6, 443-450 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ojs http://dx.doi.org/10.4236/ojs.2016.63040 A Multivariate Student’s t-Distribution Daniel T. Cassidy Department of Engineering Physics, McMaster University, Hamilton, ON, Canada Received 29 March 2016; accepted 14 June 2016; published 17 June 2016 Copyright © 2016 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract A multivariate Student’s t-distribution is derived by analogy to the derivation of a multivariate normal (Gaussian) probability density function. This multivariate Student’s t-distribution can have different shape parameters νi for the marginal probability density functions of the multi- variate distribution. Expressions for the probability density function, for the variances, and for the covariances of the multivariate t-distribution with arbitrary shape parameters for the marginals are given. Keywords Multivariate Student’s t, Variance, Covariance, Arbitrary Shape Parameters 1. Introduction An expression for a multivariate Student’s t-distribution is presented. This expression, which is different in form than the form that is commonly used, allows the shape parameter ν for each marginal probability density function (pdf) of the multivariate pdf to be different. The form that is typically used is [1] −+ν Γ+((ν n) 2) T ( n) 2 +Σ−1 n 2 (1.[xx] [ ]) (1) ΓΣ(νν2)(π ) This “typical” form attempts to generalize the univariate Student’s t-distribution and is valid when the n marginal distributions have the same shape parameter ν . -
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. -
Generalized Inferences for the Common Scale Parameter of Several Pareto Populations∗
InternationalInternational JournalJournal of of Statistics Statistics and and Management Management System System Vol.Vol. 4 No. 12 (January-June,(July-December, 2019) 2019) International Journal of Statistics and Management System, 2010, Vol. 5, No. 1–2, pp. 118–126. c 2010 Serials Publications Generalized inferences for the common scale parameter of several Pareto populations∗ Sumith Gunasekera†and Malwane M. A. Ananda‡ Received: 13th August 2018 Revised: 24th December 2018 Accepted: 10th March 2019 Abstract A problem of interest in this article is statistical inferences concerning the com- mon scale parameter of several Pareto distributions. Using the generalized p-value approach, exact confidence intervals and the exact tests for testing the common scale parameter are given. Examples are given in order to illustrate our procedures. A limited simulation study is given to demonstrate the performance of the proposed procedures. 1 Introduction In this paper, we consider k (k ≥ 2) independent Pareto distributions with an unknown common scale parameter θ (sometimes referred to as the “location pa- rameter” and also as the “truncation parameter”) and unknown possibly unequal shape parameters αi’s (i = 1, 2, ..., k). Using the generalized variable approach (Tsui and Weer- ahandi [8]), we construct an exact test for testing θ. Furthermore, using the generalized confidence interval (Weerahandi [11]), we construct an exact confidence interval for θ as well. A limited simulation study was carried out to compare the performance of these gen- eralized procedures with the approximate procedures based on the large sample method as well as with the other test procedures based on the combination of p-values. -
A Bayesian Hierarchical Spatial Copula Model: an Application to Extreme Temperatures in Extremadura (Spain)
atmosphere Article A Bayesian Hierarchical Spatial Copula Model: An Application to Extreme Temperatures in Extremadura (Spain) J. Agustín García 1,† , Mario M. Pizarro 2,*,† , F. Javier Acero 1,† and M. Isabel Parra 2,† 1 Departamento de Física, Universidad de Extremadura, Avenida de Elvas, 06006 Badajoz, Spain; [email protected] (J.A.G.); [email protected] (F.J.A.) 2 Departamento de Matemáticas, Universidad de Extremadura, Avenida de Elvas, 06006 Badajoz, Spain; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s Citation: García, J.A.; Pizarro, M.M.; location and scale parameters than when the scale parameter is taken to be constant over the region. Acero, F.J.; Parra, M.I. A Bayesian Hierarchical Spatial Copula Model: Keywords: Bayesian hierarchical model; extreme temperature; Gaussian copula; generalized extreme An Application to Extreme value distribution Temperatures in Extremadura (Spain). -
A Study of Ocean Wave Statistical Properties Using SNOW
A study of ocean wave statistical properties using nonlinear, directional, phase- resolved ocean wavefield simulations Legena Henry PhD Candidate Course 2 – OE [email protected] abstract We study the statistics of wavefields obtained from phase-resolved simulations that are non-linear (up to the third order in surface potential). We model wave-wave interactions based on the fully non- linear Zakharov equations. We vary the simulated wavefield's input spectral properties: peak shape parameter, Phillips parameter and Directional Spreading Function. We then investigate the relationships between these input spectral properties and output physical properties via statistical analysis. We investigate: 1. Surface elevation distribution in response to input spectral properties, 2. Wave definition methods in an irregular wavefield with a two- dimensional wave number, 3. Wave height/wavelength distributions in response to input spectral properties, including the occurrence and spacing of large wave events (based on definitions in 2). 1. Surface elevation distribution in response to input spectral properties: – Peak Shape Parameter – Phillips' Parameter – Directional Spreading Function 1. Surface elevation distribution in response to input spectral properties: Peak shape parameter Peak shape parameter produces higher impact on even moments of surface elevation, (i.e. surface elevation kurtosis and surface elevation variance) than on the observed odd moments (skewness) of surface elevation. The highest elevations in a wavefield with mean peak shape parameter, 3.3 are more stable than those in wavefields with mean peak shape parameter, 1.0, and 5.0 We find surface elevation kurtosis in non-linear wavefields is much smaller than surface elevation slope kurtosis. We also see that higher values of peak shape parameter produce higher kurtosis of surface slope in the mean direction of propagation.