L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES

Similar documents
Professor Wei Zhu. 1. Sampling from the Normal Population

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

The Exponentiated Lomax Distribution: Different Estimation Methods

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

Best Linear Unbiased Estimators of the Three Parameter Gamma Distribution using doubly Type-II censoring

2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx

Chapter 7 Varying Probability Sampling

A New Approach to Moments Inequalities for NRBU and RNBU Classes With Hypothesis Testing Applications

Lecture 10: Condensed matter systems

Recent Advances in Computers, Communications, Applied Social Science and Mathematics

THREE-PARAMETRIC LOGNORMAL DISTRIBUTION AND ESTIMATING ITS PARAMETERS USING THE METHOD OF L-MOMENTS

A DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES

Record Values from Size-Biased Pareto Distribution and a Characterization

Estimation of Population Mean with. a New Imputation Method

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

Module Title: Business Mathematics and Statistics 2

Estimation of Parameters of the Exponential Geometric Distribution with Presence of Outliers Generated from Uniform Distribution

XII. Addition of many identical spins

X ε ) = 0, or equivalently, lim

Exponentiated Lomax Geometric Distribution: Properties and Applications

= y and Normed Linear Spaces

CHAPTER VI Statistical Analysis of Experimental Data

SUBSEQUENCE CHARACTERIZAT ION OF UNIFORM STATISTICAL CONVERGENCE OF DOUBLE SEQUENCE

University of Pavia, Pavia, Italy. North Andover MA 01845, USA

Sandwich Theorems for Mcshane Integration

χ be any function of X and Y then

Distribution of Geometrically Weighted Sum of Bernoulli Random Variables

FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING

#A42 INTEGERS 16 (2016) THE SUM OF BINOMIAL COEFFICIENTS AND INTEGER FACTORIZATION

The Mathematical Appendix

Trace of Positive Integer Power of Adjacency Matrix

Inequalities for Dual Orlicz Mixed Quermassintegrals.

RANDOM SYSTEMS WITH COMPLETE CONNECTIONS AND THE GAUSS PROBLEM FOR THE REGULAR CONTINUED FRACTIONS

ON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE

Chapter 5 Properties of a Random Sample

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index

Robust Regression Analysis for Non-Normal Situations under Symmetric Distributions Arising In Medical Research

On Five-Parameter Lomax Distribution: Properties and Applications

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

Bayesian Nonlinear Regression Models based on Slash Skew-t Distribution

Chapter 5 Properties of a Random Sample

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Summary of the lecture in Biostatistics

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Random Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois

STK4011 and STK9011 Autumn 2016

In the classical period up to the 1980 s, research on regression

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial Lindley Distribution

Exponential Generating Functions - J. T. Butler

Multistage Median Ranked Set Sampling for Estimating the Population Median

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Point Estimation: definition of estimators

D KL (P Q) := p i ln p i q i

BASIC PRINCIPLES OF STATISTICS

FUZZY MULTINOMIAL CONTROL CHART WITH VARIABLE SAMPLE SIZE

Chapter 2 Probability and Stochastic Processes

UNIQUENESS IN SEALED HIGH BID AUCTIONS. Eric Maskin and John Riley. Last Revision. December 14, 1996**

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER

Fairing of Parametric Quintic Splines

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Computation of the Multivariate Normal Integral over a Complex Subspace

NUMERICAL SIMULATION OF TSUNAMI CURRENTS AROUND MOVING STRUCTURES

Probability. Stochastic Processes

Analysis of Variance with Weibull Data

Median as a Weighted Arithmetic Mean of All Sample Observations

SOME ARITHMETIC PROPERTIES OF OVERPARTITION K -TUPLES

Hyper-wiener index of gear fan and gear wheel related graph

Continuous Distributions

Chain Rules for Entropy

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

The physics of wedge diffraction: A model in terms of elementary diffracted waves

Minimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses

Allocations for Heterogenous Distributed Storage

Randomly Weighted Averages on Order Statistics

An Unconstrained Q - G Programming Problem and its Application

INTRODUCTION TO QUEUING MODELS

ˆ SSE SSE q SST R SST R q R R q R R q

NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS

Learning Bayesian belief networks

2. Independence and Bernoulli Trials

Non-uniform Turán-type problems

JOURNAL OF MATH SCIENCES -JMS- Url: Jl. Pemuda, No. 339 Kolaka Southeast Sulawesi, Indonesia

Econometric Methods. Review of Estimation

Introduction to local (nonparametric) density estimation. methods

A New Family of Transformations for Lifetime Data

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

Simulation Output Analysis

Probability and Stochastic Processes

18.413: Error Correcting Codes Lab March 2, Lecture 8

INTERVAL ESTIMATION FOR THE QUANTILE OF A TWO-PARAMETER EXPONENTIAL DISTRIBUTION

Functions of Random Variables

MEASURES OF DISPERSION

A Family of Non-Self Maps Satisfying i -Contractive Condition and Having Unique Common Fixed Point in Metrically Convex Spaces *

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and

Unsupervised Learning and Other Neural Networks

Transcription:

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Sees A, OF THE ROMANIAN ACADEMY Volume 8, Numbe 3/27,. - L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES Roxaa CIUMARA Academy of Ecoomc Studes, Deatmet of Mathematcs I ths ae we comute the L-momets fo detcally ad odetcally Webull dstbuted adom vaables. The esults ae based o the evaluato of momets of ode statstcs. Fst, we deve the ode statstcs ad L-momets fo geealzed Webull dstbuted adom vaables. As a secal case, we obta the exesso fo the ode statstcs ad L-momets fo the Webull dstbuto. Moeove the L-momets fo detcally dstbuted Webull adom vaabes could be obtaed fom the L-momets fo odetcally Webull dstbuted adom vaables. Key wods: L-momets; ode statstcs; Webull dstbuted adom vaables.. INTRODUCTION It s a stadad actce statstcs to use desctve measues (quattes ode to descbe the shae of the dstbuto of a oulato. Such measues has bee defed by meas of classcal momets of dffeet odes: the mea to estmate locato, the vaace to measue the sead, the stadadzed measues fo sewess ad utoss. Though t s well-ow that deste the oulaty both data descto ad moe fomal statstcal ocedues, samle momets suffe fom seveal dawbacs. Fo examle, they ae sestve to exteme obsevatos. Moeove, the asymtotc vaaces of momet-based estmatos ae maly detemed by hghe ode momets, whch ae athe lage o eve ubouded fo heavy tal dstbutos. As a cosequece, asymtotc effcecy of samle momets s athe oo esecally fo dstbutos wth fat tals. Momet-based measues ae just atcula, but ot exhaustve meas of summazg qualtatve featues of the shae of a dstbuto. The otos of dseso, sewess ad utoss ae athe abstact ad theefoe ca be descbed may ways. A alteatve set of vey effectve desctve measues called L-momets ae based o atal odegs ad seem to lagely ovecome the samlg dawbacs of classcal measues. The L-momets aeaed fo the fst tme wthout ame quatle exaso of Slltto [] ad the Hosg s [5] eseach eot. Fomally, the L-momets wee toduced by Hosg [4] as a lea combato of the ode statstcs of a oulato. Le classcal momets, L-momets ovde tutve fomato about the shae of a dstbuto, whch ca be cosstetly estmated fom the samle values. Hosg, Walls ad Wood [7] ad Hosg ad Walls [6] use L-momets estmato method to exteme value dstbuto. They foud that t efoms bette tha method of momets ad, moeove, that both momet- based methods do well small samles comaed to maxmum lelhood estmato. Tag to accout the moe satsfactoy samlg behavo of L-momets estmatos, the sueo emcal efomace ove some data sets ad the esults of fomal smulato exemets ove some selected dstbutos, some authos (fo examle, Hosg [3] ecommed the use of L-momets stead of the classcal momets. I what follows we wll emd the fomal defto of L-momets ad some motat esults coceg them. I Sectos 3 we deve the L-momets fo geealzed Webull dstbuted adom vaables. As a atcula case, Secto 4 we fd the L-momets exesso fo detcally Webull

Roxaa Cumaa 2 dstbuted adom vaables. I Secto 5 we evaluate the L-momets fo odetcally Webull dstbuted adom vaables. Also we ote that the esults fom Secto 4 could be obtaed as a secal case of those fom Secto 5. 2. SOME PRELIMINARY RESULTS Let X be a eal-valued adom vaable wth cumulatve dstbuto fucto ad let : X 2:... X be the ode statstcs of a adom samle of sze fom the dstbuto of X. X : fo Defto 2. ([3]. The L-momets ae defed to be the quattes,2,...,, whee C j, N!( *.! j j ( C E( X j: F( x, (2. Rema 2.. The L-momets as well as oday momets ae secal cases of obablty weghted momets toduced by Geewood et al. [2] as M, s s ( X ( F( X ( F( X whee,, s. Obvously ae the oday momets,,,, E, (2.2 M,, X + : + ad + M E( ( j!( j! E( X M, j, j j:.! The use of L-momets to descbe obablty dstbutos s justfed by the followg esult. Theoem 2. (Hosg [3]. ( The L-momets,,2,..,, of a eal-valued adom vaable X exst f ad oly f X has fte mea. ( A dstbuto whose mea exsts s chaactezed by ts L-momets. (,2,... Thus a dstbuto ca be secfed by ts L-momets eve f some of ts covetoal momets do ot exst. Futhemoe, such a secfcato s always uque, whch s of couse ot tue fo covetoal momets. The followg esult wll be used Secto 4. Lemma 2. (Kha et al. [8]. If X s a ostve adom vaable wth cumulatve dstbuto fucto F ( x ad ( b a J a, b x F( x ( F( x dx, whee a, b ae eal umbes chose such that J exsts ( a, b ad s fte, the J ( +, ( C J ( I Secto 5 we wll use the followg esult.. +,

3 L-momets evaluato fo Webull dstbuto Theoem 2.2 (Baaat ad Abdelade []. Let X X,..., be odetcally dstbuted adom vaables ad X : X 2:... X : the coesodg ode statstcs. The fo ad,2,... the th ode momet of ca be evaluated ecusvely as whee ( X :, 2 ( ( j ( + ( E X : ( C j I j, j + (... α ( j, ( ( α ( : m x F x dx s the th momet of the mmum of I j : j < j2 <... < j X 2 m F ( x ( X, X,..., m ( t t ad s the cumulatve dstbuto fucto of X. X 3. GENERALIZED WEIBULL DISTRIBUTION CASE Let cosde a adom samle X, X 2,..., X of sze fom a geealzed Webull dstbuto wth cumulatve dstbuto fucto γ x F ( x ex, (3. θ * whee x >, θ, γ R. The obablty desty fucto ths case s, + ( γ F( f ( x x ( F( x ( x γ. (3.2 θγ The obablty desty fucto of the th ode statstcs Deote ( (! X : s γ [ F( x F( x ]( F( x f ( x! : x! θγ. (3.3 ( ( X Tag to accout elato (3.3, the exesso of ( a b s s α : E :. (3.4 ( E( X x f ( x α : : : dx J, fom Lemma 2., the fact that ad Defto 2., we get the followg esult fo the th ode momet of the th ode statstc ad the L- momets fo the geealzed Webull dstbuto. fo Theoem 3.. Ude the evously metoed codtos, ( α : [ J (, γ J (, ] (3.5!! θγ ( ( j j 2 ( ( C [ J ( j, j γ J ( j j ] θγ j + +,,2,...,, f the quattes volved the above exesso exst ad ae fte., (3.6

Roxaa Cumaa 4 4. THE WEIBULL DISTRIBUTION CASE I ths secto we cosde a adom samle X, X 2,..., X of sze fom a two-aamete Webull dstbuto wth cumulatve dstbuto fucto F x ( x ex, (4. θ * whee x > ad, θ R +. Obvously, ths coesods to the case γ fom Secto 3. Theefoe, the obablty desty fucto ths case s f ( x x ( F( x. (4.2 θ Alyg Theoem 3.. fo γ we get ( α : [ J (, J (, ].!! θ ( ( It s easy to see fom elato the exesso of ( a b ad, theefoe, ( a, b J ( a, b J ( a +, b J, gve Lemma 2. that J (4.3 ( : J ( +,, (4.4!! θ α ( ( a esult also obtaed dectly by Kha et al. [8]. Moeove, ths atcula case we have the followg esults egadg L-momets. ad Theoem 4.. The L-momets fo the Webull dstbuto satsfy j ( ( j 2 θ j j j 2 ( ( C J ( j +, j θ + Γ ( a(, whee a, C C, fo,2,...,. j + +, (4.5, (4.6 Poof. We use Defto 2. ad elato (4.4 o Theoem 3.. ad elato (4.3. By Lemma fom Kha et al. [8], we get Futhemoe, we ote that J ( j j + j +, j ( C j J + ( +,. (4.7 J θ,. (4.8 ( a Γ a

5 L-momets evaluato fo Webull dstbuto Now, usg Theoem 4.., ad elatos (4.7 ad (4.8, we get elato (4.6. The ext esult gves a smle exesso fo the L-momets a secal case. Poosto 4.. Fo θ ad, the L-momets fo the Webull dstbuto ae gve by whee 2,...,. Poof. Saha [9] showed that fo, (4.9 ( θ ad we have α : + By the defto of L-momets, we the have. j j j j j ( C ( C ( ( j + + j 2 C 2 fom whch we get elato (4.9 fo 2,...,. Rema 4.. Obvously, ( α θ X : E fo. 5. L-MOMENTS FOR NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES Let F F,...,, 2 X X,..., F, 2, whee X be deedet ostve adom vaables wth cumulatve dstbuto fuctos * wth x >, θ, θ,..., θ., 2 R + As the evous sectos, let ( F x, (5. ( x ex θ X : X 2:... X : deote the coesodg ode statstcs, α : E X :, ad the L-momets defed by (2.. The ext esult gves a chaactezato of L-momets fo odetcally Webull dstbuted adom vaables. Theoem 5.. The L-momets fo odetcally dstbuted Webull adom vaables ae gve by whee ( j Γ ( b(, ~ I, (5.2 j j ~ b, C C ad I.... j j... j j j... < 2 < < θ + θ + + θ 2 j Poof. We aly Theoem 2.2 of Baaat ad Abdelade [] fo. Thus, we have to evaluate ot ( I j I j. Tag to accout the exesso fo ( ~ I j ad elato (5., we obta I j Γ I j, whee I ~ j has the fom metoed above. Now, we get

Roxaa Cumaa 6 α j: Γ j+ ( j C ~ j I, ad the, fom Defto 2., afte some calculato, Γ ( j j ~ C C I j, hece, fally, elato (5.2. Rema 5.. Fo detcally Webull dstbuted adom vaables, that s, Theoem 5., we get elato (4.6 fom Theoem 4.. θ θ fo,, fom REFERENCES. BARAKAT, H.M., ABDELKADER, Y.H., Comutg the momets of ode statstcs fom odetcal adom vaables, Statstcal Methods ad Alcatos, 3,. 3-24, 24. 2. GREENWOOD, J.A., LANDWEHR, J.M., MATALA, N.C., WALLIS, J.R., Pobablty weghted momets: Defto ad elato to aametes of seveal dstbuto exessable the vese fom, Wate Resouces Reseach, 5, 5,. 49-54, 979. 3. HOSKING, J.R.M., Some theoetcal esults coceg L-momets, Reseach Reot RC 4492 (6497 3/22/89, evsed 7/5/96, IBM Reseach Dvso, Yotow Heghts, 996. 4. HOSKING, J.R.M., L-momets: aalyss ad estmato of dstbutos usg lea combatos of ode statstcs, Joual of the Royal Statstcal Socety, Sees B, Methodologcal 52,. 5-24, 99. 5. HOSKING, J.R.M, The theoy of obablty weghted momets, Reseach Reot RC22, IBM Reseach, Yotow Heghts, 986. 6. HOSKING, J.R.M., WALLIS, J.R., Paamete ad quatle estmato fo the geealzed Paeto dstbuto, Techometcs, 29, 3,. 339-349, 987. 7. HOSKING, J.R.M., WALLIS, J.R., WOOD, E.F., Estmato of the geealzed exteme-value dstbuto by the method of obablty weghted momets, Techometcs, 27, 3,. 25-26, 985. 8. KHAN, A.H., KHAN, R.U., PARVEZ, S., Ivese momets of ode statstcs fom Webull dstbuto, Scadava Actuaal Joual,. 9-94, 984. 9. SARHAN, A.E., Estmato of the mea ad stadad devato by ode statstcs, A. Math. Statst., 25,. 37-328, 954.. SILLITTO, G.P., Devato of aoxmats to the vese dstbuto fucto of a cotuous uvaate oulato fom the ode statstcs of a samle, Bometa, 56,. 64-65, 969. Receved May 2, 27