Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Size: px
Start display at page:

Download "Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION"

Transcription

1 Malasa Joural of Mathematcal Sceces (): (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar Departmet of Mathematcs, Facult of Scece ad Techolog, Uverst Malasa Tereggau Departmet of Mathematcs, Facult of Scece, Uverst Putra Malasa 3 Facult of Computer Scece ad Iformato Techolog Uverst Putra Malasa E-mal: urzzatjawas@ahoo.com, fudzah_@ahoo.com.m, msulema@ahoo.com.m, azm@fstm.upm.edu.m ABSTRACT A ew fourth order four-stage Dagoall Implct Ruge-Kutta (DIRK) method whch s specall desged for the tegrato of Lear Ordar Dfferetal Equatos (LODEs) s costructed. I the dervato, Butcher s error equatos are used but oe of the error equatos ca be elmated due to the propert of the LODE tself. The stablt aspect of the method s vestgated ad t s foud to have a bgger rego of stablt compared to explct Ruge-Kutta (ERK) method of the same tpe (desged for the tegrato of LODE). A set of test problems are used to valdate the method ad umercal results show that the method produced smaller global error compared to ERK method. INTRODUCTION I ths paper, we cosder the umercal tegrato of lear homogeeous sstems of ordar dfferetal equatos (ODEs) of the form = A g( x) (.) where A s a square matrx whose etres does ot deped o or x, ad ad g( x ) are vectors. Such sstems arse the umercal soluto of partal dfferetal equatos (PDEs) goverg wave ad heat pheomea after applcato of a spatal dscretzato such as fte-dfferece method. Explct Ruge-Kutta method s ver popular for smulatos of wave equatos; (see Zgg ad Chsholm (999) ad Ferraca ad Spjer (007), due to ther hgh accurac ad low memor requremets.

2 Nur Izzat Che Jawas et al. I the dervato of Ruge-Kutta (RK) methods, certa order equatos or sometmes called error equatos eed to be satsfed; see Dormad (996). These order equatos resulted from the dervatves of the fucto = f ( x, ) tself. If the fucto s lear the some of the error equatos resulted b the oleart the dervatve fucto ca be removed, thus less order equatos eed to be satsfed, hece a more effcet method some respect tha the classcal method ca be produced or derved. I ths paper, we costruct dagoall mplct Ruge-Kutta method specfcall for lear ODEs wth costat coeffcets, the the stablt aspect of the method s looed to ad a set of test equatos are used to valdate the ew method. DERIVATION OF THE METHOD We cosder the followg scalar ODE = f ( x, ) (.) Whe a geeral s-stage dagoall mplct Ruge-Kutta method s appled to the ODE, the followg equatos are obtaed, s = hb (.a) = where = f ( x c h, h a ). (.b) j= j j = s a j j= We shall alwas assume that the row-sum codto holds c, where =,.. s. Accordg to Dormad(996) ad Butcher (003), the followg eght order equatos are equatos eeded to be satsfed b fourth order four-stage DIRK method. 96 Malasa Joural of Mathematcal Sceces

3 Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method For Lear Ordar Dfferetal Equatos TABLE : Ruge-Kutta order equatos for fourth order () b τ = τ = b c () τ = bc (3) 3 τ = b a c (3) j j 6 j τ = b c ( ) 3 τ = bc a c () j j 8 j τ = b a c () 3 j j j τ = b a a c () j j j The restrcto to lear ODEs reduces the umber of equatos whch the coeffcets of the RK method must satsf Table. Zgg ad Chsholm (999) have derved ew explct RK methods whch are sutable for lear ODEs that are more effcet tha the covetoal RK methods. For ths ew fourth order DIRK method sutable for lear ODEs, equato 6 Table ca be elmated, see paper Zgg ad Chsholm (999). Ths codto s elmated b explotg the fact that, for lear ODEs, f f = = 0. x After equato 6 Table has bee elmated, we have seve equatos to be solved wth uows. So we have four free parameters whch are chose to beγ = 0.0, c = 0.05, c3 = 0.0 ad c = All the equatos Table (except equato 6) are solved usg MAPLE pacage. The coeffcets obtaed are wrtte Butcher s arra as follows: Malasa Joural of Mathematcal Sceces 97

4 Nur Izzat Che Jawas et al. c a T b Applg all the parameters to the geeral form of RK method, we have the ew fourth order four-stage DIRK method whch s sutable for lear ODEs, = where 3 = f ( x = f ( x = f ( x = f ( x h( ) 0.0h, 0.05h, 0.0h, 0.80h, h(0.0 )) h( 0.5 h( h( )) ( ) )) )) STABILITY Oe of the practcal crtera for a good method to be useful s that t must have a rego of absolute stablt. Whe a s-stage Ruge-Kutta method (equatos (.a) ad (.b)) s appled to = f ( x, ) = λ 98 Malasa Joural of Mathematcal Sceces

5 Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method For Lear Ordar Dfferetal Equatos the followg equatos are obtaed where ) = R ( hλ ( λ) = ( ˆ) = ˆ ( ˆ ) T R h R h hb I ha e ad A s (m x m), e s (m x ) are obtaed from the coeffcets of the method tself. R(hˆ ) s called the stablt polomal of the method ad for ths method t s gve as R( h) = hˆ 0.8 ˆ 0. ˆ h h ( ˆ ˆ ˆ 3 h h h ) 0.03hˆ ( 0.6 ˆ 0. ˆ ˆ 3 h h h ) 0.006hˆ. 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h 0.5 ˆ 0.06 ˆ ˆ ( h h h ) 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h 0.073( ˆ ˆ ˆ 3 h h h 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h ( ˆ ˆ ˆ 3 h h h ) 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h ( ˆ ˆ ˆ 3 h h h 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h 0.073( ˆ ˆ ˆ 3 h h h ). 0.8 ˆ 0. ˆ 0.03 ˆ ˆ h h h h The stablt rego s obtaed b tag R( hˆ ) = = cosθ sθ ad solve for ĥ usg Mathematca pacage (see Torrece (999)). The stablt rego for ew fourth order four-stage DIRK s show Fgure. 3 Malasa Joural of Mathematcal Sceces 99

6 Nur Izzat Che Jawas et al. Imagar Part Stablt Rego Real Part Fgure : The stablt rego for ew th order -stage DIRK We also fd the stablt polomal ad the stablt rego for the fourth order four-stage explct Ruge-Kutta method (ERK) Zgg ad Chsholm (999), ad t s show Fgure below. Imagar Part Stablt Rego Real Part Fgure : The stablt rego for th order -stage ERK 00 Malasa Joural of Mathematcal Sceces

7 Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method For Lear Ordar Dfferetal Equatos PROBLEMS TESTED AND NUMERICAL RESULTS The followg are some of the problems tested. All the problems are lear ordar dfferetal equatos. PROBLEM : '( t) = ( t) = t e 0 t, (0) = Source: Rchard L.Burde ad J.Douglas Fares (00) PROBLEM : 3t '( t) = te ( t) = te e e t 3, (0) = 0 3t 3t t Source: Rchard L.Burde ad J.Douglas Fares (00) PROBLEM 3: '( t) = ta t cos t ( t) = cos t s t 0 t, (0) = Source: J. C. Butcher (003) PROBLEM : t t '( ) = t t e ( ) = ( ) 0.5 t 0 t, (0) = 0.5 Source: Rchard L.Burde ad J.Douglas Fares (00) Malasa Joural of Mathematcal Sceces 0

8 Nur Izzat Che Jawas et al. PROBLEM 5: t t t e e t '( t) = t e t ( ) = ( ) t 5, () = 0 Source: Rchard L.Burde ad J.Douglas Fares (00) The umercal results are tabulated ad compared wth the exstg method ad below are the otatos used: H ~ Step sze used MTHD ~ Method emploed MAXE ~ Maxmum error ( x ) ~ Fourth order four-stage explct RK method Zgg ad Chsholm (999) ~ New fourth order four-stage DIRK method TABLE : Comparso betwee ad for solvg Problem... MTHD H MAXE 33e e e e-008.7e e e e-0.9e e-0.685e e e e-06 0 Malasa Joural of Mathematcal Sceces

9 Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method For Lear Ordar Dfferetal Equatos TABLE 3: Comparso betwee ad for solvg problem... MTHD H MAXE.950e e-00.e e-003 5e e e e e e e e e e-00 TABLE : Comparso betwee ad for solvg problem 3... MTHD H MAXE 9.886e e e e e e e e-0.e e e e e e-05 Malasa Joural of Mathematcal Sceces 03

10 Nur Izzat Che Jawas et al. TABLE 5: Comparso betwee ad for solvg problem... MTHD H MAXE 69800e e e e e e e e e e-0.68e e-03 85e e-05 TABLE 6: Comparso betwee ad for solvg problem 5... MTHD H MAXE 53e e e e e e e e e e e e e e-00 CONCLUSION The ew fourth order four-stage DIRK method has bee preseted for the tegrato of lear sstems of ODEs. It has a bgger stablt rego compared to explct RK method (of the same order), ad hece the formula s more stable. From the umercal results Tables to 6, we ca coclude 0 Malasa Joural of Mathematcal Sceces

11 Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method For Lear Ordar Dfferetal Equatos that the ew fourth order four-stage DIRK method whch s sutable for lear ODEs performs better terms of accurac compared to fourth order four-stage ERK method. REFERENCES Burde, R.L., Fares, J.D. 00. Numercal Aalss seveth edto, Wadsworth Group. Broos/Cole, Thomso Learg, Ic. Butcher, J.C. 00 Numercal Methods for Ordar Dfferetal Equato, Joh Wle & Sos Ltd. Dormad, J.R. 99 Numercal Methods for Dfferetal Equatos, Boca Rato, New Yor, Lodo ad Toa: CRC Press, Ic. Ferraca, L., Spjer, M.N. 00 Strog stablt of Sgl-Dagoall- Implct Ruge-Kutta methods. Report o MI 007-, Mathematcal Isttute, Lede Uverst. Torrece, B.F., Torrece, E.A How to fd the stablt regos: The Studet s Itroducto to Mathematca, Cambrdge Uverst Press: pp 3- Zgg, D.W., Chsholm T.T Ruge-Kutta methods for lear ordar dfferetal equatos, Appled Numercal Mathematcs. 3: Malasa Joural of Mathematcal Sceces 05

Solving Linear Ordinary Differential Equations using Singly Diagonally Implicit Runge-Kutta fifth order five-stage method

Solving Linear Ordinary Differential Equations using Singly Diagonally Implicit Runge-Kutta fifth order five-stage method Fudzah Ismal, Nur Izzat Che Jawas, Mohamed Suleman, Azm Jaafar Solvng Lnear Ordnar Dfferental Equatons usng Sngl Dagonall Implct Runge-Kutta ffth order fve-stage method FUDZIAH ISMAIL, NUR IZZATI CHE JAWIAS,

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Numerical Solutions of Second Order Initial Value Problems of Bratu-Type equation Using Higher Ordered Rungu-Kutta Method

Numerical Solutions of Second Order Initial Value Problems of Bratu-Type equation Using Higher Ordered Rungu-Kutta Method Iteratoal Joural of Scetfc ad Research Publcatos Volume 7 Issue October 7 87 ISSN 5-353 Numercal Solutos of Secod Order Ital Value Problems of Bratu-Tpe equato Usg Hgher Ordered Rugu-Kutta Method Habtamu

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

Bivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials

Bivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-78, p-issn: 19-76X. Volume 1, Issue Ver. II (Jul. - Aug.016), PP -0 www.osrjourals.org Bvarate Veta-Fboacc ad Bvarate Veta-Lucas Polomals E. Gokce KOCER 1

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt Ital-Value Problems or ODEs d GIVEN: t t,, a FIND: t or atb umercal errors (roud-o ad trucato errors) Cosder a perturbed sstem: dz t, z t, at b z a a Does z(t) (t)? () (uqueess) a uque soluto (t) exsts

More information

Generalized One-Step Third Derivative Implicit Hybrid Block Method for the Direct Solution of Second Order Ordinary Differential Equation

Generalized One-Step Third Derivative Implicit Hybrid Block Method for the Direct Solution of Second Order Ordinary Differential Equation Appled Mathematcal Sceces, Vol. 1, 16, o. 9, 417-4 HIKARI Ltd, www.m-hkar.com http://dx.do.org/1.1988/ams.16.51667 Geeralzed Oe-Step Thrd Dervatve Implct Hybrd Block Method for the Drect Soluto of Secod

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos

More information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

16 Homework lecture 16

16 Homework lecture 16 Quees College, CUNY, Departmet of Computer Scece Numercal Methods CSCI 361 / 761 Fall 2018 Istructor: Dr. Sateesh Mae c Sateesh R. Mae 2018 16 Homework lecture 16 Please emal your soluto, as a fle attachmet,

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

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

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Fractional Order Finite Difference Scheme For Soil Moisture Diffusion Equation And Its Applications

Fractional Order Finite Difference Scheme For Soil Moisture Diffusion Equation And Its Applications IOS Joural of Mathematcs (IOS-JM e-iss: 78-578. Volume 5, Issue 4 (Ja. - Feb. 3, PP -8 www.osrourals.org Fractoal Order Fte Dfferece Scheme For Sol Mosture Dffuso quato Ad Its Applcatos S.M.Jogdad, K.C.Takale,

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

CHAPTER 3 POSTERIOR DISTRIBUTIONS

CHAPTER 3 POSTERIOR DISTRIBUTIONS CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

Numerical Solution of Linear Second Order Ordinary Differential Equations with Mixed Boundary Conditions by Galerkin Method

Numerical Solution of Linear Second Order Ordinary Differential Equations with Mixed Boundary Conditions by Galerkin Method Mathematcs ad Computer Scece 7; (5: 66-78 http://www.scecepublshggroup.com//mcs do:.648/.mcs.75. Numercal Soluto of Lear Secod Order Ordary Dfferetal Equatos wth Mxed Boudary Codtos by Galer Method Aalu

More information

Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates

Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates Joural of Moder Appled Statstcal Methods Volume Issue Artcle 8 --03 Comparso of Parameters of Logormal Dstrbuto Based O the Classcal ad Posteror Estmates Raja Sulta Uversty of Kashmr, Sragar, Ida, hamzasulta8@yahoo.com

More information

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES 0/5/04 ITERESTIG FIITE AD IFIITE PRODUCTS FROM SIMPLE ALGEBRAIC IDETITIES Thomas J Osler Mathematcs Departmet Rowa Uversty Glassboro J 0808 Osler@rowaedu Itroducto The dfferece of two squares, y = + y

More information

Correlation and Regression Analysis

Correlation and Regression Analysis Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Joural of Appled Matematcs ad Computatoal Mecacs 04, 3(4), 7-34 FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Ata Cekot, Stasław Kukla Isttute of Matematcs, Czestocowa Uversty of Tecology Częstocowa,

More information

Order statistics from non-identical Standard type II Generalized logistic variables and applications at moments

Order statistics from non-identical Standard type II Generalized logistic variables and applications at moments Amerca Joural of Theoretcal ad Appled Statstcs 05; 4(: -5 Pulshed ole Jauar 3, 05 (http://www.scecepulshggroup.com//atas do: 0.648/.atas.05040. ISSN: 36-8999 (Prt; ISSN: 36-9006 (Ole Order statstcs from

More information

Line Fitting and Regression

Line Fitting and Regression Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch

More information

The numerical simulation of compressible flow in a Shubin nozzle using schemes of Bean-Warming and flux vector splitting

The numerical simulation of compressible flow in a Shubin nozzle using schemes of Bean-Warming and flux vector splitting The umercal smulato of compressble flow a Shub ozzle usg schemes of Bea-Warmg ad flux vector splttg Gh. Paygaeh a, A. Hadd b,*, M. Hallaj b ad N. Garjas b a Departmet of Mechacal Egeerg, Shahd Rajaee Teacher

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

Applied Mathematics and Computation

Applied Mathematics and Computation Appled Mathematcs ad Computato 215 (2010) 4198 4202 Cotets lsts avalable at SceceDrect Appled Mathematcs ad Computato joural homepage: www.elsever.com/locate/amc Improvemet estmatg the populato mea smple

More information

arxiv: v4 [math.nt] 14 Aug 2015

arxiv: v4 [math.nt] 14 Aug 2015 arxv:52.799v4 [math.nt] 4 Aug 25 O the propertes of terated bomal trasforms for the Padova ad Perr matrx sequeces Nazmye Ylmaz ad Necat Tasara Departmet of Mathematcs, Faculty of Scece, Selcu Uversty,

More information

Lecture Note to Rice Chapter 8

Lecture Note to Rice Chapter 8 ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

h-analogue of Fibonacci Numbers

h-analogue of Fibonacci Numbers h-aalogue of Fboacc Numbers arxv:090.0038v [math-ph 30 Sep 009 H.B. Beaoum Prce Mohammad Uversty, Al-Khobar 395, Saud Araba Abstract I ths paper, we troduce the h-aalogue of Fboacc umbers for o-commutatve

More information

Solution of General Dual Fuzzy Linear Systems. Using ABS Algorithm

Solution of General Dual Fuzzy Linear Systems. Using ABS Algorithm Appled Mathematcal Sceces, Vol 6, 0, o 4, 63-7 Soluto of Geeral Dual Fuzzy Lear Systems Usg ABS Algorthm M A Farborz Aragh * ad M M ossezadeh Departmet of Mathematcs, Islamc Azad Uversty Cetral ehra Brach,

More information

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

More information

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved.

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved. VOL., NO., November 0 ISSN 5-77 ARPN Joural of Scece ad Techology 0-0. All rghts reserved. http://www.ejouralofscece.org Usg Square-Root Iverted Gamma Dstrbuto as Pror to Draw Iferece o the Raylegh Dstrbuto

More information

Fibonacci Identities as Binomial Sums

Fibonacci Identities as Binomial Sums It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu

More information

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

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

The Application of the Hybrid Method to Solving the Volterra Integro-differential Equation

The Application of the Hybrid Method to Solving the Volterra Integro-differential Equation Proceedgs of the World Cogress o Egeerg 3 Vol I WCE 3 Jul 3-5 3 Lodo UK The Applcato of the Hbrd Method to Solvg the Volterra Itegro-dfferetal Equato G Mehdeva M Iaova ad V Ibrahov Abstract There are several

More information

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Sebasta Starz COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Abstract The am of the work s to preset a method of rakg a fte set of dscrete radom varables. The proposed method s based o two approaches:

More information

Answer key to problem set # 2 ECON 342 J. Marcelo Ochoa Spring, 2009

Answer key to problem set # 2 ECON 342 J. Marcelo Ochoa Spring, 2009 Aswer key to problem set # ECON 34 J. Marcelo Ochoa Sprg, 009 Problem. For T cosder the stadard pael data model: y t x t β + α + ǫ t a Numercally compare the fxed effect ad frst dfferece estmates. b Compare

More information

J P S S. A comprehensive journal of probability and statistics for theorists, methodologists, practitioners, teachers, and others

J P S S. A comprehensive journal of probability and statistics for theorists, methodologists, practitioners, teachers, and others ISSN 76-338 J P S S A comprehesve joural of probablty ad statstcs for theorsts methodologsts practtoers teachers ad others JOURNAL OF PROBABILITY AND STATISTICAL SCIENCE Volume 8 Number August 00 Joural

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.

More information

Supervised learning: Linear regression Logistic regression

Supervised learning: Linear regression Logistic regression CS 57 Itroducto to AI Lecture 4 Supervsed learg: Lear regresso Logstc regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Data: D { D D.. D D Supervsed learg d a set of eamples s

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR DIFFERANTIAL EQUATIONS

A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR DIFFERANTIAL EQUATIONS Secer, A., et al.: A New Numerıcal Approach for Solvıg Hıgh-Order Lıear ad No-Lıear... HERMAL SCIENCE: Year 8, Vol., Suppl., pp. S67-S77 S67 A NEW NUMERICAL APPROACH FOR SOLVING HIGH-ORDER LINEAR AND NON-LINEAR

More information

DKA method for single variable holomorphic functions

DKA method for single variable holomorphic functions DKA method for sgle varable holomorphc fuctos TOSHIAKI ITOH Itegrated Arts ad Natural Sceces The Uversty of Toushma -, Mamhosama, Toushma, 770-8502 JAPAN Abstract: - Durad-Kerer-Aberth (DKA method for

More information

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Journal of Mathematical Analysis and Applications

Journal of Mathematical Analysis and Applications J. Math. Aal. Appl. 365 200) 358 362 Cotets lsts avalable at SceceDrect Joural of Mathematcal Aalyss ad Applcatos www.elsever.com/locate/maa Asymptotc behavor of termedate pots the dfferetal mea value

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE merca Jr of Mathematcs ad Sceces Vol, No,(Jauary 0) Copyrght Md Reader Publcatos wwwjouralshubcom MX-MIN ND MIN-MX VLUES OF VRIOUS MESURES OF FUZZY DIVERGENCE RKTul Departmet of Mathematcs SSM College

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01 ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε

More information

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction Computer Aded Geometrc Desg 9 79 78 www.elsever.com/locate/cagd Applcato of Legedre Berste bass trasformatos to degree elevato ad degree reducto Byug-Gook Lee a Yubeom Park b Jaechl Yoo c a Dvso of Iteret

More information

Chapter 2 Supplemental Text Material

Chapter 2 Supplemental Text Material -. Models for the Data ad the t-test Chapter upplemetal Text Materal The model preseted the text, equato (-3) s more properl called a meas model. ce the mea s a locato parameter, ths tpe of model s also

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

The Necessarily Efficient Point Method for Interval Molp Problems

The Necessarily Efficient Point Method for Interval Molp Problems ISS 6-69 Eglad K Joural of Iformato ad omputg Scece Vol. o. 9 pp. - The ecessarly Effcet Pot Method for Iterval Molp Problems Hassa Mshmast eh ad Marzeh Alezhad + Mathematcs Departmet versty of Ssta ad

More information

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 6 2006, #A12 LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX Hacèe Belbachr 1 USTHB, Departmet of Mathematcs, POBox 32 El Ala, 16111,

More information

Solutions to problem set ); (, ) (

Solutions to problem set ); (, ) ( Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated

More information

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 59, 2947-2951 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/ma.2013.310259 O the Iterval Zoro Symmetrc Sgle Step Procedure IZSS1-5D for the Smultaeous

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information