هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني

Size: px
Start display at page:

Download "هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني"

Transcription

1 هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني يف حالت البياناث املفقودة باستخذام احملاكاة د أ. الباحثة ظافر حسين رشيد جامعة بغداد- كمية االدارة واالقتصاد قسم االحصاء آوات سردار وادي املستخلص Maxiu Lielihood Mehod Shiage Mehod Siha ho Bowa, Sheo ad La Moe Calo MSE Absac he esiaio of he paaees of wo Paaees Gaa Disibuio i case of issig daa has bee ade by usig wo ipoa ehods: he Maxiu Lielihood Mehod ad he Shiage Mehod. he foe oe cosiss of hee ehods o solve he MLE o-liea equaio by which he esiaos of he axiu lielihood ca be obaied: ewo-raphso, ho ad Siha ehods. ho ad Siha ehods ae developed by he eseache o be suiable i case of issig daa. Fuheoe, he Bowa, Sheo ad La Mehod, which depeds o he hee Paaees Gaa Disibuio o ge he axiu lielihood esiaos, has bee developed. A copaiso has bee ade bewee he ehods i he expeieal aspec o fid he bes ehod hough siulaio by usig he Moe Calo Mehod. Seveal expeieaios have bee ade by usig he ipoa saisical easue: Mea Squae Eo MSE. : املقذهت واهلذف:

2 Icoplee Dey Coi ad Dey Coi ad JI Maxiu Lielihood Mehod Siha ho Shiage Mehod Siha, Bowa, Sheo ad La Moe Calo MSE : تقذير هعلواث توزيع كاها ري املعلوتني يف حالت البياناث املفقودة:

3 Disibuio of Cuulaive ie- o- Failue Lielihood Fucio MLEs Maxiu Lielihood Esiaio, = X + X + +X ;=,,, X i X i ~ i.i.d gaa, X i i ~ gaa, ; =,,, ; =,,, f f \ exp X i f f \ P R PR= Mixue Model

4 f f \ exp P R exp P R=,, =,,, =,,, = ax {\ > 0} M M M /

5 Esiaig by Maxiu Lielihood Mehod i i i exp L, i i L, f exp, \ f \ LL, M ML L L L L M : LL, M 0 : LL, ML L LL

6 Digaa Fucio ML L ML ML L ML L Maxiu Lielihood Esiaios MLEs ewo-raphso Mehod i 4 i i i i g g g ML g

7 i i i i i ML i M, Develope of ho Mehod ho ho L 4y /3, 4y L ho L L / ML

8 0 / 6 L L L 4 / 3 4 Siha Develope of Siha Mehod Siha L, y Siha L

9 ML L ] [ ML ML L L s L s s s

10 Bowa, Sheo ad La Develope of Bowa, Sheo ad La Mehod Bowa, Sheo ad La, 3, X i Rado Vaiable 0 0,, ;, x I e x x f x X 30 i X i Moe Geeaig Fucio X = Ee X dx e x e x x y = x X e 3 Z = Z Z Ee X X X Ee... ] ]...[ ][ [ X X X

11 = e e e... i X i Z e 3 e f f ; \ 33 \,,,, f L exp 34

12 LL M LL M LL L ML LL A 36 M A M A A 37

13 A M A =0 bl bl bl 38 M, bl bl 3- التقذير باستخذام طريقت التقلص طريقت هقرتحت: Esiaio by Shiage Mehod Poposed Mehod Siha ho ho Siha hopso ~ h h 0 h 39 ~ ~ h h ~ MSE ~ E E[ h h ]

14 h MSE ~ h MSE h h B h h h [ MSE ~ h h ] 40 0 h 4 [ MSE h 4 ] h 3: اجلانب التجريبي: = 6, =0,30,50,00 =,,,

15 =6, = =,,, =6 =,,, 6 =0 4- اختيار قين املعلواث االفرتاضيت: Ivese asfo 0, xi,,,..., x i i,,..., i Logu, i i,,..., Coiuous Uifo Vaiae u i

16 38 L MSE i L MSE 4 i L =6 Model s bl sh

17 Model 3 = 6 s MSE bl sh =6 Model 3 s bl sh

18 Model =6 MSE s bl sh = Model s bl sh

19 Model = s MSE bl sh = Model s bl sh

20 Model = s MSE bl sh

21 : 4 االستنتاجاث والتوصياث, MSE Siha, ho Bowa, Sheo ad La

22 املصادر العربيت: املصادر األجنبيت:. Bowa, K.O., Sheo, L.R. ad La, H.K. 987 Siulaio ad Easiaio Pobles Associaed wih he 3-Paaee Gaa Desiy. Couicaios i Saisics, Seies B- Siulaio ad Copuaio, Vol.6, o.4, PP Choi, S.C., ad Wee, R. 969, Maxiu Lielihood Esiaio of he Paaees of he Gaa Disibuio ad hei Bias, echoeics, Vol., o. 4, PP Coi, D.W., ad Dey, K.A. 999, Aalysis of Gouped Daa fo Field- Failue Repoig Syses, Reliabiliy Egieeig ad Syse Safey, 65, Coi, D.W., ad JI,. 000, Gaa Disibuio Paaee Esiaio fo Field Reliabiliy Daa wih Missig Failue ies, IEEE asacios, 3, Dey, K.A. 98, Saisical Aalysis of oisy ad Icoplee Failue Daa i Poceedigs Aual Reliabiliy ad Maiaiabiliy Syposiu RAMS, IEEE, Piscaaway. J. PP Lawless. J. F. 003, Saisical Models ad Mehods fo Lifeie Daa, d ed., ew Jesey, Joh Wiley & Sos, Ic.. Sheo, L. R., ad Bowa, K.O. 970, Reas o ho s Esiaos fo he Gaa Disibuio, Mohly Weahe Review, Vol. 98, o., PP

Comparing Different Estimators for Parameters of Kumaraswamy Distribution

Comparing Different Estimators for Parameters of Kumaraswamy Distribution Compaig Diffee Esimaos fo Paamees of Kumaaswamy Disibuio ا.م.د نذير عباس ابراهيم الشمري جامعة النهرين/بغداد-العراق أ.م.د نشات جاسم محمد الجامعة التقنية الوسطى/بغداد- العراق Absac: This pape deals wih compaig

More information

Available online at J. Math. Comput. Sci. 2 (2012), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 2 (2012), No. 4, ISSN: Available olie a h://scik.og J. Mah. Comu. Sci. 2 (22), No. 4, 83-835 ISSN: 927-537 UNBIASED ESTIMATION IN BURR DISTRIBUTION YASHBIR SINGH * Deame of Saisics, School of Mahemaics, Saisics ad Comuaioal

More information

FBD of SDOF Base Excitation. 2.4 Base Excitation. Particular Solution (sine term) SDOF Base Excitation (cont) F=-(-)-(-)= 2ζω ωf

FBD of SDOF Base Excitation. 2.4 Base Excitation. Particular Solution (sine term) SDOF Base Excitation (cont) F=-(-)-(-)= 2ζω ωf .4 Base Exiaio Ipoa lass of vibaio aalysis Peveig exiaios fo passig fo a vibaig base hough is ou io a suue Vibaio isolaio Vibaios i you a Saellie opeaio Dis dives, e. FBD of SDOF Base Exiaio x() y() Syse

More information

Supplementary Information

Supplementary Information Supplemeay Ifomaio No-ivasive, asie deemiaio of he coe empeaue of a hea-geeaig solid body Dea Ahoy, Daipaya Saka, Aku Jai * Mechaical ad Aeospace Egieeig Depame Uivesiy of Texas a Aligo, Aligo, TX, USA.

More information

Cameras and World Geometry

Cameras and World Geometry Caeas ad Wold Geoe How all is his woa? How high is he caea? Wha is he caea oaio w. wold? Which ball is close? Jaes Has Thigs o eebe Has Pihole caea odel ad caea (pojecio) ai Hoogeeous coodiaes allow pojecio

More information

O & M Cost O & M Cost

O & M Cost O & M Cost 5/5/008 Turbie Reliabiliy, Maieace ad Faul Deecio Zhe Sog, Adrew Kusiak 39 Seamas Ceer Iowa Ciy, Iowa 54-57 adrew-kusiak@uiowa.edu Tel: 39-335-5934 Fax: 39-335-5669 hp://www.icae.uiowa.edu/~akusiak Oulie

More information

Numerical Solution of Sine-Gordon Equation by Reduced Differential Transform Method

Numerical Solution of Sine-Gordon Equation by Reduced Differential Transform Method Poceedigs of he Wold Cogess o Egieeig Vol I WCE, July 6-8,, Lodo, U.K. Nueical Soluio of Sie-Godo Equaio by Reduced Diffeeial Tasfo Mehod Yıldıay Kesi, İbahi Çağla ad Ayşe Beül Koç Absac Reduced diffeeial

More information

An Asymptotic Expansion for the Non-Central Chi-square Distribution. By Jinan Hamzah Farhood Department of Mathematics College of Education

An Asymptotic Expansion for the Non-Central Chi-square Distribution. By Jinan Hamzah Farhood Department of Mathematics College of Education A Asypoic Expasio fo h o-cal Chi-squa Disibuio By Jia Hazah ahood Dpa of Mahaics Collg of Educaio 6 Absac W div a asypoic xpasio fo h o-cal chi-squa disibuio as wh X i is h o-cal chi-squa vaiabl wih dg

More information

Spectrum of The Direct Sum of Operators. 1. Introduction

Spectrum of The Direct Sum of Operators. 1. Introduction Specu of The Diec Su of Opeaos by E.OTKUN ÇEVİK ad Z.I.ISMILOV Kaadeiz Techical Uivesiy, Faculy of Scieces, Depae of Maheaics 6080 Tabzo, TURKEY e-ail adess : zaeddi@yahoo.co bsac: I his wok, a coecio

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

Department of Mathematical and Statistical Sciences University of Alberta

Department of Mathematical and Statistical Sciences University of Alberta MATH 4 (R) Wier 008 Iermediae Calculus I Soluios o Problem Se # Due: Friday Jauary 8, 008 Deparme of Mahemaical ad Saisical Scieces Uiversiy of Albera Quesio. [Sec.., #] Fid a formula for he geeral erm

More information

Mathematical Statistics. 1 Introduction to the materials to be covered in this course

Mathematical Statistics. 1 Introduction to the materials to be covered in this course Mahemaical Saisics Iroducio o he maerials o be covered i his course. Uivariae & Mulivariae r.v s 2. Borl-Caelli Lemma Large Deviaios. e.g. X,, X are iid r.v s, P ( X + + X where I(A) is a umber depedig

More information

Relations on the Apostol Type (p, q)-frobenius-euler Polynomials and Generalizations of the Srivastava-Pintér Addition Theorems

Relations on the Apostol Type (p, q)-frobenius-euler Polynomials and Generalizations of the Srivastava-Pintér Addition Theorems Tish Joal of Aalysis ad Nmbe Theoy 27 Vol 5 No 4 26-3 Available olie a hp://pbssciepbcom/ja/5/4/2 Sciece ad Edcaio Pblishig DOI:269/ja-5-4-2 Relaios o he Aposol Type (p -Fobeis-Ele Polyomials ad Geealizaios

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

Moment Generating Function

Moment Generating Function 1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example

More information

Outline. Review Homework Problem. Review Homework Problem II. Review Dimensionless Problem. Review Convection Problem

Outline. Review Homework Problem. Review Homework Problem II. Review Dimensionless Problem. Review Convection Problem adial diffsio eqaio Febay 4 9 Diffsio Eqaios i ylidical oodiaes ay aeo Mechaical Egieeig 5B Seia i Egieeig Aalysis Febay 4, 9 Olie eview las class Gadie ad covecio boday codiio Diffsio eqaio i adial coodiaes

More information

Degree of Approximation of Fourier Series

Degree of Approximation of Fourier Series Ieaioal Mahemaical Foum Vol. 9 4 o. 9 49-47 HIARI Ld www.m-hiai.com h://d.doi.og/.988/im.4.49 Degee o Aoimaio o Fouie Seies by N E Meas B. P. Padhy U.. Misa Maheda Misa 3 ad Saosh uma Naya 4 Deame o Mahemaics

More information

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1 Samplig Example Le x = cos( 4π)cos( π). The fudameal frequecy of cos 4π fudameal frequecy of cos π is Hz. The ( f ) = ( / ) δ ( f 7) + δ ( f + 7) / δ ( f ) + δ ( f + ). ( f ) = ( / 4) δ ( f 8) + δ ( f

More information

WILD Estimating Abundance for Closed Populations with Mark-Recapture Methods

WILD Estimating Abundance for Closed Populations with Mark-Recapture Methods WILD 50 - Esiaig Abudace for Closed Populaios wih Mark-Recapure Mehods Readig: Chaper 4 of WNC book (especially secios 4. & 4.) Esiaig N is uch ore difficul ha you igh iiially expec A variey of ehods ca

More information

b : the two eigenvectors of the Grover iteration Quantum counting algorithm

b : the two eigenvectors of the Grover iteration Quantum counting algorithm .5.3. Quau couig algorih How quickly ca we deerie he uber of arge saes i Grover s algorih, i.e. arked saes, r, o a N= daa base search proble, if r is o kow i advace. Classical search ~ ( N ) Quau search

More information

Processamento Digital de Sinal

Processamento Digital de Sinal Deparaeo de Elecróica e Telecouicações da Uiversidade de Aveiro Processaeo Digial de ial Processos Esocásicos uar ado Processes aioar ad ergodic Correlaio auo ad cross Fucio Covariace Fucio Esiaes of he

More information

Parameter Optimization of Multi-element Synthetic Aperture Imaging Systems

Parameter Optimization of Multi-element Synthetic Aperture Imaging Systems Paaee Opiizaio of Muli-elee Syheic Apeue Iagig Syses Vea Beha Isiue fo Paallel Pocessig Bulgaia Acadey of Scieces 5-A Acad. G. Bochev S., Sofia 1113, Bulgaia E-ail: beha@bas.bg Received: Jauay 19, 7 Acceped:

More information

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x) 1 Trasform Techiques h m m m m mome : E[ ] x f ( x) dx h m m m m ceral mome : E[( ) ] ( ) ( x) f ( x) dx A coveie wa of fidig he momes of a radom variable is he mome geeraig fucio (MGF). Oher rasform echiques

More information

Extended Laguerre Polynomials

Extended Laguerre Polynomials I J Coemp Mah Scieces, Vol 7, 1, o, 189 194 Exeded Laguerre Polyomials Ada Kha Naioal College of Busiess Admiisraio ad Ecoomics Gulberg-III, Lahore, Pakisa adakhaariq@gmailcom G M Habibullah Naioal College

More information

S n. = n. Sum of first n terms of an A. P is

S n. = n. Sum of first n terms of an A. P is PROGREION I his secio we discuss hree impora series amely ) Arihmeic Progressio (A.P), ) Geomeric Progressio (G.P), ad 3) Harmoic Progressio (H.P) Which are very widely used i biological scieces ad humaiies.

More information

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP) ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames

More information

ABSOLUTE INDEXED SUMMABILITY FACTOR OF AN INFINITE SERIES USING QUASI-F-POWER INCREASING SEQUENCES

ABSOLUTE INDEXED SUMMABILITY FACTOR OF AN INFINITE SERIES USING QUASI-F-POWER INCREASING SEQUENCES Available olie a h://sciog Egieeig Maheaics Lees 2 (23) No 56-66 ISSN 249-9337 ABSLUE INDEED SUMMABILIY FACR F AN INFINIE SERIES USING QUASI-F-WER INCREASING SEQUENCES SKAIKRAY * RKJAI 2 UKMISRA 3 NCSAH

More information

New Class of Estimators of Population Mean. DECISION SCIENCES INSTITUTE New Class of Estimators of Population Mean Utilizing Median of Study Variable

New Class of Estimators of Population Mean. DECISION SCIENCES INSTITUTE New Class of Estimators of Population Mean Utilizing Median of Study Variable New lass of Esiaors of Poulaio Mea DEISION SIENES INSTITUTE New lass of Esiaors of Poulaio Mea Uilizig Media of Sudy Variable S.K. Yadav Dr. RML Avadh Uiversiy drskysas@gail.co Diesh K. Shara Uiversiy

More information

Recurrence Relations for the Product, Ratio and Single Moments of Order Statistics from Truncated Inverse Weibull (IW) Distribution

Recurrence Relations for the Product, Ratio and Single Moments of Order Statistics from Truncated Inverse Weibull (IW) Distribution Recuece Relatios fo the Poduct, Ratio ad Sigle Moets of Ode Statistics fo Tucated Ivese Weiull (IW) Distiutio ISSN 684 8403 Joual of Statistics Vol: 3, No. (2006) Recuece Relatios fo the Poduct, Ratio

More information

Institute of Actuaries of India

Institute of Actuaries of India Isiue of cuaries of Idia Subjec CT3-robabiliy ad Mahemaical Saisics May 008 Eamiaio INDICTIVE SOLUTION Iroducio The idicaive soluio has bee wrie by he Eamiers wih he aim of helig cadidaes. The soluios

More information

Inference of the Second Order Autoregressive. Model with Unit Roots

Inference of the Second Order Autoregressive. Model with Unit Roots Ieraioal Mahemaical Forum Vol. 6 0 o. 5 595-604 Iferece of he Secod Order Auoregressive Model wih Ui Roos Ahmed H. Youssef Professor of Applied Saisics ad Ecoomerics Isiue of Saisical Sudies ad Research

More information

LESSON 15: COMPOUND INTEREST

LESSON 15: COMPOUND INTEREST High School: Expoeial Fuctios LESSON 15: COMPOUND INTEREST 1. You have see this fomula fo compoud ieest. Paamete P is the picipal amou (the moey you stat with). Paamete is the ieest ate pe yea expessed

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Paper Introduction. ~ Modelling the Uncertainty in Recovering Articulation from Acoustics ~ Korin Richmond, Simon King, and Paul Taylor.

Paper Introduction. ~ Modelling the Uncertainty in Recovering Articulation from Acoustics ~ Korin Richmond, Simon King, and Paul Taylor. Paper Iroducio ~ Modellig he Uceraiy i Recoverig Ariculaio fro Acousics ~ Kori Richod, Sio Kig, ad Paul Taylor Tooi Toda Noveber 6, 2003 Proble Addressed i This Paper Modellig he acousic-o-ariculaory appig

More information

Analysis of Using a Hybrid Neural Network Forecast Model to Study Annual Precipitation

Analysis of Using a Hybrid Neural Network Forecast Model to Study Annual Precipitation Aalysis of Usig a Hybrid Neural Nework Forecas Model o Sudy Aual Precipiaio Li MA, 2, 3, Xuelia LI, 2, Ji Wag, 2 Jiagsu Egieerig Ceer of Nework Moiorig, Najig Uiversiy of Iformaio Sciece & Techology, Najig

More information

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws. Limi of a fucio.. Oe-Sided..... Ifiie limis Verical Asympoes... Calculaig Usig he Limi Laws.5 The Squeeze Theorem.6 The Precise Defiiio of a Limi......7 Coiuiy.8 Iermediae Value Theorem..9 Refereces..

More information

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mahemaics Prelim Quesio Paper Soluio Q. Aemp ay FIVE of he followig : [0] Q.(a) Defie Eve ad odd fucios. [] As.: A fucio f() is said o be eve fucio if

More information

On a Z-Transformation Approach to a Continuous-Time Markov Process with Nonfixed Transition Rates

On a Z-Transformation Approach to a Continuous-Time Markov Process with Nonfixed Transition Rates Ge. Mah. Noes, Vol. 24, No. 2, Ocobe 24, pp. 85-96 ISSN 229-784; Copyigh ICSRS Publicaio, 24 www.i-css.og Available fee olie a hp://www.gema.i O a Z-Tasfomaio Appoach o a Coiuous-Time Maov Pocess wih Nofixed

More information

The Eigen Function of Linear Systems

The Eigen Function of Linear Systems 1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =

More information

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4) 7 Differeial equaios Review Solve by he mehod of udeermied coefficies ad by he mehod of variaio of parameers (4) y y = si Soluio; we firs solve he homogeeous equaio (4) y y = 4 The correspodig characerisic

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

CSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists

CSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists CSE 41 Algorihms ad Daa Srucures 10/14/015 Skip Liss This hadou gives he skip lis mehods ha we discussed i class. A skip lis is a ordered, doublyliked lis wih some exra poiers ha allow us o jump over muliple

More information

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION Inenaional Jounal of Science, Technology & Managemen Volume No 04, Special Issue No. 0, Mach 205 ISSN (online): 2394-537 STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE

More information

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,

More information

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues Alied Maheaical Sciece Vol. 8 o. 5 747-75 The No-Tucaed Bul Aival Queue M x /M/ wih Reei Bali Sae-Deede ad a Addiioal Seve fo Loe Queue A. A. EL Shebiy aculy of Sciece Meofia Uiveiy Ey elhebiy@yahoo.co

More information

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003 ODEs II, Suppleme o Lecures 6 & 7: The Jorda Normal Form: Solvig Auoomous, Homogeeous Liear Sysems April 2, 23 I his oe, we describe he Jorda ormal form of a marix ad use i o solve a geeral homogeeous

More information

Section 8 Convolution and Deconvolution

Section 8 Convolution and Deconvolution APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:

More information

Intro to multivariate AR(1) models estimating interaction strengths, aka the B matrix

Intro to multivariate AR(1) models estimating interaction strengths, aka the B matrix Iro o ulivariae AR(1) odels esiaig ieracio sreghs, aka he B ari Eli Holes FISH 507 Applied Tie Series Aalysis 23 Feruary 2017 Mea reverig processes I lecure, I ill alk aou esiaig ea-reversio i he coe of

More information

ECE 350 Matlab-Based Project #3

ECE 350 Matlab-Based Project #3 ECE 350 Malab-Based Projec #3 Due Dae: Nov. 26, 2008 Read he aached Malab uorial ad read he help files abou fucio i, subs, sem, bar, sum, aa2. he wrie a sigle Malab M file o complee he followig ask for

More information

The Central Limit Theorems for Sums of Powers of Function of Independent Random Variables

The Central Limit Theorems for Sums of Powers of Function of Independent Random Variables ScieceAsia 8 () : 55-6 The Ceal Limi Theoems fo Sums of Poes of Fucio of Idepede Radom Vaiables K Laipapo a ad K Neammaee b a Depame of Mahemaics Walailak Uivesiy Nakho Si Thammaa 86 Thailad b Depame of

More information

The Central Limit Theorem

The Central Limit Theorem The Ceral Limi Theorem The ceral i heorem is oe of he mos impora heorems i probabiliy heory. While here a variey of forms of he ceral i heorem, he mos geeral form saes ha give a sufficiely large umber,

More information

On a Problem of Littlewood

On a Problem of Littlewood Ž. JOURAL OF MATHEMATICAL AALYSIS AD APPLICATIOS 199, 403 408 1996 ARTICLE O. 0149 O a Poblem of Littlewood Host Alze Mosbache Stasse 10, 51545 Waldbol, Gemay Submitted by J. L. Bee Received May 19, 1995

More information

CSE 202: Design and Analysis of Algorithms Lecture 16

CSE 202: Design and Analysis of Algorithms Lecture 16 CSE 202: Desig ad Aalysis of Algorihms Lecure 16 Isrucor: Kamalia Chaudhuri Iequaliy 1: Marov s Iequaliy Pr(X=x) Pr(X >= a) 0 x a If X is a radom variable which aes o-egaive values, ad a > 0, he Pr[X a]

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

MATH Midterm Solutions

MATH Midterm Solutions MATH 2113 - Midtem Solutios Febuay 18 1. A bag of mables cotais 4 which ae ed, 4 which ae blue ad 4 which ae gee. a How may mables must be chose fom the bag to guaatee that thee ae the same colou? We ca

More information

Basic Results in Functional Analysis

Basic Results in Functional Analysis Preared by: F.. ewis Udaed: Suday, Augus 7, 4 Basic Resuls i Fucioal Aalysis f ( ): X Y is coiuous o X if X, (, ) z f( z) f( ) f ( ): X Y is uiformly coiuous o X if i is coiuous ad ( ) does o deed o. f

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

The analysis of the method on the one variable function s limit Ke Wu

The analysis of the method on the one variable function s limit Ke Wu Ieraioal Coferece o Advaces i Mechaical Egieerig ad Idusrial Iformaics (AMEII 5) The aalysis of he mehod o he oe variable fucio s i Ke Wu Deparme of Mahemaics ad Saisics Zaozhuag Uiversiy Zaozhuag 776

More information

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly

More information

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial

More information

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend 6//4 Defiiio Time series Daa A ime series Measures he same pheomeo a equal iervals of ime Time series Graph Compoes of ime series 5 5 5-5 7 Q 7 Q 7 Q 3 7 Q 4 8 Q 8 Q 8 Q 3 8 Q 4 9 Q 9 Q 9 Q 3 9 Q 4 Q Q

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions.

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions. Iera. J. Mah. & Mah. Si. Vol. 6 No. 3 (1983) 559-566 559 ASYMPTOTIC RELATIOHIPS BETWEEN TWO HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS TAKA KUSANO laculy of Sciece Hrosh llersy 1982) ABSTRACT. Some asympoic

More information

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise Hi ue Hi ue -ay Deecio Pole Coide he ole of decidig which of hyohei i ue aed o oevig a ado vaiale (veco). he efoace cieia we coide i he aveage eo oailiy. ha i he oailiy of decidig ayhig ece hyohei H whe

More information

Linear Time Invariant Systems

Linear Time Invariant Systems 1 Liear Time Ivaria Sysems Oulie We will show ha he oupu equals he covoluio bewee he ipu ad he ui impulse respose: sysem for a discree-ime, for a coiuous-ime sysdem, y x h y x h 2 Discree Time LTI Sysems

More information

ON POINTWISE APPROXIMATION OF FUNCTIONS BY SOME MATRIX MEANS OF FOURIER SERIES

ON POINTWISE APPROXIMATION OF FUNCTIONS BY SOME MATRIX MEANS OF FOURIER SERIES M aheaical I equaliies & A pplicaios Volue 19, Nube 1 (216), 287 296 doi:1.7153/ia-19-21 ON POINTWISE APPROXIMATION OF FUNCTIONS BY SOME MATRIX MEANS OF FOURIER SERIES W. ŁENSKI AND B. SZAL (Couicaed by

More information

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA Volume 8 No. 8, 45-54 ISSN: 34-3395 (o-lie versio) url: hp://www.ijpam.eu ijpam.eu INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA A.Arul dass M.Dhaapal

More information

Estimation of the Correlation Coefficient for a Bivariate Normal Distribution with Missing Data

Estimation of the Correlation Coefficient for a Bivariate Normal Distribution with Missing Data Kasetsat J. (Nat. Sci. 45 : 736-74 ( Estimation of the Coelation Coefficient fo a Bivaiate Nomal Distibution with Missing Data Juthaphon Sinsomboonthong* ABSTRACT This study poposes an estimato of the

More information

On The Estimation of Two Missing Values in Randomized Complete Block Designs

On The Estimation of Two Missing Values in Randomized Complete Block Designs Mahemaical Theoy and Modeling ISSN 45804 (Pape ISSN 505 (Online Vol.6, No.7, 06 www.iise.og On The Esimaion of Two Missing Values in Randomized Complee Bloc Designs EFFANGA, EFFANGA OKON AND BASSE, E.

More information

Low-complexity Algorithms for MIMO Multiplexing Systems

Low-complexity Algorithms for MIMO Multiplexing Systems Low-complexiy Algoihms fo MIMO Muliplexing Sysems Ouline Inoducion QRD-M M algoihm Algoihm I: : o educe he numbe of suviving pahs. Algoihm II: : o educe he numbe of candidaes fo each ansmied signal. :

More information

Development of Kalman Filter and Analogs Schemes to Improve Numerical Weather Predictions

Development of Kalman Filter and Analogs Schemes to Improve Numerical Weather Predictions Developme of Kalma Filer ad Aalogs Schemes o Improve Numerical Weaher Predicios Luca Delle Moache *, Aimé Fourier, Yubao Liu, Gregory Roux, ad Thomas Warer (NCAR) Thomas Nipe, ad Rolad Sull (UBC) Wid Eergy

More information

Chapter 3 Moments of a Distribution

Chapter 3 Moments of a Distribution Chaper 3 Moes of a Disribuio Epecaio We develop he epecaio operaor i ers of he Lebesgue iegral. Recall ha he Lebesgue easure λ(a) for soe se A gives he legh/area/volue of he se A. If A = (3; 7), he λ(a)

More information

Introduction to Engineering Reliability

Introduction to Engineering Reliability 3 Iroducio o Egieerig Reliabiliy 3. NEED FOR RELIABILITY The reliabiliy of egieerig sysems has become a impora issue durig heir desig because of he icreasig depedece of our daily lives ad schedules o he

More information

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS Joa of Aed Mahema ad Comaoa Meha 4 3( 6-73 APPLCATON OF A Z-TRANSFORMS METHOD FOR NVESTGATON OF MARKOV G-NETWORKS Mha Maay Vo Nameo e of Mahema Ceohowa Uey of Tehoogy Cęohowa Poad Fay of Mahema ad Come

More information

L-functions and Class Numbers

L-functions and Class Numbers L-fucios ad Class Numbers Sude Number Theory Semiar S. M.-C. 4 Sepember 05 We follow Romyar Sharifi s Noes o Iwasawa Theory, wih some help from Neukirch s Algebraic Number Theory. L-fucios of Dirichle

More information

2012 GCE A Level H2 Maths Solution Paper Let x,

2012 GCE A Level H2 Maths Solution Paper Let x, GCE A Level H Maths Solutio Pape. Let, y ad z be the cost of a ticet fo ude yeas, betwee ad 5 yeas, ad ove 5 yeas categoies espectively. 9 + y + 4z =. 7 + 5y + z = 8. + 4y + 5z = 58.5 Fo ude, ticet costs

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon

More information

PRICING AMERICAN PUT OPTION WITH DIVIDENDS ON VARIATIONAL INEQUALITY

PRICING AMERICAN PUT OPTION WITH DIVIDENDS ON VARIATIONAL INEQUALITY Joual of Mahemaical cieces: Aaces a Applicaios olume 37 06 Pages 9-36 Aailable a hp://scieificaacescoi DOI: hp://oiog/0864/msaa_700609 PRICIG AMERICA PUT OPTIO ITH DIIDED O ARIATIOAL IEQUALITY XIAOFAG

More information

Algebra 2A. Algebra 2A- Unit 5

Algebra 2A. Algebra 2A- Unit 5 Algeba 2A Algeba 2A- Ui 5 ALGEBRA 2A Less: 5.1 Name: Dae: Plymial fis O b j e i! I a evalae plymial fis! I a ideify geeal shapes f gaphs f plymial fis Plymial Fi: ly e vaiable (x) V a b l a y a :, ze a

More information

Research Article On Pointwise Approximation of Conjugate Functions by Some Hump Matrix Means of Conjugate Fourier Series

Research Article On Pointwise Approximation of Conjugate Functions by Some Hump Matrix Means of Conjugate Fourier Series Hidawi Publishig Copoaio Joual of Fucio Spaces Volue 5, Aicle ID 475, 9 pages hp://dx.doi.og/.55/5/475 Reseach Aicle O Poiwise Appoxiaio of Cojugae Fucios by Soe Hup Maix Meas of Cojugae Fouie Seies W.

More information

July 24-25, Overview. Why the Reliability Issue is Important? Some Well-known Reliability Measures. Weibull and lognormal Probability Plots

July 24-25, Overview. Why the Reliability Issue is Important? Some Well-known Reliability Measures. Weibull and lognormal Probability Plots Par I: July 24-25, 204 Overview Why he Reliabiliy Issue is Impora? Reliabiliy Daa Paer Some Well-kow Reliabiliy Measures Weibull ad logormal Probabiliy Plos Maximum Likelihood Esimaor 2 Wha is Reliabiliy?

More information

Review - Week 10. There are two types of errors one can make when performing significance tests:

Review - Week 10. There are two types of errors one can make when performing significance tests: Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k =

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k = wwwskshieduciocom BINOMIAL HEOREM OBJEIVE PROBLEMS he coefficies of, i e esio of k e equl he k /7 If e coefficie of, d ems i e i AP, e e vlue of is he coefficies i e,, 7 ems i e esio of e i AP he 7 7 em

More information

Stationarity and Error Correction

Stationarity and Error Correction Saioariy ad Error Correcio. Saioariy a. If a ie series of a rado variable Y has a fiie σ Y ad σ Y,Y-s or deeds oly o he lag legh s (s > ), bu o o, he series is saioary, or iegraed of order - I(). The rocess

More information

UNIVERSITY OF CANTABRIA

UNIVERSITY OF CANTABRIA Summay of Contibutions to the FAD-CDF Compatibility and Y/T Woking Goups UNIVERSITY OF CANTABRIA June 1998 J. Ruiz Ocejo F. Gutiéez-Solana Depatamento de Ciencia e Ingenieía del Teeno y de los Mateiales

More information

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test Research Desig - - Topic Ifereial aisics: The -es 00 R.C. Garer, Ph.D. Geeral Raioale Uerlyig he -es (Garer & Tremblay, 007, Ch. ) The Iepee -es The Correlae (paire) -es Effec ize a Power (Kirk, 995, pp

More information

An Analysis of State Evolution for Approximate Message Passing with Side Information

An Analysis of State Evolution for Approximate Message Passing with Side Information A Aalysis of Sae Evoluio for Approxiae Message Passig wih Side Iforaio Hagji Liu NC Sae Uiversiy Eail: hliu5@csu.edu Cyhia Rush Colubia Uiversiy Eail: cyhia.rush@colubia.edu Dror Baro NC Sae Uiversiy Eail:

More information

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3 Ieraioal Joural of Saisics ad Aalysis. ISSN 48-9959 Volume 6, Number (6, pp. -8 Research Idia Publicaios hp://www.ripublicaio.com The Populaio Mea ad is Variace i he Presece of Geocide for a Simple Birh-Deah-

More information

TWO INTERFACIAL COLLINEAR GRIFFITH CRACKS IN THERMO- ELASTIC COMPOSITE MEDIA

TWO INTERFACIAL COLLINEAR GRIFFITH CRACKS IN THERMO- ELASTIC COMPOSITE MEDIA WO INERFIL OLLINER GRIFFIH RS IN HERMO- ELSI OMOSIE MEDI h m MISHR S DS * Deme o Mheml See I Ie o eholog BHU V-5 I he oee o he le o he e e o eeg o o olle Gh e he ee o he wo ohoo mel e e e emee el. he olem

More information

Improvement Over General And Wider Class of Estimators Using Ranked Set Sampling

Improvement Over General And Wider Class of Estimators Using Ranked Set Sampling ITERATIOAL JOURAL OF SIETIFI & TEOLOG RESEAR VOLUME ISSUE 7 AUGUST ISS 77-866 Iprovee Over Geeral Ad ider lass of Esiaors Usi Raked Se Sapli V L Madoara iu Meha Raka Absrac: I his paper Iprovee over eeral

More information

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω Fourier rasform Coiuous-ime Fourier rasform (CTFT P. Deoe ( he Fourier rasform of he sigal x(. Deermie he followig values, wihou compuig (. a (0 b ( d c ( si d ( d d e iverse Fourier rasform for Re { (

More information

K3 p K2 p Kp 0 p 2 p 3 p

K3 p K2 p Kp 0 p 2 p 3 p Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION AN ALGEBRAIC APPROACH TO -BAN WAELETS CONSTRUCTION Toy L Qy S Pewe Ho Ntol Lotoy o e Peeto Pe Uety Be 8 P. R. C Att T e eet le o to ott - otool welet e. A yte of ott eto ote fo - otool flte te olto e o

More information

A Note on a Characterization of J-Shaped Distribution. by Truncated Moment

A Note on a Characterization of J-Shaped Distribution. by Truncated Moment Applied Mahemaical Scieces Vol. 8 4 o. 7 58-58 HIKARI Ld www.m-hikari.com hp://d.doi.or/.988/ams.4.47556 A Noe o a Characerizaio o J-Shaped Disribuio by Trucaed Mome Mohammad Ahsaullah Deparme o Maaeme

More information