APPLICATION REGRESSION METHOD IN THE CALCULATION OF INDICATORS ECONOMIC RISK
|
|
- Loreen Townsend
- 6 years ago
- Views:
Transcription
1 APPLICATIO REGRESSIO METHOD I THE CALCULATIO OF IDICATORS ECOOMIC RISK Ec. PhD Flor ROMA STA Asrc The ojecve of hs Arcle s o show h ecoomc rsk s flueced mulple fcors, d regresso mehod c eslsh he ee of fluece of ech fcor. For hs purpose hve ee crred ou clculos d ess for dffere umer of commercl compes, d herefore for seres smller or gger, usg umer s less h or greer h depede vrles h c fluece ecoomc rsk, s vrle depede o hese les, s well s regresso mehod, m fcle he process mgeme from he po of vew of oh correcess decsos ke, s well s how m of hose decsos. Ke words: rsk ecoomc, regresso mehod, esmor, depede vrle, depede vrle, smple. Alsg processes he ecoom, lrge umer of cses, mkes ecessr o oservo processes for ll cses or ems populo, whch s ver epesve, eher for smple of cses, whch mples he rsk devo from he rel vlues of he prmeers suded. To mme hs rsk d o o effecve resuls usg low umer of cses, resuls h c e geerled o he whole cses whch he ecoomc lss, he heor esme proposes deermg se for prmeer some og smple, sed o fuco of he oservo(s), whch descres specfc ehvor of he dcor, so s o e ppromo of he projec s precse prmeer. The fuco(s) s cosdered esmor, d he vlue ke hs, esme. If esmorul s defed s formul or mehod whch s cosdered prmeer ukow, esm wll e umerc vlue h resul from pplg he formul o he smple of d h chrcerse he process eg led. For deermo of hose prmeers whch epress o wh ee oe or severl fcors fluece ecoomc process, sscl mehodolog focuses o he regresso fuco. As resul, he esmo fuco prmeers descrg cremel depedece of effec () d fcors () shll e mde usg regresso mehod. Revs Româă de Sscă r. 8 / 03 57
2 I hs respec, sscll, ecoomc rsk sds for vrle reulv depede o oe, wo or more depede vrles fcor comes shll e descred. If o deerme he rsk ecoomcll depede vrle Y, regresso mehod s used, wll uld regresso fuco f(,, ----) o he ss of whch wll eslsh he lk ewee reulv vrle Y d depede vrles,, ---, gvg he regresso equo: Y=f(,, ---). umer depede vrles,, 59,0 ----, m e more or less, depedg o he fcors fluece cosdered. The regresso model ufcorl s used whe he vrle Y operes sgle X, he oher fcors hvg co cos d eglgle. I hs cse, he regresso equo s: Y=f()+ε. Ufcorl regresso model used s: Y=++ε, Where:,=prmeers, he coeffces o e clculed. The prmeers, s esmed usg he mehod of les squres (MCMMP), ccordg o whch, mou of he squres dscrepces ewee pos oserved (cul vlues) d he vlues of o e mml,.e. : mmum = mmum If we cosder ecoomc rsk (RE) he vrle depedece for umer of fve ecoomc u s eg flueced sgle depede vrle, mel he degree of rsk de urde (GR), we wll clcule he prmeers, s follows from Tle : 58 Rom Sscl Revew r. 8 / 03
3 Ecoomc Rsk Suo De Burde d he Degree Of Rsk o umer Of 5 Compes Tle S.C. GR= RE=., 5,5. 0,9 5 3.,5 6 4.,8 6,3 5., 5, 6. 0,3 4, 7. 0,8 5,7 8. 0,6 4,9 9., 6,3 0.,37 6,4.,55 6,75.,8 7,0 3.,95 7, 4.,3 7,46 5.,49 7,63 ^,57 4,06,57 ; ; 5 ^ The coeffce 4, 06 shll e he level of rsk, whch s o deermed he degree of rsk de urde, u lso oher fcors, d he coeffce ^,57 dces h ewee he wo vrles here s drec coeco; o verge, o crese oe u of he degree de urde of rsk, ecoomc rsk creses.57 creds. AOVA d coeffces Revs Româă de Sscă r. 8 / 03 59
4 I he ssem of orml equos: + = + = =5 umer of us oserved =+ or = - Ths mes h he regresso rgh-hd psses hrough he po evrome ( ; ) = ( ) = ; If he vrle depedece o s flueced m vrles, he wll e usg regresso model dsorder wh mulple eologes, ler model whose epresso s gve he relo : Y= 0 p Where : 0 =coeffce epressg fluece of fcors whch re o cluded he model, c e cosdered wh cos co., ; p mulple regresso coeffces, whch shows he shre wh h fluece ech rue fcorl reulve feure o. p, 60 Rom Sscl Revew r. 8 / 03
5 Prmeers,,, 0 p re clculed o he ss of he mehod of les squres. Cosderg re dcors fcl uoom (Rf), d he re of fudg of he socks (Rfs) s wo depede vrles he se whch depeds o he se ecoomc rsk, c e deermed ecoomc rsk mes of ler regresso fuco of wo vrles, usg SPSS. msllce So, for 8 commercl compes hvg s s ojec of cv cosruco merls we hve clculed he wo dcors, fer I hve processed. Relos for he deermo of he wo res shll e: EquCpl Rf EquCpl BorrowedCpl Rf epress he shre ow fcg sources ol cpl vlle. WorkgCpl R fs Socks R fs epress possl of fcg socks of workg cpl. The resuls oed c e see followg lous: Dsefor he purpose of pplg ler regresso Revs Româă de Sscă r. 8 / 03 6
6 The processg of he d ler regresso mehod The ler regresso fuco resulg s: Y= Fuco of he regresso oed s oserved h 0,45 represes he ecoomc rsk whch s o deermed he self-fcg re or he re of fudg of he socks, u lso oher fcors. Bewee re ecoomc rsk d fcl uoom here s drec coeco. So, o crese oe u of he re fcl uoom, ecoomc rsk creses verge of 5,69 creds. Bewee ecoomc rsk d re of fudg of he socks here s coeco reverse order; he cresg re of fudg of he socks wh drve, ecoomc rsk decreses verge of.57 creds. I s foud h he resuls oed re chrcersc of ecoom rso, whch re specfc d cos of oher fcors ecoroll whch m cuse dsurce he opero s usess. 6 Rom Sscl Revew r. 8 / 03
7 Cosderg he dcors : Rf= FR Rfs= S Kpr Kmpr Kpr Where : Rf=re fcl uoom Rfs=re of fudg of S=socks socks, I c e clculed ecoomc rsk mes of ler fuco of wo vrles. Ths fuco wll e wre s follows: ( )+ ( ) Where : ; ; The prmeers, m e deermed he followg ssem of wo equos wh wo ukows : The wo dcors, he re fcl uoom (Rf), d he re of fudg of he socks (Rfs), depede vrles cosdered for egh compes, hve led o deerme he prmeers d s resul of successve ppromos, s s cler from he clculos mde ove. Whe hree depede vrles fluece ecoomc rsk, ler fuco of hree vrles wll e wre s follows : c. Revs Româă de Sscă r. 8 / 03 63
8 Rom Sscl Revew r. 8 / Esmle prmeers,,c he mehod of les squres shll e crred ou he ssem of hree ler equos: c c c I deermg he vlues mmum mmum vrle reulve, usg he mehod ler progrmmg. Geerl form of ler progrmmg prolem s gve fr (mmum or mmum ) of ler fuco of vrles, of he form: f= c c c, Provded h he vrles o ssf ssem of m ler equos ecu or he form: m m m j j ; ; ; 0 m(m)[f]=m(m) j c j j j j j (S): m j ; ; ; 0 Geerl procedure for he resoluo, specfc mehod s ler progrmmg smple, u m e ppled d oher mehods such s:
9 -The mehod grphcs (geomerc), f he vrle reulv s flueced mmum of wo depede vrles; -lgerc mehod, where wo depede vrles c e wre s fuco of he hrd. As pplco of regresso model, I dd for he cosruco of regresso model, for he purpose of llusrg depedece o - ecoomc rsk d dffere res ecoomc, depede vrles. These depede vrles re: Tol fcl A= Curre gross lles, prof Tol fcl lles B= Tol gross prof Ieres epese C= Curre gross prof, Ieres epese D= Tol gross prof, Fcl epeses Fclllesmur er E=, Tol gross prof F= Ieres epese, Turover Ieres epese G=. Fcl lles Afer selecg vrles we dd he cosruco of he regresso fuco. The regresso fuco ecomes: f()=, where: A, B, C, D, E, F, G Revs Româă de Sscă r. 8 / 03 65
10 Ths fuco epresses how he vrle reulv evolves uder he fluece vrle. The vlues of dcors cosdered chrcersed he suo of 6 compes from he po of vew of erlkges ewee cred scorg d dcors ecoomc rsk; do o reflec suos del, u suo frequec curre ecoom codos. Thus, he fcg coss d fcl lles wh mur of less h oe er m o e covered lws from ol gross prof. Cocluso o e drw s h should e crred ou regresso lss for hree versos: vr whch he vlues of dcors fcl eposure re del d sgfc o group of compes effecve, vr whch he re grouped compes hvg regrd suos ecoomc-fcl wek from he po of vew of fcl rsk dcors d he oher vr whch he re represeed compes wh suos he cse of some fvorle o ufvourle dcors d oher dcors of cred scorg. Vlues for he vrle depede o ecoomc rsk (ml. Le) d depede vrles coeffces o he 6 compes re le o.. The Idepede Ad Depede Vrles To A umer Of 6 Compes, ecessr For The Deermo Of The Regresso Fuco Tle r. cr. A B C D E F G RISCEC Rom Sscl Revew r. 8 / 03
11 Usg he progrm SPSS hs ee oed from he followg suo coeffces d of he fuco show he regresso lou o. 4 : The Regresso Coeffces Fuco ^ Ths mes h he regresso fuco s: = A+7.43B C-3.99D+0.753E+4.85F+6.4G B pssg over hs sge of he esme model prmeers, wll follow sgfcce es prmeers esme. I h w, he regresso fuco d shows h : - he coeffce^ =3,99 represes he ecoomc rsk whch s o deermed he 7 depede vrles cosdered, u oher fcors; - he coeffce ^ = -0,094 specfes h ewee ecoomc rsk d he vrle pr of reverse here s coeco: he hgher he coeffce vrle A, he ecoomc rsk s less; - he coeffce c^ = 7,43, whch dces h ewee ecoomc rsk d vrle B here s drec coeco,.e. he hgher he coeffce vrle B wh greer ecoomc rsk; - reverse coeco lso ess ewee ecoomc rsk d vrle D, d ewee rsk d oher vrles C, E, F d G here s drec coeco. The cofdece ervl s deermed for prol of 95 %, resulg he le d he me lms wh whch fll wh he vlues Revs Româă de Sscă r. 8 / 03 67
12 of he coeffces, u lso her vlues whe ccou s ke of spred (he coeffces sdrde). Model vldo s crred ou usg es F, respecvel wh lss dspersole. Model Regresso Resdul Tol AOVA. Predcors: (Cos), G, F, C, E, B, A, D. Depede Vrle: RISCEC Sum of Me Squres df Squre F Sg F clc =7,65 F =4,6 0,05;; 4 F clc F 0,05;; 4 THE MODEL IS VALIDATED AS BEIG ACCEPTABLE. I he followg le shows h properes re me he ler regresso coeffce, whch cofrms he vld ler regresso model. Model Correlos Covrces. Depede Vrle: RISCEC F A F A Coeffce Correlos G F C E B A D Tle r Rom Sscl Revew r. 8 / 03
13 Coclusos Bsc des semmg from delg wh hs opc o he use ler regresso mehod he defco of he fcors fluece ecoomc rsk d of he ee o whch mfes hemselves fluece ech fcor re: - lss relevce requres smple cossg of s m commercl compes; -fcors of fluece of ecoomc rsk c vr re of cv of he comp; -here s o perfec models of rsk lss; -hese models re eses kg o ccou ecoomc coe ol d erol. - usg hs formo ceer compug, processg, erpreo, m e possle o mke esm of he prmeers cosdered d c e performed for predcos. Dowsrem from upsrem, from he resuls o he fcors of fluece, predcos would hve pplcl more h sock records. I s ecessr o mprove he d se for he purposes of use of dcors h rele o us of me less h oe er (qurers, mohs) o cpure more refed, deled effecs. Mus e elrged he referece frmework roducg o greer ee dcors he feld fcl d kg. Bu, s well s hs oel, Fem, s mpor for he upur o recoge prl gorce d leve room dou... o o s s ever ee h we ve come o kow everhg. Blogrph: - Adrew T. Sscs d Ecoomercs, Ecoomc Pulshg House, Buchres, Begu L. S., Mr E. heorecl d ecoomc Sscs, www. dgl-lrr. - The Chr of polcl ecoom, Polcl ecoom, Ecoomc Pulshg House, Buchres, Doroă,. Polcl ecoom - ufed reme of he ssues of people-vl, Ecoomc Pulshg House, Buchres, Isc-M, Al., Mruţ, C., Voegu, V. Sscs for usess mgeme ; Ecoomc Pulshg House, Buchres, Pecc, S. E. Ecoomer... ecoomss Ecoomercs heor d pplcos, Ecoomc Pulshg House, Buchres, 005. Revs Româă de Sscă r. 8 / 03 69
Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationDecompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)
. Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.
More informationIntegral Equations and their Relationship to Differential Equations with Initial Conditions
Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp
More informationBEST PATTERN OF MULTIPLE LINEAR REGRESSION
ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS
More informationLaplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.
Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o
More informationCalculation of Effective Resonance Integrals
Clculo of ffecve Resoce egrls S.B. Borzkov FLNP JNR Du Russ Clculo of e effecve oce egrl wc cludes e rel eerg deedece of euro flux des d correco o e euro cure e smle s eeded for ccure flux deermo d euro
More informationUnscented Transformation Unscented Kalman Filter
Usceed rsformo Usceed Klm Fler Usceed rcle Fler Flerg roblem Geerl roblem Seme where s he se d s he observo Flerg s he problem of sequell esmg he ses (prmeers or hdde vrbles) of ssem s se of observos become
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More informationMultivariate Regression: A Very Powerful Forecasting Method
Archves of Busess Reserch Vol., No. Pulco De: Jue. 5, 8 DOI:.78/r..7. Vslooulos. (8). Mulvre Regresso: A Very Powerful Forecsg Mehod. Archves of Busess Reserch, (), 8. Mulvre Regresso: A Very Powerful
More information4. Runge-Kutta Formula For Differential Equations
NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul
More informationModeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25
Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:
More informationChapter Simpson s 1/3 Rule of Integration. ( x)
Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use
More informationStat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty
S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.
More information4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula
NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul
More informationSTOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION
The Bk of Thld Fcl Isuos Polcy Group Que Models & Fcl Egeerg Tem Fcl Mhemcs Foudo Noe 8 STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION. ก Through he use of ordry d/or prl deres, ODE/PDE c rele
More informationIsotropic Non-Heisenberg Magnet for Spin S=1
Ierol Jourl of Physcs d Applcos. IN 974- Volume, Number (, pp. 7-4 Ierol Reserch Publco House hp://www.rphouse.com Isoropc No-Heseberg Mge for p = Y. Yousef d Kh. Kh. Mumov.U. Umrov Physcl-Techcl Isue
More informationThe Infinite NHPP Software Reliability Model based on Monotonic Intensity Function
Id Jourl of Scece d Techology, Vol 8(4), DOI:.7485/js/25/v84/68342, July 25 ISSN (Pr) : 974-6846 ISSN (Ole) : 974-5645 The Ife Sofwre Relly Model sed o Moooc Iesy Fuco Te-Hyu Yoo * Deprme of Scece To,
More informationChapter Trapezoidal Rule of Integration
Cper 7 Trpezodl Rule o Iegro Aer redg s per, you sould e le o: derve e rpezodl rule o egro, use e rpezodl rule o egro o solve prolems, derve e mulple-segme rpezodl rule o egro, 4 use e mulple-segme rpezodl
More informationLecture 3 summary. C4 Lecture 3 - Jim Libby 1
Lecue su Fes of efeece Ivce ude sfoos oo of H wve fuco: d-fucos Eple: e e - µ µ - Agul oeu s oo geeo Eule gles Geec slos cosevo lws d Noehe s heoe C4 Lecue - Lbb Fes of efeece Cosde fe of efeece O whch
More informationMODELING AND FORECASTING THE TEXTILE PRICE INDEX USING SEMI-NONPARAMETRIC REGRESSION TECHNIQUE
Ierol Jourl of Iovve Mgeme Iformo & Produco ISME Ierol c 4 ISSN 85-5455 Volume 5 Numer Mrch 4 PP. 89-98 MODELING ND FORECSTING THE TEXTILE PRICE INDEX USING SEMI-NONPRMETRIC REGRESSION TECHNIQUE JINGHUI
More informationIntroduction to Neural Networks Computing. CMSC491N/691N, Spring 2001
Iroduco o Neurl Neorks Compug CMSC49N/69N, Sprg 00 us: cvo/oupu: f eghs: X, Y j X Noos, j s pu u, for oher us, j pu sgl here f. s he cvo fuco for j from u o u j oher books use Y f _ j j j Y j X j Y j bs:
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationOptimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek
Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationFORCED VIBRATION of MDOF SYSTEMS
FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me
More informationMidterm Exam. Tuesday, September hour, 15 minutes
Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.
More informationTechnical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005.
Techc Appedx fo Iveoy geme fo Assemy Sysem wh Poduc o Compoe eus ecox d Zp geme Scece 2005 Lemm µ µ s c Poof If J d µ > µ he ˆ 0 µ µ µ µ µ µ µ µ Sm gumes essh he esu f µ ˆ > µ > µ > µ o K ˆ If J he so
More informationAn improved Bennett s inequality
COMMUNICATIONS IN STATISTICS THEORY AND METHODS 017,VOL.0,NO.0,1 8 hps://do.org/10.1080/0361096.017.1367818 A mproved Bee s equly Sogfeg Zheg Deprme of Mhemcs, Mssour Se Uversy, Sprgfeld, MO, USA ABSTRACT
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationFinal Exam Applied Econometrics
Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc
More informationWeek 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)
Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he
More informationAnalysis of the Preference Shift of. Customer Brand Selection. and Its Matrix Structure. -Expansion to the second order lag
Jourl of Compuo & Modellg vol. o. 6-9 ISS: 79-76 (pr) 79-88 (ole) Scepre Ld l of he Preferece Shf of Cuomer Brd Seleco d I Mr Srucure -Epo o he ecod order lg Kuhro Teu rc I ofe oerved h coumer elec he
More informationChapter 2. Review of Hydrodynamics and Vector Analysis
her. Ree o Hdrodmcs d Vecor Alss. Tlor seres L L L L ' ' L L " " " M L L! " ' L " ' I s o he c e romed he Tlor seres. O he oher hd ' " L . osero o mss -dreco: L L IN ] OUT [mss l [mss l] mss ccmled h me
More informationDATA FITTING. Intensive Computation 2013/2014. Annalisa Massini
DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg
More informationASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES
ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl
More informationModified Taylor's Method and Nonlinear Mixed Integral Equation
Uversl Jourl of Iegrl quos 4 (6), 9 wwwpperscecescom Modfed Tylor's Mehod d oler Mxed Iegrl quo R T Moog Fculy of Appled Scece, Umm Al Qurh Uversy Mkh, Kgdom of Sud Ar rmoog_777@yhoocom Asrc I hs pper,
More informationIn Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is
Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I
More informationθ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:
Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log
More information-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for
Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationHow to explore replicator equations? G.P. Karev
How o explore replcor equos? GP Krev Locheed r SD Nol Isue of Helh Bldg 38 R 5N5N 86 Rocvlle Pe Behes D 2894 US E-l: rev@clhgov src Replcor equos RE) re og he sc ools hecl heory of seleco d evoluo We develop
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationPreliminary Examinations: Upper V Mathematics Paper 1
relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0
More informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationI I M O I S K J H G. b gb g. Chapter 8. Problem Solutions. Semiconductor Physics and Devices: Basic Principles, 3 rd edition Chapter 8
emcouc hyscs evces: Bsc rcles, r eo Cher 8 oluos ul rolem oluos Cher 8 rolem oluos 8. he fwr s e ex f The e ex f e e f ex () () f f f f l G e f f ex f 59.9 m 60 m 0 9. m m 8. e ex we c wre hs s e ex h
More informationIMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS
Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &
More informationThe z-transform. LTI System description. Prof. Siripong Potisuk
The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationNumerical Analysis Topic 4: Least Squares Curve Fitting
Numerl Alss Top 4: Lest Squres Curve Fttg Red Chpter 7 of the tetook Alss_Numerk Motvto Gve set of epermetl dt: 3 5. 5.9 6.3 The reltoshp etwee d m ot e ler. Fd futo f tht est ft the dt 3 Alss_Numerk Motvto
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS
Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss
More informationArea and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]
Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles
More informationOPTIMAL BUS DISPATCHING POLICY UNDER VARIABLE DEMAND OVER TIME AND ROUTE LENGTH
OPTIMAL BUS DISPATCHING POLICY UNDE VAIABLE DEMAND OVE TIME AND OUTE LENGTH Prof. Aml S. Kumrge Professor of Cvl Egeerg Uvers of Moruw, Sr L H.A.C. Perer Cvl Egeer Cerl Egeerg Cosulc Bureu, Sr L M.D..P.
More informationSolution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations
Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationAn Investigation on Effective Factors on Share of Agricultural Sector in GDP of Iranian Economy
Ierol Reserch Jourl of ppled d Bsc Sceces 03 vlble ole www.rbs.com ISS 5-838X / Vol, 7 (3): 034-04 Scece xplorer Publcos Ivesgo o ffecve Fcors o Shre of grculurl Secor GDP of Ir coomy Sed khodbkhshzdeh*,morez
More informationOptimal Eye Movement Strategies in Visual Search (Supplement)
Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case
More informationCURVE FITTING LEAST SQUARES METHOD
Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued
More informationExpectation and Moments
Her Sr d Joh W. Woods robbl Sscs d Rdom Vrbles or geers 4h ed. erso duco Ic.. ISB: 978----6 Cher 4 eco d omes Secos 4. eced Vlue o Rdom Vrble 5 O he Vld o quo 4.-8 8 4. Codol ecos Codol eco s Rdom Vrble
More informationCouncil for Innovative Research Peer Review Research Publishing System
ISSN 47-9 Oscllo Crer For Eve Order Noler Nerl Dfferel Eos Wh Med Argmes ABSTRACT E Thd SPdmvh S Pels Rmj Ise for Advced Sd Mhemcs Uvers of Mdrs Che 600 005 Id ehd@hooco Acdem Mlr Dermeo de Cêcs Ecs e
More informationMTH 146 Class 7 Notes
7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg
More informationRedundancy System Fault Sampling Under Imperfect Maintenance
A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce
More informationP-Convexity Property in Musielak-Orlicz Function Space of Bohner Type
J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg
More informationSt John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION
St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of
More informationEfficient Estimators for Population Variance using Auxiliary Information
Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav
More informationReview for the Midterm Exam.
Review for he iderm Exm Rememer! Gross re e re Vriles suh s,, /, p / p, r, d R re gross res 2 You should kow he disiio ewee he fesile se d he udge se, d kow how o derive hem The Fesile Se Wihou goverme
More information1. Consider an economy of identical individuals with preferences given by the utility function
CO 755 Problem Se e Cbrer. Cosder ecoomy o decl dduls wh reereces e by he uly uco U l l Pre- rces o ll hree oods re ormled o oe. Idduls suly ood lbor < d cosume oods d. The oerme c mose d lorem es o oods
More informationRATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA
The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao
More informationthis is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]
Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow
More informationKey words: Fractional difference equation, oscillatory solutions,
OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg
More information13. DYNAMIC ANALYSIS USING MODE SUPERPOSITION
. DYAMI AALYI UIG MODE UPEPOIIO he Mode hes used o Ucoule he Dmc Equlrum Equos eed o Be he Exc Free-Vro Mode hes. EQUAIO O BE OLVED { XE "Mode hes" }{ XE "Mode ueroso Alss" }{ XE "Pece-Wse Ler Lodg" }he
More informationAn Adaptation of the Scheifele Method to Stiff Systems of Differential Equations
86 he Ope Appled Mhecs Jourl 8 86-94 Ope Access A Adpo of he Schefele Mehod o Sff Syses of Dfferel Equos J.A. Reyes F. Grcí-Aloso d Y. Vllcp* Depre of Appled Mhecs. Hgher Polyechc School (EPS). Uversy
More informationA New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering
Sed Orders for Reprs o reprs@behmscece.e The Ope Cyberecs & Sysemcs Jourl, 04, 8, 89-834 89 Ope ccess New NFIS Model bsed o Mul-Ipu Hmcher T-orm Subrc Cluserg Feg-Y Zhg Zh-Go Lo * Deprme of Mgeme, Gugx
More informationLecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination
Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of
More informationA NEW FIVE-POINT BINARY SUBDIVISION SCHEME WITH A PARAMETER
Jourl of ure d Appled Mhemcs: Advces d Applcos Volume 9 Numer ges -9 Avlle hp://scefcdvcesco DOI: hp://dxdoorg/6/ms_9 A NEW FIVE-OINT BINARY UBDIVIION CHEME WITH A ARAMETER YAN WANG * d HIMING LI chool
More informationTEACHERS ASSESS STUDENT S MATHEMATICAL CREATIVITY COMPETENCE IN HIGH SCHOOL
Jourl o See d rs Yer 5, No., pp. 5-, 5 ORIGINL PPER TECHERS SSESS STUDENT S MTHEMTICL CRETIVITY COMPETENCE IN HIGH SCHOOL TRN TRUNG TINH Musrp reeved: 9..5; eped pper:..5; Pulsed ole:..5. sr. ssessme s
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationSoo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:
Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1
More informationFor the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.
The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe
More informationThe Products of Regularly Solvable Operators with Their Spectra in Direct Sum Spaces
Advces Pure Mhemcs 3 3 45-49 h://dxdoorg/436/m3346 Pulshed Ole July 3 (h://wwwscrorg/ourl/m) he Producs of Regulrly Solvle Oerors wh her Secr Drec Sum Sces Sohy El-Syed Irhm Derme of Mhemcs Fculy of Scece
More informationNONLINEAR SYSTEM OF SINGULAR PARTIAL DIFFERENTIAL EQUATIONS
Jourl of Mhemcl Sceces: dvces d pplcos Volume 43, 27, Pges 3-53 vlble hp://scefcdvces.co. DOI: hp://d.do.org/.8642/ms_72748 OLIER SYSTEM OF SIGULR PRTIL DIFFERETIL EQUTIOS PTRICE POGÉRRD Mhemcs Lborory
More informationKINEMATICS OF RIGID BODIES RELATIVE VELOCITY RELATIVE ACCELERATION PROBLEMS
KINEMTICS OF RIGID ODIES RELTIVE VELOCITY RELTIVE CCELERTION PROLEMS 1. The crculr dsk rolls o he lef whou slppg. If.7 m s deerme he eloc d ccelero of he ceer O of he dsk. (516) .7 m s O? O? . The ed rollers
More informationModel for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts
Joural of Evromeal cece ad Egeerg A 7 (08) 8-45 do:0.765/6-598/08.06.00 D DAVID UBLIHING Model for Opmal Maageme of he pare ars ock a a Irregular Dsrbuo of pare ars veozar Madzhov Fores Research Isue,
More informationAuthor(s) Guenfoud, Salah; Bosakov, Sergey V.; Laefer, Debra F.
Provded he uhor(s) d Uvers College Dul Lrr orde wh pulsher poles. Plese e he pulshed verso whe vlle. Tle Dm lss of ple resg o els hlf-spe wh dsruve properes Auhor(s) Guefoud Slh; Boskov Serge V.; Lefer
More informationDirect Current Circuits
Eler urren (hrges n Moon) Eler urren () The ne moun of hrge h psses hrough onduor per un me ny pon. urren s defned s: Dre urren rus = dq d Eler urren s mesured n oulom s per seond or mperes. ( = /s) n
More informationMathematical Formulation
Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg
More informationCooper and McGillem Chapter 4: Moments Linear Regression
Cooper d McGllem Chpter 4: Momets Ler Regresso Chpter 4: lemets of Sttstcs 4-6 Curve Fttg d Ler Regresso 4-7 Correlto Betwee Two Sets of Dt Cocepts How close re the smple vlues to the uderlg pdf vlues?
More informationModule 2: Introduction to Numerical Analysis
CY00 Itroducto to Computtol Chemtr Autum 00-0 Module : Itroducto to umercl Al Am of the preet module. Itroducto to c umercl l. Developg mple progrm to mplemet the umercl method opc of teret. Iterpolto:
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More informationINTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY
[Mjuh, : Jury, 0] ISSN: -96 Scefc Jourl Impc Fcr: 9 ISRA, Impc Fcr: IJESRT INTERNATIONAL JOURNAL OF ENINEERIN SCIENCES & RESEARCH TECHNOLOY HAMILTONIAN LACEABILITY IN MIDDLE RAPHS Mjuh*, MurlR, B Shmukh
More informationCompetitive Facility Location Problem with Demands Depending on the Facilities
Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg
More informationCOMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION
COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue
More informationChapter Gauss-Seidel Method
Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos
More information4. THE DENSITY MATRIX
4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o
More informationRoberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums
Roerto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 2 Rem sums Wht you eed to kow lredy: The defto of re for rectgle. Rememer tht our curret prolem s how to compute the re of ple rego
More information