DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED PROGRAMMING APPROACH. 1. Introduction
|
|
- Angelica Merritt
- 5 years ago
- Views:
Transcription
1 Iaa Joa of Fzz Sstes Vo. No DAA ENVEOPMEN ANAYSIS WIH FUZZY ANDOM INPUS AND OUPUS: A CHANCE-CONSAINED POGAMMING APPOACH S. AMEZANZADEH M. MEMAIANI AND S. SAAI ABSAC. I ths ae we dea wth fzz ado vaabes fo ts ad otts Data Eveoet Aass DEA. hese vaabes ae cosdeed as fzz ado fat bes wth kow dstbto. he obe s to fd a ethod fo covetg the ecse chace-costaed DEA ode to a cs oe. hs ca be doe b fst defzzfcato of ecse obabt b costctg a stabe ebesh fcto secod defzzfcato of the aaetes sg a -ct ad fa covetg the chace-costaed DEA to a cs ode sg the ethod of Cooe [4].. Itodcto Most eseach wok DEA deas wth ecse ad detestc foato. he aes eated to cetat ae ethe dea wth ado foato see [ &8] o fzz foato fo ts ad otts see [9 & ]. Howeve we have ot otced a cotbto that a cooate the hbd cetat.e. fzzess ad adoess DEA. Whe the aaetes ts ad otts of a DEA ode ae fzz ado vaabes we have a fzz chace-costaed DEA obe. heefoe we cosde a DEA ode to evaate the effcec of DMUs whe data ae fzz ado vaabes. I addto the obabt of the costats a aso be descbed as fzz eato. Kawakeaak [3] ad P & aesc [9] todced fzz ado vaabes ad Gaga & Ye [7&8] [4&5] Chakabot [&3] ad hada [6&7] eseted soe otat ethods fo sovg atheatca ogag wth fzz ado vaabe coeffcets. I ths ae we cosde the CC ode of DEA wth chace-costaed ogag aoach whch ts ad otts ae fzz ado vaabes; we asse that these fzz ado vaabes ae fat fzz bes. O obectve s to covet the fzz chace-costat DEA to cs DEA. Fo ths ose fst of a the ode s defzzfed b sg a stabe ebesh fcto fo the fzz eato of the obabt. I the secod stage the fzzess of the aaetes s eoved b a -ct aoach ad fa the adoess s ectfed b cassca ea-vaace ethod of Cooe [4]. he stcte of ths ae s as foows: Secto esets the fzz eceved: Novebe 4; Acceted: Je 5 Ke wods ad hases: Data eveoet aass Chace-costaed DEA Fzz ado vaabe aga fzz be.
2 S. aezazadeh M. Meaa ad S. Saat chace-costaed DEA ad the ocede of ts coveso to a cs DEA ode. o deostate the ocess a eca eae s gve secto 3. Secto 4 cossts of a cocso.. Fzz Chace Costaed DEA We foate the fzz chace-costaed DEA as foows: et... ad... eeset s a s fzz ado t ad ott vectos fo each DMU whee ; & ; s ae fat fzz ado vaabes eated to a ado vaabe ad ae the eft ad ght seads. We eset the fzz chace-costaed CC ode as foows: M s.t : Pob Pob > > s... "> " sgfes that the costats ae fzz satsfed wth obabt. Net we deostate a vew of the effcet fote of sch a ode fo sge t ad sge ott. Fo sct of esetato we cosde to be a ado vaabe ad to be a taga fzz ado vaabe. I Fge the es ad 3 eeset the e at ea ad the ote at of the effcec fote. Each -ct tesects ths fote ado vaabes. Fo stace the abscssa of the ot fo s a ado vaabe wth dstbto N σ ad ts odate s a ado vaabe wth dstbto N σ. I what foows the ocess of coveso has bee deveoed thee hases. I hase I the ecse obabt s defzzfed. I hase II defzzfcato of the aaetes s caed ot theeb covetg the obe to a chace-costaed
3 Data Eveoet Aass wth Fzz ado Its ad Otts: A Chace-costaed Pogag Aoach 3 DEA. Fa the coveso of ths chace-costaed DEA to a cs ode s efoed hase III. DMU N µ σ N σ 3 FIGUE. Effcec fote of a fzz chace-costaed CC ode. Phase I: It s cea that evets. Fo sct et ad C deote the evet ae fzz. he ebesh fcto µ eas the gade to whch... ob fts the costats. he obabt of ths fzz evet s the ease of the degee to whch s eabe. Accodg to [] we defe the ebesh fcto fo the Pob C > as foows: whee µ Pob f Pob C Pob C f Pob C othewse s the toeace of the obabt. It s evdet that: Pob C
4 S. aezazadeh M. Meaa ad S. Saat 4 heefoe... Pob ad sa...s Pob So ca be coveted to:... s... Pob... Pob : s.t M Phase II: I ths hase the fzzess of the coeffcets ae deat wth. Fo ths we a the cocet of -ct as []. B todcg the -ct of costats ad sato of fat fzz bes we w have the foowg obe: : s.t M [ ]... Pob [ ] s... Pob... 3
5 Data Eveoet Aass wth Fzz ado Its ad Otts: A Chace-costaed Pogag Aoach 5 Accodg to Saat et a. [] to evaate the effcec of DMUs the owe eve of ts ad e eve of otts that s the best at of DMUs fo each DMU ae coaed wth the e at of effcec fote. he best at of a DMU s ad the e at of the fote s. heefoe ode 3 ca be wtte as foows: 4 hs obe s a aaetc chace-costaed DEA whe ] s a aaete. Phase III: Now we ca covet the chace-costaed DEA 4 to the foowg cs oea ogag b Cooe [4]: s s.t : M σ ϕ σ ϕ Pob... Pob : s.t M s
6 6 S. aezazadeh M. Meaa ad S. Saat whee : s the ea of the ado vaabe ϕ s the stadad oa dstbto fcto ϕ s vese of ϕ σ σ k Va Va Cov Cov k Cov Cov. k k k We have a ota soto fo each. hs fo dffeet ] the ota sotos ca be obtaed based o the choce of decso ake wth egad to ad the aoate soto a be seected. he o-eat 5 s de to σ adσ. he σ ad [... ] V [... ] t σ [... ] V [... ] t σ σ ae as foows: whee V ad V ae vaace-covaace atces fo each set of the costats esectve. Sce these atces ae ostve defte so σ ad σ ae cove []. heefoe we ca ca that the obe 5 s cove ogag obe. As a seca case whe the data ae taga fzz ado bes.e. ad ; s the ode 5 s as foows:
7 Data Eveoet Aass wth Fzz ado Its ad Otts: A Chace-costaed Pogag Aoach 7 M s.t : ϕ σ ϕ σ s whee σ σ k Cov Va Va Cov k Cov Cov. k k 6 3. Istato Eae he effcec of 4 fas D D D 3 ad D 4 wth aeas of ad 7 aces esectve ae to be evaated. I a the fas the co ctvated s wheat. he aot of the ed s a ado vaabe oa dstbted wth ea ad 3.5. he vaace s fo a. he aot of afa s estated as a fzz ado vaabe wth aaetes ρ ρ ρ ρ γ ad η η η η whee N6 ρ N γ N4 ad η N.5. he ed s the ott of the ode ad the aea ad afa ae ts. he data ae sted tabe ad the effceces of DMUs wth the oosed ethod fo dffeet vaes ae sted abe.
8 8 S. aezazadeh M. Meaa ad S. Saat I I O D D D 3 D 4 5 N.5 ρ 5 N 3 ABE. Data fo Neca Eae γ 4 N 5 η 7 N 3.5 D D D 3 D ABE. he Effceces b Poosed Method As see the effceces ae deceased b ceasg bt D 3 s effcet fo a. I case of 6 s evaet to the chace-costaed CC ode. Ftheoe DMUs ae aked as D 3 D D ad D Cocso I ths ae a CC ode s sggested fo chace-costaed DEA wth fzz ado data. We asse that the fzz ado vaabes ae fat fzz bes. Fo o-ea cases afte -ct the eato 3 ad 4 st be odfed accodg to the dstbto of the fzz be. We oose a ethod fo covetg ths obe to a cs chace-costaed DEA ode based o -ct ad fzz obabt ease. I ths ethod the owe eve of ts ad e eve of otts ae coaed wth the e at of effcec fote. he statve eae shows the acabt of the ode. It s sggested that the effcec of ths agoth be stded fo age obes. Ackowedget. he athos wod ke to thak the aoos efeee whose coets oved the at of the ae. EFEENCES [] A. Chaes W. W. Cooe ad G. Y Modes fo deag wth ecse data DEA Maaget Scece [] D. Chakabot J.. ao ad. N. wa Mtobectve ecse chace-costaed ogag obe J. Fzz Math Coged to: Mtobectve ecse-chace costaed ogag obe J. Fzz Math [3] D. Chakabot edefg chace-costaed ogag fzz evoet Fzz Sets ad Sstes [4] W. W. Cooe H. Deg Z. M. Hag ad S. X. Satsfg DEA odes de Chace costats he Aas of Oeatos eseach a
9 Data Eveoet Aass wth Fzz ado Its ad Otts: A Chace-costaed Pogag Aoach 9 [5] W. W. Cooe H. Deg Z. M. Hag ad S. X. Chace costaed ogag aoaches to techca effceces ad effceces stochastc data eveoaet aass Joa of the Oeatoa eseach Socet 53 a [6] W. W. Cooe H. Deg Z. M. Hag ad S. X. Chace costaed ogag aoaches to cogesto stochastc data eveoaet aass Eoea Joa of Oeatoa eseach [7] W. Gagva ad Z. Ye he theo of fzz stochastc ocesses Fzz Sets ad Sstes [8] W. Gaga ad Q. Zhog ea ogag wth fzz ado vaabe coeffcets FSS [9] P. Gao ad H. aaka Fzz DEA : A eeceta evaato ethod Fzz Sets ad Sstes [] J.. Hogaad Fzz scoes of techca effcec Eoea Joa of Oeato eseach [] P. Ka ad S. W. Waace Stochastc Pogag Joh We &Sos New Yok 994. [] C. Kao ad S.. Fzz Effcec Meases Data Eveoet Aass Fzz Sets ad Sstes [3] H. Kwakeaak Fzz ado vaabes deftos ad theoes If. Sc [4] B. Fzz ado chace-costaed ogag IEEE asactos o Fzz Sstes [5] B. Fzz ado deedet-chace ogag IEEE asactos o Fzz Sstes [6] M.K. hada Fzzess ad adoess a otzato faewok Fzz Sets ad Sstes [7] M. K. hada ad M. M.Gta O fzz stochastc otzato Fzz Sets ad Sstes [8] O.B. Oese ad N. C. Petese Chace costaed effcec evaato Maageet Scece [9] M.. P ad D.A. aesc Fzz ado vaabes J.Math. Aa. A [] S. Saat A. Meaa ad G.. Jahashahoo Effcec aass ad akg of DMUs wth fzz data Fzz Otzato ad Decso Makg [] B. Seave ad K. ats A fzz csteg aoach sed evaatg techca effcec eases afactg Joa of odctvt Aass [] J. K. Segta A Fzz Sste Aoach Data Eveoet Aass Cotes Math. Ac SAEED AMEZANZADEH* DEPAMEN OF MAHEMAICS POICE UNIVESIY EHAN IAN E-a addess: aezazadeh_s@ahoo.co AZIZOAH MEMAIANI DEPAMEN OF INDUSIA ENGINEEING BU-AI SINA UNIVESIY HAMEDAN IAN E-a addess: a_eaa@ahoo.co SABE SAAI DEPAMEN OF MAHEMAICS EHAN NOH BANCH ISAMIC AZAD UNIVESIY EHAN IAN E-a addess: ssaat@ahoo.co * COESPONDING AUHO O.. GOUP INSIUE FO FUNDAMENA ESEACH IMAM-HOSSEIN UNIVESIY EHAN IAN
NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS
HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, See A, OF HE ROMANIAN ACADEMY Volue 9, Nube 3/8,. NONDIFFERENIABLE MAHEMAICAL PROGRAMS. OPIMALIY AND HIGHER-ORDER DUALIY RESULS Vale PREDA Uvety
More informationA PAIR OF HIGHER ORDER SYMMETRIC NONDIFFERENTIABLE MULTIOBJECTIVE MINI-MAXMIXED PROGRAMMING PROBLEMS
Iteatoal Joal of Cote Scece ad Cocato Vol. 3, No., Jaa-Je 0,. 9-5 A PAIR OF HIGHER ORDER SYMMERIC NONDIFFERENIABLE MULIOBJECIVE MINI-MAXMIXED PROGRAMMING PROBLEMS Aa Ka ath ad Gaat Dev Deatet of Matheatcs,
More informationare positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.
Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called
More informationˆ SSE SSE q SST R SST R q R R q R R q
Bll Evas Spg 06 Sggested Aswes, Poblem Set 5 ECON 3033. a) The R meases the facto of the vaato Y eplaed by the model. I ths case, R =SSM/SST. Yo ae gve that SSM = 3.059 bt ot SST. Howeve, ote that SST=SSM+SSE
More informationXII. Addition of many identical spins
XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.
More information= y and Normed Linear Spaces
304-50 LINER SYSTEMS Lectue 8: Solutos to = ad Nomed Lea Spaces 73 Fdg N To fd N, we eed to chaacteze all solutos to = 0 Recall that ow opeatos peseve N, so that = 0 = 0 We ca solve = 0 ecusvel backwads
More informationTuning of Centroid with Modified Kernik Mendel (KM) Algorithm
Iteatoa Joua of odeg ad Otzato, Vo., o. 6, Decebe 01 Tug of Cetod wth odfed Ke ede (K goth Saat at ad Jaa S, ebe, ICSIT bstact Cetod coutato of teva te- fuzz ogc sste, s a oeeg wo that ovdes a easue of
More information( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi
Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)
More informationConsumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle
Thomas Soesso Mcoecoomcs Lecte Cosme theoy A. The efeece odeg B. The feasble set C. The cosmto decso A. The efeece odeg Cosmto bdle x ( 2 x, x,... x ) x Assmtos: Comleteess 2 Tastvty 3 Reflexvty 4 No-satato
More informationProfessor Wei Zhu. 1. Sampling from the Normal Population
AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple
More informationExponential Generating Functions - J. T. Butler
Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle
More informationχ be any function of X and Y then
We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,
More informationAn Enhanced Russell Measure of Super-Efficiency for Ranking Efficient Units in Data Envelopment Analysis
Aeca Joual of Appled Sceces 8 (): 92-96, 20 ISSN 546-9239 200 Scece Publcatos A Ehaced Russell Measue of Supe-Effcecy fo Rakg Effcet Uts Data Evelopet Aalyss,2 Al Ashaf,,3 Az B Jaafa,,4 La Soo Lee ad,4
More informationRECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S
Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets
More informationLecture 2: The Simple Regression Model
Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.
More informationImages of Linear Block Codes over Fq ufq vfq uvfq
Oe Joua of ed Sceces, 03, 3, 7-3 do:036/oas033006 Pubshed Oe 03 (htt://wwwscog/oua/oas) Iages of Lea oc Codes ove u v uv Jae D Paaco, Vgo P Sso Isttute of Matheatca Sceces ad Physcs, Uvesty of the Phes
More informationA Deterministic Model for Channel Capacity with Utility
CAPTER 6 A Detestc Model fo Chel Cct wth tlt 6. todcto Chel cct s tl oeto ssocted wth elble cocto d defed s the hghest te t whch foto c be set ove the chel wth btl sll obblt of eo. Chel codg theoes d the
More informationAbstract. 1. Introduction
Joura of Mathematca Sceces: Advaces ad Appcatos Voume 4 umber 2 2 Pages 33-34 COVERGECE OF HE PROJECO YPE SHKAWA ERAO PROCESS WH ERRORS FOR A FE FAMY OF OSEF -ASYMPOCAY QUAS-OEXPASVE MAPPGS HUA QU ad S-SHEG
More informationOn EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx
Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.
More informationFIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES
Joual of Appled Matheatcs ad Coputatoal Mechacs 7, 6(), 59-7 www.ac.pcz.pl p-issn 99-9965 DOI:.75/jac.7..3 e-issn 353-588 FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL
More informationFuzzy Erlangian Queuing System with State Dependent Service Rate, Balking, Reneging and Retention of Reneged customers
Iteatioa Joa of Basic & Aied Scieces IJBAS-IJENS Vo:4 No: 6 Fzzy Eagia Qeig System with State Deedet Sevice Rate Bakig Reegig ad Retetio of Reeged cstomes MS E Paomy Deatmet of Statistics Facty of Commece
More informationsuch that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1
Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9
More informationOn Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution
ISSN 684-843 Joua of Sac Voue 5 8 pp. 7-5 O Pobaby Dey Fuco of he Quoe of Geeaed Ode Sac fo he Webu Dbuo Abac The pobaby dey fuco of Muhaad Aee X k Y k Z whee k X ad Y k ae h ad h geeaed ode ac fo Webu
More informationSUBSEQUENCE CHARACTERIZAT ION OF UNIFORM STATISTICAL CONVERGENCE OF DOUBLE SEQUENCE
Reseach ad Coucatos atheatcs ad atheatcal ceces Vol 9 Issue 7 Pages 37-5 IN 39-6939 Publshed Ole o Novebe 9 7 7 Jyot cadec Pess htt//yotacadecessog UBEQUENCE CHRCTERIZT ION OF UNIFOR TTITIC CONVERGENCE
More informationOn Submanifolds of an Almost r-paracontact Riemannian Manifold Endowed with a Quarter Symmetric Metric Connection
Theoretcal Mathematcs & Applcatos vol. 4 o. 4 04-7 ISS: 79-9687 prt 79-9709 ole Scepress Ltd 04 O Submafolds of a Almost r-paracotact emaa Mafold Edowed wth a Quarter Symmetrc Metrc Coecto Mob Ahmad Abdullah.
More informationSTABILIZATION OF A CLASS OF NONLINEAR MODEL WITH PERIODIC
SABILIZAION OF A CLASS OF NONLINEAR MODEL WIH PERIODIC PARAMEERS IN HE AKAGI-SUGENO FORM A KRUSZEWSKI M GUERRA LAMIH UMR CNRS 853 Uvesté de Vaecees et du Haaut-Cambéss Le Mot Houy 5933 Vaecees Cedex 9
More informationConsider two masses m 1 at x = x 1 and m 2 at x 2.
Chapte 09 Syste of Patcles Cete of ass: The cete of ass of a body o a syste of bodes s the pot that oes as f all of the ass ae cocetated thee ad all exteal foces ae appled thee. Note that HRW uses co but
More informationVECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles.
Seeth Edto CHPTER 4 VECTOR MECHNICS FOR ENINEERS: DYNMICS Fedad P. ee E. Russell Johsto, J. Systems of Patcles Lectue Notes: J. Walt Ole Texas Tech Uesty 003 The Mcaw-Hll Compaes, Ic. ll ghts eseed. Seeth
More informationOn The Circulant K Fibonacci Matrices
IOSR Jou of Mthetcs (IOSR-JM) e-issn: 78-578 p-issn: 39-765X. Voue 3 Issue Ve. II (M. - Ap. 07) PP 38-4 www.osous.og O he Ccut K bocc Mtces Sego co (Deptet of Mthetcs Uvesty of Ls Ps de G C Sp) Abstct:
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationIncreasing the Image Quality of Atomic Force Microscope by Using Improved Double Tapered Micro Cantilever
Rece Reseaces Teecocaos foacs Eecocs a Sga Pocessg ceasg e age Qa of oc Foce Mcope Usg pove oe Tapee Mco aeve Saeg epae of Mecaca Egeeg aava Bac sac za Uves aava Tea a a_saeg@aavaa.ac. sac: Te esoa feqec
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 informationChapter 2: Descriptive Statistics
Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate
More informationBest Linear Unbiased Estimators of the Three Parameter Gamma Distribution using doubly Type-II censoring
Best Lea Ubased Estmatos of the hee Paamete Gamma Dstbuto usg doubly ype-ii cesog Amal S. Hassa Salwa Abd El-Aty Abstact Recetly ode statstcs ad the momets have assumed cosdeable teest may applcatos volvg
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationCOMP 465: Data Mining More on PageRank
COMP 465: Dt Mnng Moe on PgeRnk Sldes Adpted Fo: www.ds.og (Mnng Mssve Dtsets) Powe Iteton: Set = 1/ 1: = 2: = Goto 1 Exple: d 1/3 1/3 5/12 9/24 6/15 = 1/3 3/6 1/3 11/24 6/15 1/3 1/6 3/12 1/6 3/15 Iteton
More informationA GENERAL CLASS OF ESTIMATORS UNDER MULTI PHASE SAMPLING
TATITIC IN TRANITION-ew sees Octobe 9 83 TATITIC IN TRANITION-ew sees Octobe 9 Vol. No. pp. 83 9 A GENERAL CLA OF ETIMATOR UNDER MULTI PHAE AMPLING M.. Ahed & Atsu.. Dovlo ABTRACT Ths pape deves the geeal
More informationA New Batch FHE Scheme over the Integers
A New Bth FHE Shee oe the Iteges Kw W Sog K Cho U Shoo of Mthets K I Sg Uesty yogyg Deot eoe s e of Koe Astt The FHE (fy hoooh eyto) shees [7 3] sed o the odfed AGCD oe (osefee AGCD oe) e ee to t tts ese
More information1. Introduction. Georgiy M. Levchuk 1, Feili Yu 2, Yuri Levchuk 1, and Krishna R. Pattipati 2 ABSTRACT
etwos of Decso-ag a oucatg Agets: A ew ethooog fo Desg a Evauato of Ogazatoa Stateges a Heteachca Stuctues* Geog Levchu Fe Yu Yu Levchu a Ksha R Pattat Ata Ic G Steet Sute Wobu A Uvest of oectcut Det of
More informationThe 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 information2/24/2014. The point mass. Impulse for a single collision The impulse of a force is a vector. The Center of Mass. System of particles
/4/04 Chapte 7 Lnea oentu Lnea oentu of a Sngle Patcle Lnea oentu: p υ It s a easue of the patcle s oton It s a vecto, sla to the veloct p υ p υ p υ z z p It also depends on the ass of the object, sla
More informationPermutations that Decompose in Cycles of Length 2 and are Given by Monomials
Poceedgs of The Natoa Cofeece O Udegaduate Reseach (NCUR) 00 The Uvesty of Noth Caoa at Asheve Asheve, Noth Caoa Ap -, 00 Pemutatos that Decompose Cyces of Legth ad ae Gve y Moomas Lous J Cuz Depatmet
More informationMinimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index
Joual of Multdscplay Egeeg Scece ad Techology (JMEST) ISSN: 359-0040 Vol Issue, Febuay - 05 Mmum Hype-Wee Idex of Molecula Gaph ad Some Results o eged Related Idex We Gao School of Ifomato Scece ad Techology,
More informationCouncil for Innovative Research
Geometc-athmetc Idex ad Zageb Idces of Ceta Specal Molecula Gaphs efe X, e Gao School of Tousm ad Geogaphc Sceces, Yua Nomal Uesty Kumg 650500, Cha School of Ifomato Scece ad Techology, Yua Nomal Uesty
More informationSubspace based Model Predictive Control for Linear Parameter Varying Systems
IERAIOA JOURA OF CIRCUIS, SYSEMS AD SIGA PROCESSIG Vome, 6 Sbsace base Moe Pectve Coto o ea Paamete Vag Sstems Xaoso o Abstact A ove moe ectve coto metho base o sbsace etcato o ea aamete vag (PV sstems
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationUniversity of Pavia, Pavia, Italy. North Andover MA 01845, USA
Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *
More informationFractional Integrals Involving Generalized Polynomials And Multivariable Function
IOSR Joual of ateatcs (IOSRJ) ISSN: 78-578 Volue, Issue 5 (Jul-Aug 0), PP 05- wwwosoualsog Factoal Itegals Ivolvg Geealzed Poloals Ad ultvaable Fucto D Neela Pade ad Resa Ka Deatet of ateatcs APS uvest
More informationSolving 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 informationAlternating Direction Implicit Method
Alteratg Drecto Implct Method Whle dealg wth Ellptc Eqatos the Implct form the mber of eqatos to be solved are N M whch are qte large mber. Thogh the coeffcet matrx has may zeros bt t s ot a baded system.
More informationOverview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition
ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationEuropean Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN
Euroe Jour of Mthemtcs d omuter Scece Vo. No. 6 ISSN 59-995 ISSN 59-995 ON AN INVESTIGATION O THE MATRIX O THE SEOND PARTIA DERIVATIVE IN ONE EONOMI DYNAMIS MODE S. I. Hmdov Bu Stte Uverst ABSTRAT The
More informationObjectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method)
Ojectves 7 Statcs 7. Cete of Gavty 7. Equlum of patcles 7.3 Equlum of g oes y Lew Sau oh Leag Outcome (a) efe cete of gavty () state the coto whch the cete of mass s the cete of gavty (c) state the coto
More informationDerivation 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( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.
Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof
More informationSome results and conjectures about recurrence relations for certain sequences of binomial sums.
Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have
More information7.0 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 informationMinimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses
Mmzg sphecal abeatos Explotg the exstece of cojugate pots sphecal leses Let s ecall that whe usg asphecal leses, abeato fee magg occus oly fo a couple of, so called, cojugate pots ( ad the fgue below)
More informationMechanics of Materials CIVL 3322 / MECH 3322
Mechacs of Materals CVL / MECH Cetrods ad Momet of erta Calculatos Cetrods = A = = = A = = Cetrod ad Momet of erta Calculatos z= z A = = Parallel As Theorem f ou kow the momet of erta about a cetrodal
More informationTopology optimization method applied to the design of electromagnetic devices: focus on convexity issues
oolog otzato ethod aled to the desg of electoagetc devces: focus o covet ssues h. Labbé* F. Gleu** B. Dehez*** * F.R.S.-FNRS fellow Uvesté Catholque de Louva/Cete fo Reseach Mechatocs Louva-la-Neuve BELGIUM
More informationChapter #2 EEE State Space Analysis and Controller Design
Chpte EEE8- Chpte # EEE8- Stte Spce Al d Cotolle Deg Itodcto to tte pce Obevblt/Cotollblt Modle ede: D D Go - d.go@cl.c.k /4 Chpte EEE8-. Itodcto Ae tht we hve th ode te: f, ', '',.... Ve dffclt to td
More information7. Queueing and sharing systems. ELEC-C7210 Modeling and analysis of communication networks 1
7. Queueg ad shag systes ELECC7 Modelg ad aalyss of coucato etwoks 7. Queueg ad shag systes Cotets Refeshe: Sle teletaffc odel Queueg dscle M/M/FIFO ( seve, watg laces, custoe laces M/M/PS ( seve, watg
More informationUnique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen
Vol No : Joural of Facult of Egeerg & echolog JFE Pages 9- Uque Coo Fed Pot of Sequeces of Mags -Metrc Sace M. Ara * Noshee * Deartet of Matheatcs C Uverst Lahore Pasta. Eal: ara7@ahoo.co Deartet of Matheatcs
More informationMotion and Flow II. Structure from Motion. Passive Navigation and Structure from Motion. rot
Moto ad Flow II Sce fom Moto Passve Navgato ad Sce fom Moto = + t, w F = zˆ t ( zˆ ( ([ ] =? hesystemmoveswth a gd moto wth aslat oal velocty t = ( U, V, W ad atoalvelocty w = ( α, β, γ. Scee pots R =
More informationFinancial volatility and independent and identically distributed variables
Facal volatlty ad deedet ad detcally dstbuted vaables Abal Fgueedo a, Ia Glea b, Raul Matsushta c, Sego Da Slva d,* a Deatet of Physcs, Uvesty of Basla, 7090-900 Basla DF, Bazl b Deatet of Physcs, Fedeal
More informationPossibilistic Modeling for Loss Distribution and Premium Calculation
Possbstc Modeg fo Loss Dstbuto ad Pemum Cacuato Zhe Huag Ottebe Coege Westeve, OH 4308 Lja Guo Agothmcs, Ftch Goup Newto, MA 02466 ABSTRICT Ths pape uses the possbty dstbuto appoach to estmate the suace
More information_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9
C C A 55NED n R 5 0 9 b c c \ { s AS EC 2 5? 9 Con 0 \ 0265 o + s ^! 4 y!! {! w Y n < R > s s = ~ C c [ + * c n j R c C / e A / = + j ) d /! Y 6 ] s v * ^ / ) v } > { ± n S = S w c s y c C { ~! > R = n
More informationCURVE FITTING ON EMPIRICAL DATA WHEN BOTH VARIABLES ARE LOADED BY ERRORS
TOME VI (ye 8) FASCICULE (ISSN 584 665) CURVE FITTING ON EMPIRICAL ATA WHEN BOTH VARIABLES ARE LOAE BY ERRORS ANRÁS NYĺRI LÁSZLÓ ÖNÖZSY Pofesso emets etmet of Fd d Het Egeeg Uvesty of Msoc H-55 Msoc-Egyetemváos
More informationA Tutorial on Multiple Integrals (for Natural Sciences / Computer Sciences Tripos Part IA Maths)
A Tutoial on Multiple Integals (fo Natual Sciences / Compute Sciences Tipos Pat IA Maths) Coections to D Ian Rud (http://people.ds.cam.ac.uk/ia/contact.html) please. This tutoial gives some bief eamples
More informationChapter 5. Long Waves
ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass
More informationComparison 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 informationSuppose 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 informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationρ < 1 be five real numbers. The
Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace
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 informationCompactness in Multiset Topology
opatess ultset Topolog Sougata ahata S K Saata Depatet of atheats Vsva-haat Satketa-7335 Ida Abstat The pupose of ths pape s to todue the oept of opatess ultset topologal spae e vestgate soe bas esults
More informationMATRIX ANALYSIS OF ANCHORED STRUCTURES
SES It Cof o DMIL SSEMS ad COOL ece Ita oveber - pp-8 M LSIS OF CHOED SES IOS MSOIS Head of the Departet of Coputer Scece Mtar Ist of verst Educato / Heec ava cade era Hatraou 8 Praeus GEECE http://wwwwseasorg/astoras
More informationGeneralized Duality for a Nondifferentiable Control Problem
Aeca Joal of Appled Matheatcs ad Statstcs, 4, Vol., No. 4, 93- Avalable ole at http://pbs.scepb.co/aas//4/3 Scece ad Edcato Pblsh DO:.69/aas--4-3 Geealzed Dalty fo a Nodffeetable Cotol Poble. Hsa,*, Vkas
More informationON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT
V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com
More informationA New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming
ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research
More informationφ (x,y,z) in the direction of a is given by
UNIT-II VECTOR CALCULUS Dectoal devatve The devatve o a pot ucto (scala o vecto) a patcula decto s called ts dectoal devatve alo the decto. The dectoal devatve o a scala pot ucto a ve decto s the ate o
More informationVIII Dynamics of Systems of Particles
VIII Dyacs of Systes of Patcles Cete of ass: Cete of ass Lea oetu of a Syste Agula oetu of a syste Ketc & Potetal Eegy of a Syste oto of Two Iteactg Bodes: The Reduced ass Collsos: o Elastc Collsos R whee:
More informationOn the hydrogen wave function in Momentum-space, Clifford algebra and the Generating function of Gegenbauer polynomial
O he hoge we fco Moe-sce ffo geb he eeg fco of egebe oo Meh Hge Hss To ce hs eso: Meh Hge Hss O he hoge we fco Moe-sce ffo geb he eeg fco of egebe oo 8 HL I: h- hs://hches-oeesf/h- Sbe o J 8 HL s
More informationProbability and Statistics. What is probability? What is statistics?
robablt ad Statstcs What s robablt? What s statstcs? robablt ad Statstcs robablt Formall defed usg a set of aoms Seeks to determe the lkelhood that a gve evet or observato or measuremet wll or has haeed
More informationDiscrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationQuestion 1. Typical Cellular System. Some geometry TELE4353. About cellular system. About cellular system (2)
TELE4353 Moble a atellte Commucato ystems Tutoal 1 (week 3-4 4 Questo 1 ove that fo a hexagoal geomety, the co-chael euse ato s gve by: Q (3 N Whee N + j + j 1/ 1 Typcal Cellula ystem j cells up cells
More informationDUALITY FOR MINIMUM MATRIX NORM PROBLEMS
HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMNIN CDEMY, Seres, OF HE ROMNIN CDEMY Vole 6, Nber /2005,. 000-000 DULIY FOR MINIMUM MRI NORM PROBLEMS Vasle PRED, Crstca FULG Uverst of Bcharest, Faclt of Matheatcs
More informationDISTURBANCE TERMS. is a scalar and x i
DISTURBANCE TERMS I a feld of research desg, we ofte have the qesto abot whether there s a relatoshp betwee a observed varable (sa, ) ad the other observed varables (sa, x ). To aswer the qesto, we ma
More information19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 DETERMINATION OF THE SOUND RADIATION OF TURBULENT FLAMES USING AN INTEGRAL METHOD
9 th NTERNATONAL ONGRE ON AOUT MADRD, -7 EPTEMBER 7 DETERMNATON OF THE OUND RADATON OF TURBULENT FLAME UNG AN NTEGRAL METHOD PA: 43..Rz Pscoya, Rafael; Ochma, Mat Uvesty of Aled ceces; Lembe t., 3353 Bel,
More informationChain Rules for Entropy
Cha Rules for Etroy The etroy of a collecto of radom varables s the sum of codtoal etroes. Theorem: Let be radom varables havg the mass robablty x x.x. The...... The roof s obtaed by reeatg the alcato
More informationChapter 4 Multiple Random Variables
Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:
More informationFun and Fascinating Bible Reference for Kids Ages 8 to 12. starts on page 3! starts on page 163!
F a Faa R K 8 12 a a 3! a a 163! 2013 a P, I. ISN 978-1-62416-216-9. N a a a a a, a,. C a a a a P, a 500 a a aa a. W, : F G: K Fa a Q &, a P, I. U. L aa a a a Fa a Q & a. C a 2 (M) Ta H P M (K) Wa P a
More informationGENOMEWIDE dense marker maps are now available
Coght Ó 009 b the Geetcs Socet of Ameca DOI: 0.534/geetcs.09.050 Pedctg Qattatve Tats Wth Regesso Modes fo Dese Moeca Makes ad Pedgee Gstavo de os Camos* Hgo aa Dae Gaoa* José Cossa Adés Legaa** Edado
More informationMultivariate 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 informationA New Measure of Probabilistic Entropy. and its Properties
Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com
More informationJournal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi SOME PROPERTIES CONCERNING THE HYPERSURFACES OF A WEYL SPACE
Jou of Eee d Ntu Scece Mühed e Fe Be De S 5/4 SOME PROPERTIES CONCERNING THE HYPERSURFACES OF A EYL SPACE N KOFOĞLU M S Güze St Üete, Fe-Edeyt Füte, Mtet Böüü, Beştş-İSTANBUL Geş/Receed:..4 Ku/Accepted:
More informationTHE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp
THE PUBLISHIN HOUSE PROCEEDINS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/8, THE UNITS IN Stela Corelu ANDRONESCU Uversty of Pteşt, Deartmet of Mathematcs, Târgu Vale
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More information