Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling

Size: px
Start display at page:

Download "Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling"

Transcription

1 Oen Journal of ascs Publshed Onlne Auus 04 n ces. h:// h://d.do.or/0.436/ojs Mure eresson Esaors Usn Mul-Aular Varables and Arbues n Two-Phase aln John Kun u Grace Chuba Leo Odono Dearen of Maheacs Kenaa Unvers Narob Kena Eal: johnunu08@ahoo.co chubarace@al.co eceved 3 Ma 04; revsed 6 June 04; acceed 0 Jul 04 Corh 04 b auhors and cenfc esearch Publshn Inc. Ths wor s lcensed under he Creave Coons Arbuon Inernaonal Lcense CC BY. h://creavecoons.or/lcenses/b/4.0/ Absrac In hs aer we have develoed esaors of fne oulaon ean usn Mure eresson esaors usn ul-aular varables and arbues n wo-hase saln and nvesaed s fne sale roeres n full aral and no nforaon cases. An ercal sud usn naural daa s ven o coare he erforance of he roosed esaors wh he esn esaors ha ulzes eher aular varables or arbues or boh for fne oulaon ean. The Mure eresson esaors n full nforaon case usn ulle aular varables and arbues are ore effcen han ean er un eresson esaor usn one aular varable or arbue eresson esaor usn ulle aular varable or arbues and Mure eresson esaors n boh aral and no nforaon case n wo-hase saln. A Mure eresson esaor n aral nforaon case s ore effcen han Mure eresson esaors n no nforaon case. Kewords eresson Esaor Mulle Aular Varables Mulle Aular Arbues Two-Phase aln B-eral Correlaon Coeffcen. Inroducon The hsor of usn aular nforaon n surve saln s as old as he hsor of surve saln. The wor of Nean [] a be referred o as he nal wor where aular nforaon has been used o esae oulaon araeers. Hansen and Hurwz [] also suesed he use of aular nforaon n selecn he sale wh varn robables. The conce of rao esaon was nroduced n sale surve b Cochran How o ce hs aer: Kun u J. Chuba G. and Odono L. 04 Mure eresson Esaors Usn Mul-Aular Varables and Arbues n Two-Phase aln. Oen Journal of ascs h://d.do.or/0.436/ojs

2 [3]; s referred when he sud varable s hhl osvel correlaed wh he aular varable. Wason [4] used he reresson esaor of leaf area on leaf weh o esae he averae area of he leaves on a lan. Oln [5] was he frs erson usn nforaon on ore han one suleenar characer whch s osvel correlaed wh he varable under sud usn a lnear cobnaon of rao esaor based on each aular varable. aj [6] suesed a ehod of usn ul-aular nforaon n sale surve. The conce of double saln was frs roosed b Nean [] n saln huan oulaons when he ean of aular varable was unnown. I was laer eended o ulhase b obson [7]. Abdul Zahoor and Hanf [8] also develoed a eneralzed ulvarae reresson esaor for ul-hase saln usn ulaular varables. Zahoor Abdul and Muhhaad [9] suesed a eneralzed reresson-cu-rao esaor for wo-hase saln usn ulle aular varables. I s advanaeous when he an n recson s subsanal as coared o he ncrease n he cos due o collecon of nforaon on he aular varae for lare sales. I was roved ha ou esaor n he roosed class of esaors was aroael euall effcen wh he usual based lnear reresson esaor. auddn and Hanf [0] nroduced rao and reresson esaon rocedures for esan oulaon ean n wo-hase saln for dfferen hree suaons deendn uon he avalabl of nforaon on wo aular varables for oulaon. The consdered hree suaons frs when nforaon on boh aular varables was no avalable second when nforaon on one aular varable was avalable and hrd when nforaon was avalable on boh aular varables. Jhajj hara and Grover [] roosed a fal of esaors usn nforaon on aular arbue. The used nown nforaon of oulaon rooron ossessn an arbue hhl correlaed wh sud varable Y. The ou esae of he roosed fal of ean was less based and ore effcen han ean er un esaor. The arbue s norall used when he aular varable s no avalable e.. an aoun of l roduced and a arcular breed of cow or an aoun of eld of whea and a arcular vare of whea. The esaor erfored beer han he usual sale ean and Na and Gua [] esaor. ajesh Panaj Nrala and Florenns [3] used he aular arbue n reresson-rao e eonenal esaor follown he wor of Bahl and Tueja [4]; he esaor was ore effcen coared o ean er un rao and roduc e eonenal esaor as well as Na and Gua [] esaor. Hanf Ha and hahbaz [5] roosed a eneral fal of esaors usn ulle aular arbue n snle and double hase saln. The esaor had a saller coared o ha of Jhajj hara and Grover []. The also eended her wor o rao and reresson esaor whch was eneralzaon of Na and Gua [] esaor n snle and double hase saln wh full nforaon aral nforaon and no nforaon. Moeen hahbaz and HanIf [6] roosed a class of ure rao and reresson esaors for snle hase saln for esan oulaon ean b usn nforaon on aular varables and arbues sulaneousl. Kun u and Odono [7] and [8] roosed rao-cu-roduc esaors usn ulle aular arbues n snle and wo-hase saln. In our aer we wll eend he ure reresson esaor roosed b Moeen hahbaz and HanIf [6] o wo-hase saln under full aral and no nforaon case sraees nroduced b auddn and Hanf [0] and also ncororae Arora and Bans [9] aroach n wrn down he ean suared error.. Prelnares.. Noaon and Assuon Consder a oulaon of N uns. Le Y be he varable for whch we wan o esae he oulaon ean and X X X are aular varables. For wo-hase saln desn le n and n n < n are sale szes for frs and second hase resecvel. and denoe he h aular varables for frs and second hase sales resecvel and denoe he varable of neres fro second hase. X and C denoe he oulaon eans and coeffcen of varaon of h aular varables resecvel and denoes he oulaon correlaon coeffcen of Y and X. Furher le θ θ θ < θ n N n N Y + e X + e X + e and.0 356

3 where e e and e are saln error and are ver sall. We assue ha E e E e E e 0. Consder a sale of sze n drawn b sle rando saln whou relaceen fro a oulaon of sze h N. Le j and denoes he observaons on varable and r resecvel for he j un where j n. In defnn he arbues we assue colee dchoo so ha; Le N A and a j j h h f j un of oulaon ossess aular arbue j 0 oherwse n j j. be he oal nuber of uns n he oulaon and sale resecvel os- A a sessn arbue. Le P and be he corresondn rooron of uns ossessn a secfc N n arbues and s he ean of he an varable a second hase. Le and denoe he h aular arbue for frs and second hase sales resecvel and denoe he varable of neres fro second hase. The ean of an varable of neres a second hase wll be denoed b. Also le us defne e Y e P e P.3 The coeffcen of varaon and correlaon coeffcen are ven b C C Y C Y P z z z Pb and z Then for sle rando saln whou relaceen for boh frs and second hases we wre b usn hase wse oeraon of eecaons as: θ θ θ θ θ θ θ Pb θ θ j θ θ e θ θ PC ; ; θ j j j θ j j j θ θ j ; j θ θ j j j j j θ j θ j j ; j j ; j j j ; θ θ j j j E e Y C E e e P C E e e X C E e e YX C C E e e YPC C E e e e YX C C E e e e X C E e e E e e X X C C j E e e PPC C j E e e e YX C C E e e e e PPC C j E e e e e XXC C E e e e j X X C C j E e e e PPC C j z z.4 T Aj d A A C.5 A j A Arora and La [9].6 357

4 The follown noaons wll be used n dervn he ean suare errors of roosed esaors Deernan of oulaon correlaon ar of varables and. r Deernan of h nor of corresondn o he h eleen of. Denoes he ulle coeffcen of deernaon of on r and r. r r Denoes he ulle coeffcen of deernaon of on and. Deernan of oulaon correlaon ar of varables r and r. Deernan of oulaon correlaon ar of varables and. Deernan of he correlaon ar of r and r. Deernan of he correlaon ar of and. j Deernan of he nor corresondn o of he correlaon ar of r j j r and r j. j Deernan of he nor corresondn o of he correlaon ar of j and.7.. Mean er Un n Two-Phase aln j j The sale ean usn sle rando saln whou relaceen n wo hase saln s ven b s ven b Whle s varance s ven Var n n j.0 θ Y C..3. eresson Esaors Usn One and Mulle Aular Varables and Arbues Le n j n j n j n j and be he unbased esaor of sale eans of Y and X resecvel n wo hase saln. The sle reresson esaor for nown X suesed b Wason [4] s Is ean suared error s ven b EX β + X. EX θy C In case of ulle aular varables reresson esaor s ven b Is ean suared error s ven b.3 MEX + α X.4 MEX θy C Na and Gua [] defned eresson esaor of oulaon when he ror nforaon of oulaon rooron of uns ossessn he sae arbue s varable as.5 358

5 Is ean suared error s ven b EP α + P.6 EP θy C.7 YC b α are ou for eresson esaor. C P r In case of ulle aular varables reresson esaor s ven b Is ean suared error s ven b r Pb s he b-seral correlaon coeffcen. MEP + α r P.8 MEX θy C The ure rao esaor based on ulle aular varables and arbues b Moeen hahbaz and HanIf [6] s ven b: EXP.9 α β X + P + I s norall nown ha he above esaors are based bu he bas ben of he order n can be assued nelble n lare sales. I s assued ha he sale of sze n s lare enouh so ha he bases of hese esaors are nelble. Our rojec wll eend he ure reresson esaor roosed b Moeen hahbaz and Hanf [6] o wohase saln under full aral and no nforaon case sraees nroduced b auddn and Hanf [0]. 3. Mehodolo 3.. Proosed Mure eresson Esaor n Two-Phase aln Full Inforaon Case If we esae a sud varable when nforaon on all aular varables and arbues s avalable fro oulaon s ulzed n he for of her eans. B an he advanae of Mure eresson esaor echnue for wo-hase saln a eneralzed esaor for esan oulaon ean of sud varable Y wh he use of ul aular varables and arbues s suesed as: P P P α X + α X + + α X M 3.0 ubsun Euaon.0 and.3 n 3.0 we e The ean suared error of M 3.0 s ven b + β + β + + β e + Y α e β e M 3.0 r + M 3.0 M h+ α β E Y E e e e We dfferenae he Euaon 3.3 arall wh resec o α and β + + hen euae o zero usn and.7 we e + YC α 3.4 XC 359

6 + YC β PC h Usn noral euaon ha s used o fnd he ou values ven 3.3 we can wre 3.3 as h 3.5 h+ M 3.0 E e e α e β e + + h M 3.0 α β E e E e e E e e Tan eecaon n 3.7 and subsun.4 we e h YC + YC h M 3.0 θ Y C + X YCC + PYC C Pb XC PC I h M 3.0 θ Y C h h Pb + h M 3.0 θy C θy C Usn.6 n 3.3 we e θy C M M 3.0 θy C θ M 3.0 Y C. 3.. Mure eresson Esaor n Two-Phase aln Paral Inforaon Case In hs case suose we have no nforaon on all aular varables and h aular arbues fro oulaon. Consdern Mure eresson esaor echnue he oulaon ean of sud varable Y can be es- 360

7 aed for wo-hase saln usn ul-aular varables and arbues as: δ X α α α P P P P β + P β P + α + α + + α + δ X + δ X M β + β + + β γ γ + + γ + β ubsun.0 and.3 n 3.5 we e α δ α β + + e + Y + e e e + e e + e e γe + β e e Mean suared error of M 3. esaor s ven b e e e e e E M 3. Ee + α e e δe + α e e + + β γ r + β We dfferenae he Euaon 3.4 wh resec o α r β r α + + γ + + λ + + r r h h euae o zero and use.6 and.7. The ou value s as follows 3.7 γ h+ h+ and YC + + YC s α δ XC XC s r r YC YC + + s z β α XC PC s z h C γ Y YC + β + PC PC h h + + h h+ h+ Usn noral euaon ha are used o fnd he ou values ven 3.7 we can wre e e e e e M 3. EEe e + α e e δe + α e e + + β γ r + β

8 E e M 3. + α EE e e e δe e e + δee e e e Usn.4 n 3.8 we e + α E E e e e γ E e e + δ E E e e e θ Y C M 3. + θ θ α XYCC θ δ XCC + θ θ XCC α + + θ θ β PYCC θ γ PCC θ θ β PCC Pb Pb Pb Y C + + θ θ θ + θ j j θθ + θθ + + θ + j j + θ θ Pb γ Pb + h+ + Y C θ θ θ θ θ θ + θ θ + θ θ θ θ + j + + Pb Pb j j j + + θ Pb + θθ γ Pb + + M 3. j Y C θ θ + + Pb r+ + + j r + + θ + Pb + Pb

9 j θ θ θ Y C Usn.6 n 3.7 we e.. Y C M 3. θ θ + θ M 3. θ. + θ.. Y C Mure eresson Esaor n Two-Phase aln No Inforaon Case If we esae a sud varable when nforaon on all aular varables s unavalable fro oulaon s ulzed n he for of her eans. B an he advanae of Mure eresson esaor echnue for wohase saln a eneralzed esaor for esan oulaon ean of sud varable Y wh he use of ul aular varables and arbues s suesed as: α α α M 3. + β + β + + β + + ubsun euaon.0 and. n 3.9 we e β e + Y + α e e + e e M 3. + The ean suared error of P 3. s ven b M 3. M α h+ β 3.3 E Y E e + e e + e e + We dfferenae he Euaon 3.3 arall wh resec o α and β + + hen euae o zero usn and.7 we e + YC P α 3.3 XC + YC β PC h Usn noral euaon ha s used o fnd he ou values ven 3.3 we can wre h+ M 3. α + h β E e e + e e + e e h MP 3. α β + E e + E e e e E e e e + Tan eecaon and subsun 3.3 and 3.33 and we e

10 YC M 3. θ Y C + θ θ X YCC XC + h YC h + θ θ PYC C PC + h + h h M 3. Y C θ + θ θ + θ θ Pb + h Usn.6 n 3.38 we e lfn 3.38 we e. Y C M 3. θ θ + θ Y C M 3. θ θ. + θ M 3. θ. + θ. Y C Bas and Conssenc of Mure eresson Esaors These ure reresson esaors usn ulle aular varables n wo hase saln are based. However hese bases are nelble for oderae and lare sales. I s easl shown ha he ure reresson esaors are conssen esaors usn ulle aular varables snce he are lnear cobnaons of conssen esaors follows ha he are also conssen. 4. esul and Dscusson In hs secon we carred ou soe daa analss usn sascal acae o coare he erforance of ure reresson esaors wh alread esn esaor n wo-hase saln for fne oulaon ha uses one or ulle aular varables or arbues. In he naural oulaon he sud varable was bod fa and aular varables are Thh crcuference and ches crcuference whle arbues were abdoen and h crcuference. N 5 n 80 n Pb Pb Poulaon: The sulaed oulaon was a norall dsrbued wh he follown araeers N 600 n 90 n 36 ean 75 sandard devaon Pb Pb All he resuls were obaned afer carrn ou several rando sale and an he averae. In order o evaluae he effcenc an we could acheve b usn he roosed esaors we have calculaed he varance of ean er un and he ean suared error of all esaors we have consdered. We have hen calculaed ercen relave effcenc of each esaor n relaon o varance of ean er un. We have hen coared he ercen relave effcenc of each esaor he esaor wh he hhes ercen relave effcenc s consdered o be he os effcen han he oher esaor. The ercen relave effcenc s calculaed usn he follown forulae. 364

11 Y ˆ ˆ Y ˆ J. Kun u e al. Var eff The Table shows ercen relave effcenc of roosed and esn esaor wh resec o ean er un esaor for wo hase saln. I s observed ha eresson esaors usn one aular varables and arbues are ore effcen han ean er un n he wo oulaons. Aan eresson esaors usn ulle aular varables and arbues are ore effcen han ean er un and eresson esaors. Fnall Mure eresson esaors usn ulle aular varables and arbues s he os effcen of he fve esaors n he wo oulaons snce has he hhes ercen relave effcenc. Fnall Table coares he effcenc of full nforaon case and aral case o no nforaon case and full o aral nforaon case. I s observed ha he full nforaon case and aral nforaon case are ore effcen han no nforaon case because he have hher ercen relave effcenc han no nforaon case. In addon he full nforaon case s ore effcen han he aral nforaon case because has a hher ercen relave effcenc han aral nforaon case. 5. Conclusons The ercen relave effcenc s used n sale surve o coare he effcenc of dfferen esaors. The esaor wh he hhes ercen relave effcenc wh resec o ean er un s norall consdered o be ore effcen coared o he oher esaors. Accordn o Table he roosed Mure eresson esaors usn ulle aular varables and arbues n wo-hase saln has he hhes ercen relave effcenc coared o ean er un eresson esaors usn one aular varable and arbues eresson esaors usn ulle aular varables and arbues. Ths eans ha he rao-cu-roduc esaor n wo-hase saln s he os effcen esaor coared o he esaors ha ulze aular varables and arbues. The Mure eresson esaors were hen eended o wo-hase saln n aral and no nforaon case. In Table we coared he effcenc of full and aral nforaon case o no nforaon case and Table. elave effcenc of suesed esaor wh resec o ean er un esaor for wo hase saln. Esaors elave effcenc of suesed esaor wh resec o ean er un esaor for wo hase saln Poulaon I Poulaon II EX EP 7 30 MEX MEP roosed M Table. Coarsons of full aral and no nforaon cases for roosed ure reresson esaor. Poulaon Percen relave effcenc of full and aral o no nforaon Percen relave effcenc of full o aral n foraon case Esaors M 3. M 3. M 3.0 M 3. M

12 found ha he wo are ore effcen han he no nforaon case. We also coared he effcenc of full nforaon case o aral nforaon case and found ha he full nforaon case s ore effcen han he aral nforaon case. The roosed Mure eresson esaor usn ulle aular varables and arbues n wo-hase saln s recoended o esae he fne oulaons ean for full nforaon case as ouerfors all he oher esn esaors for full nforaon usn one aular or ulle aular varables and arbues. I also ouerfors Mure eresson esaors usn ulle aular varables and arbues n aral and no nforaon cases. When soe aular varables are unnown he wo-hase saln s recoended. If soe aular varables are nown he Mure eresson esaors usn ulle aular varables and arbues n aral nforaon case should be used bu f all he aular varables and arbues are unnown. Mure eresson esaors usn ulle aular varables n no nforaon case should be used o esae he fne oulaon ean. eferences [] Nean J. 938 Conrbuon o he Theor of aln Huan Poulaons. Journal of he Aercan ascal Assocaon h://d.do.or/0.080/ [] Hansen M.H. and Hurwz W.N. 943 On he Theor of aln fro Fne Poulaons. Annals of Maheacal ascs h://d.do.or/0.4/aos/ [3] Cochran W.G. 940 The Esaon of he Yelds of he Cereal Eerens b aln for he ao of Gran o Toal Produce. Journal of Arculural cence h://d.do.or/0.07/ [4] Wason D.J. 937 The Esaon of Leaf Areas. Journal of Arculural cence h://d.do.or/0.07/ x [5] Oln I. 958 Mulvarae ao Esaon for Fne Poulaon. Boera h://d.do.or/0.093/boe/ [6] aj D. 965 On a Mehod of Usn Mul-Aular Inforaon n ale urves. Journals of he Aercan ascal Assocaon h://d.do.or/0.080/ [7] obson D.. 95 Mulle aln of Arbues. Journal of he Aercan ascal Assocaon h://d.do.or/0.080/ [8] Zahoor A. Muhhaad H. and Munr A. 009 Generalzed Mulvarae ao Esaor Usn Mulle Aular Varables for Mul-Phase aln. Pasan Journal of asc [9] Zahoor A. Muhhaad H. and Munr A. 009 Generalzed eresson-cu-ao Esaors for Two Phase aln Usn Mulle Aular Varables. Pasan Journal of ascs [0] uddn M. and Hanf M. 007 Esaon of Poulaon Mean n nle and Two Phase aln wh or whou Addonal Inforaon. Pasan Journal of ascs [] Jhajj H.. hara M.K. and Grover L.K. 006 A Fal of Esaor of Poulaon Mean Usn Inforaon on Aular Arbues. Pasan Journal of ascs [] Na V.D. and Gua P.C. 996 A Noe on Esaon of Mean wh Known Poulaon of Aular Characer. Journal of he Indan oce of Arculural ascs [3] ajesh. Panaj C. Nrala. and Florenns. 007 ao-produc Te Eonenal Esaor for Esan Fne Poulaon Mean Usn Inforaon on Aular Arbues. enassance Hh Press UA. [4] Bahl. and Tueja.K. 99 ao and Produc Te Esaor. Inforaon and Ozaon cence h://d.do.or/0.080/ [5] Hanf M. Ha I.U. and hahbaz M.Q. 009 On a New Fal of Esaor Usn Mulle Aular Arbues. World Aled cence Journal [6] Moeen M. hahbaz Q. and HanIf M. 0 Mure ao and eresson Esaors Usn Mul-Aular Varable and Arbues n nle Phase aln. World Aled cences Journal [7] Kun u J. and Odono L. 04 ao-cu-produc Esaor Usn Mulle Aular Arbues n nle Phase aln. Oen Journal of ascs h://d.do.or/0.436/ojs [8] Kun u J. and Odono L. 04 ao-cu-produc Esaor Usn Mulle Aular Arbues n Two-Phase aln. Oen Journal of ascs h://d.do.or/0.436/ojs [9] Arora. and Bans Lal. 989 New Maheacal ascs. aa Praashan New Delh. 366

13

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda Pankaj Chauhan, Nrmala awan chool of ascs, DAVV, Indore (M.P.), Inda Florenn marandache Deparmen of Mahemacs, Unvers of New Meco, Gallup, UA

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling Improvemen n Esmang Populaon Mean usng Two Auxlar Varables n Two-Phase amplng Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda (rsnghsa@ahoo.com) Pankaj Chauhan and Nrmala awan chool of ascs,

More information

Advanced time-series analysis (University of Lund, Economic History Department)

Advanced time-series analysis (University of Lund, Economic History Department) Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information

Endogeneity. Is the term given to the situation when one or more of the regressors in the model are correlated with the error term such that

Endogeneity. Is the term given to the situation when one or more of the regressors in the model are correlated with the error term such that s row Endogeney Is he erm gven o he suaon when one or more of he regressors n he model are correlaed wh he error erm such ha E( u 0 The 3 man causes of endogeney are: Measuremen error n he rgh hand sde

More information

Response of MDOF systems

Response of MDOF systems Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss

More information

PHYS 705: Classical Mechanics. Canonical Transformation

PHYS 705: Classical Mechanics. Canonical Transformation PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon

More information

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN Ener-Effcen Tare Coverae n Wreless Sensor Newors Presened b M Trà Tá -4-4 Inroducon Bacround Relaed Wor Our Proosal Oulne Maxmum Se Covers (MSC) Problem MSC Problem s NP-Comlee MSC Heursc Concluson Sensor

More information

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol

More information

INTRODUCTION TO MACHINE LEARNING 3RD EDITION

INTRODUCTION TO MACHINE LEARNING 3RD EDITION ETHEM ALPAYDIN The MIT Press, 2014 Lecure Sdes for INTRODUCTION TO MACHINE LEARNING 3RD EDITION aaydn@boun.edu.r h://www.ce.boun.edu.r/~ehe/23e CHAPTER 7: CLUSTERING Searaerc Densy Esaon 3 Paraerc: Assue

More information

Panel Data Regression Models

Panel Data Regression Models Panel Daa Regresson Models Wha s Panel Daa? () Mulple dmensoned Dmensons, e.g., cross-secon and me node-o-node (c) Pongsa Pornchawseskul, Faculy of Economcs, Chulalongkorn Unversy (c) Pongsa Pornchawseskul,

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015 /4/ Learnng Objecves Self Organzaon Map Learnng whou Exaples. Inroducon. MAXNET 3. Cluserng 4. Feaure Map. Self-organzng Feaure Map 6. Concluson 38 Inroducon. Learnng whou exaples. Daa are npu o he syse

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study) Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

On Local Existence and Blow-Up of Solutions for Nonlinear Wave Equations of Higher-Order Kirchhoff Type with Strong Dissipation

On Local Existence and Blow-Up of Solutions for Nonlinear Wave Equations of Higher-Order Kirchhoff Type with Strong Dissipation Inernaonal Journal of Modern Nonlnear Theory and Alcaon 7 6-5 h://wwwscrorg/journal/jna ISSN Onlne: 67-987 ISSN Prn: 67-979 On Local Exsence and Blow-U of Soluons for Nonlnear Wave Euaons of Hgher-Order

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

EP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES

EP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES EP Queung heory and eleraffc sysems 3rd lecure Marov chans Brh-deah rocess - Posson rocess Vora Fodor KTH EES Oulne for oday Marov rocesses Connuous-me Marov-chans Grah and marx reresenaon Transen and

More information

January Examinations 2012

January Examinations 2012 Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons

More information

グラフィカルモデルによる推論 確率伝搬法 (2) Kenji Fukumizu The Institute of Statistical Mathematics 計算推論科学概論 II (2010 年度, 後期 )

グラフィカルモデルによる推論 確率伝搬法 (2) Kenji Fukumizu The Institute of Statistical Mathematics 計算推論科学概論 II (2010 年度, 後期 ) グラフィカルモデルによる推論 確率伝搬法 Kenj Fukuzu he Insue of Sascal Maheacs 計算推論科学概論 II 年度 後期 Inference on Hdden Markov Model Inference on Hdden Markov Model Revew: HMM odel : hdden sae fne Inference Coue... for any Naïve

More information

MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM

MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM Yugoslav Journal of Oeraons Research (0), Nuer, 307-34 DOI: 0.98/YJOR0307I MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM Olver ILIĆ, Mlć RADOVIĆ Faculy of Organzaonal

More information

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

GMM parameter estimation. Xiaoye Lu CMPS290c Final Project

GMM parameter estimation. Xiaoye Lu CMPS290c Final Project GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π

More information

A New Generalisation of Sam-Solai s Multivariate symmetric Arcsine Distribution of Kind-1*

A New Generalisation of Sam-Solai s Multivariate symmetric Arcsine Distribution of Kind-1* IOSR Journal o Mahemacs IOSRJM ISSN: 78-578 Volume, Issue May-June 0, PP 4-48 www.osrournals.org A New Generalsaon o Sam-Sola s Mulvarae symmerc Arcsne Dsrbuon o Knd-* Dr. G.S. Davd Sam Jayaumar. Dr.A.Solarau.

More information

A New Generalized Gronwall-Bellman Type Inequality

A New Generalized Gronwall-Bellman Type Inequality 22 Inernaonal Conference on Image, Vson and Comung (ICIVC 22) IPCSIT vol. 5 (22) (22) IACSIT Press, Sngaore DOI:.7763/IPCSIT.22.V5.46 A New Generalzed Gronwall-Bellman Tye Ineualy Qnghua Feng School of

More information

A note on diagonalization of integral quadratic forms modulo p m

A note on diagonalization of integral quadratic forms modulo p m NNTDM 7 ( 3-36 A noe on diagonalizaion of inegral quadraic fors odulo Ali H Hakai Dearen of Maheaics King Khalid Universiy POo 94 Abha Posal Code: 643 Saudi Arabia E-ail: aalhakai@kkuedusa Absrac: Le be

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

A COMPARISON OF ADJUSTED BAYES ESTIMATORS OF AN ENSEMBLE OF SMALL AREA PARAMETERS

A COMPARISON OF ADJUSTED BAYES ESTIMATORS OF AN ENSEMBLE OF SMALL AREA PARAMETERS STATISTICA, anno LXIX, n. 4, 009 A COMPARISON OF ADJUSTED BAYES ESTIMATORS OF AN ENSEMBLE OF SMALL AREA PARAMETERS Enrco Fabrz 1. INTRODUCTION In recen years saple surveys have been characerzed by a growng

More information

Department of Economics University of Toronto

Department of Economics University of Toronto Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of

More information

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6) Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen

More information

Physics 3 (PHYF144) Chap 3: The Kinetic Theory of Gases - 1

Physics 3 (PHYF144) Chap 3: The Kinetic Theory of Gases - 1 Physcs (PYF44) ha : he nec heory of Gases -. Molecular Moel of an Ieal Gas he goal of he olecular oel of an eal gas s o unersan he acroscoc roeres (such as ressure an eeraure ) of gas n e of s croscoc

More information

Sensor Scheduling for Multiple Parameters Estimation Under Energy Constraint

Sensor Scheduling for Multiple Parameters Estimation Under Energy Constraint Sensor Scheduln for Mulple Parameers Esmaon Under Enery Consran Y Wan, Mnyan Lu and Demoshens Tenekezs Deparmen of Elecrcal Enneern and Compuer Scence Unversy of Mchan, Ann Arbor, MI {yws,mnyan,eneke}@eecs.umch.edu

More information

Two-Step versus Simultaneous Estimation of Survey-Non-Sampling Error and True Value Components of Small Area Sample Estimators

Two-Step versus Simultaneous Estimation of Survey-Non-Sampling Error and True Value Components of Small Area Sample Estimators Two-Sep versus Sulaneous Esaon of Survey-Non-Saplng Error and True Value Coponens of Sall rea Saple Esaors a PVB Sway, TS Zeran b c and JS Meha a,b Bureau of Labor Sascs, Roo 4985, Massachuses venue, NE,

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Lecture 12: HEMT AC Properties

Lecture 12: HEMT AC Properties Lecure : HEMT A Proeres Quas-sac oeraon Transcaacances -araeers Non-quas ac effecs Parasc ressances / caacancs f f ax ean ue for aer 6: 7-86 95-407 {407-46 sk MEFET ars} 47-44. (.e. sk an MEFET ars brefl

More information

Advanced Machine Learning & Perception

Advanced Machine Learning & Perception Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel

More information

EE241 - Spring 2003 Advanced Digital Integrated Circuits

EE241 - Spring 2003 Advanced Digital Integrated Circuits EE4 EE4 - rn 00 Advanced Dal Ineraed rcus Lecure 9 arry-lookahead Adders B. Nkolc, J. Rabaey arry-lookahead Adders Adder rees» Radx of a ree» Mnmum deh rees» arse rees Loc manulaons» onvenonal vs. Ln»

More information

Viscous Damping Summary Sheet No Damping Case: Damped behaviour depends on the relative size of ω o and b/2m 3 Cases: 1.

Viscous Damping Summary Sheet No Damping Case: Damped behaviour depends on the relative size of ω o and b/2m 3 Cases: 1. Viscous Daping: && + & + ω Viscous Daping Suary Shee No Daping Case: & + ω solve A ( ω + α ) Daped ehaviour depends on he relaive size of ω o and / 3 Cases:. Criical Daping Wee 5 Lecure solve sae BC s

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

Testing a new idea to solve the P = NP problem with mathematical induction

Testing a new idea to solve the P = NP problem with mathematical induction Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he

More information

Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Two-Phase Sampling

Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Two-Phase Sampling On Jounal of Sascs, 04, 4, 46-57 Publshd Onln Jun 04 n Scs. h://www.sc.o/ounal/os h://dx.do.o/0.436/os.04.4404 ao-um-poduc Esmao Usn Mull Auxla Abus n Two-Phas Samln John Kun u, Lo Odono Damn of Mahmacs,

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient 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 information

Tight results for Next Fit and Worst Fit with resource augmentation

Tight results for Next Fit and Worst Fit with resource augmentation Tgh resuls for Nex F and Wors F wh resource augmenaon Joan Boyar Leah Epsen Asaf Levn Asrac I s well known ha he wo smple algorhms for he classc n packng prolem, NF and WF oh have an approxmaon rao of

More information

Almost Unbiased Estimator for Estimating Population Mean Using Known Value of Some Population Parameter(s)

Almost Unbiased Estimator for Estimating Population Mean Using Known Value of Some Population Parameter(s) Almos Unbiased Esimaor for Esimaing Populaion Mean Using Known Value of Some Populaion Parameers Rajesh Singh Deparmen of Saisics, Banaras Hindu Universi U.P., India rsinghsa@ahoo.com Mukesh Kumar Deparmen

More information

2 Position-Binary Technology of Monitoring Defect at Its Origin

2 Position-Binary Technology of Monitoring Defect at Its Origin Poson-Bnary echnoloy of Monorn Defec a Is Orn Specfc Properes of Perodc Effec Objecs I s nown ha n os cases he specral ehods are used for he experenal analyss of he cyclcal (perodc) processes [ 4] For

More information

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling Opn Journal of Sascs 04 4 776-788 Publshd Onln Ocobr 04 n Scs hp://scrporg/ournal/os hp://ddoorg/0436/os0449073 Mur ao Esmaors Usng Mul-Aular Varabls and Arbus for To-Phas Samplng Paul Mang Waru John Kung

More information

CHAPTER 5: MULTIVARIATE METHODS

CHAPTER 5: MULTIVARIATE METHODS CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he

More information

ALMOST UNBIASED RATIO AND PRODUCT TYPE EXPONENTIAL ESTIMATORS

ALMOST UNBIASED RATIO AND PRODUCT TYPE EXPONENTIAL ESTIMATORS STATISTIS IN TANSITION-new series, December 0 537 STATISTIS IN TANSITION-new series, December 0 Vol. 3, No. 3, pp. 537 550 ALMOST UNBIASED ATIO AND ODUT TYE EXONENTIAL ESTIMATOS ohini Yadav, Lakshmi N.

More information

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia Malaysan Journal of Mahemacal Scences 9(2): 277-300 (2015) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal homeage: h://ensemumedumy/journal A Mehod for Deermnng -Adc Orders of Facorals 1* Rafka Zulkal,

More information

Exponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute

Exponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 193-9466 Vol. 10, Issue 1 (June 015), pp. 106-113 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) Exponental Tpe Product Estmator

More information

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE S13 A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE by Hossen JAFARI a,b, Haleh TAJADODI c, and Sarah Jane JOHNSTON a a Deparen of Maheacal Scences, Unversy

More information

Chapter 8 Dynamic Models

Chapter 8 Dynamic Models Chaper 8 Dnamc odels 8. Inroducon 8. Seral correlaon models 8.3 Cross-seconal correlaons and me-seres crosssecon models 8.4 me-varng coeffcens 8.5 Kalman fler approach 8. Inroducon When s mporan o consder

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

The Single Particle Path Integral and Its Calculations. Lai Zhong Yuan

The Single Particle Path Integral and Its Calculations. Lai Zhong Yuan Te Sngle Parcle Pa Inegral and Is Calculaons La Zong Yuan Suary O Conens Inroducon and Movaon Soe Eaples n Calculang Pa Inegrals Te Free Parcle Te Haronc Oscllaor Perurbaon Epansons Inroducon and Movaon

More information

Robust and Accurate Cancer Classification with Gene Expression Profiling

Robust and Accurate Cancer Classification with Gene Expression Profiling Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem

More information

Keywords: Hedonic regressions; hedonic indexes; consumer price indexes; superlative indexes.

Keywords: Hedonic regressions; hedonic indexes; consumer price indexes; superlative indexes. Hedonc Imuaon versus Tme Dummy Hedonc Indexes Erwn Dewer, Saeed Herav and Mck Slver December 5, 27 (wh a commenary by Jan de Haan) Dscusson Paer 7-7, Dearmen of Economcs, Unversy of Brsh Columba, 997-873

More information

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10)

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10) Publc Affars 974 Menze D. Chnn Fall 2009 Socal Scences 7418 Unversy of Wsconsn-Madson Problem Se 2 Answers Due n lecure on Thursday, November 12. " Box n" your answers o he algebrac quesons. 1. Consder

More information

The Comparison of Spline Estimators in the Smoothing Spline Nonparametric Regression Model Based on Weighted...

The Comparison of Spline Estimators in the Smoothing Spline Nonparametric Regression Model Based on Weighted... he Coparson o Splne Esaors n he Soohng Splne Nonparaerc Regresson Model Based on Weghed... he Coparson o Splne Esaors n he Soohng Splne Nonparaerc Regresson Model Based on Weghed Leas Square (WLS and Penalzed

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field Submed o: Suden Essay Awards n Magnecs Bernoull process wh 8 ky perodcy s deeced n he R-N reversals of he earh s magnec feld Jozsef Gara Deparmen of Earh Scences Florda Inernaonal Unversy Unversy Park,

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions Journal of Scences Islac Reublc of Iran 9(): 67-78 (08) Unversy of Tehran ISSN 06-04 h://jscencesuacr Dagnosc Measures n Rdge Regresson Model wh AR() Errors under he Sochasc Lnear Resrcons A Zaherzadeh

More information

[Link to MIT-Lab 6P.1 goes here.] After completing the lab, fill in the following blanks: Numerical. Simulation s Calculations

[Link to MIT-Lab 6P.1 goes here.] After completing the lab, fill in the following blanks: Numerical. Simulation s Calculations Chaper 6: Ordnary Leas Squares Esmaon Procedure he Properes Chaper 6 Oulne Cln s Assgnmen: Assess he Effec of Sudyng on Quz Scores Revew o Regresson Model o Ordnary Leas Squares () Esmaon Procedure o he

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Speech recognition in noise by using word graph combinations

Speech recognition in noise by using word graph combinations Proceedngs of 0 h Inernaonal Congress on Acouscs, ICA 00 3-7 Augus 00, Sydney, Ausrala Seech recognon n by usng word grah cobnaons Shunsuke Kuraaa, Masaharu Kao and Tesuo Kosaka Graduae School of Scence

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

Data Collection Definitions of Variables - Conceptualize vs Operationalize Sample Selection Criteria Source of Data Consistency of Data

Data Collection Definitions of Variables - Conceptualize vs Operationalize Sample Selection Criteria Source of Data Consistency of Data Apply Sascs and Economercs n Fnancal Research Obj. of Sudy & Hypoheses Tesng From framework objecves of sudy are needed o clarfy, hen, n research mehodology he hypoheses esng are saed, ncludng esng mehods.

More information

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are

More information

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS ON THE WEA LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS FENGBO HANG Absrac. We denfy all he weak sequenal lms of smooh maps n W (M N). In parcular, hs mples a necessary su cen opologcal

More information

An Almost Unbiased Estimator for Population Mean using Known Value of Population Parameter(s)

An Almost Unbiased Estimator for Population Mean using Known Value of Population Parameter(s) J. a. Al. ro., o., -6 (04) Journal of aisics Alicaions & robabili An Inernaional Journal h://dx.doi.org/0.785/jsa/ahare An Almos Unbiased Esimaor for oulaion Mean using nown Value of oulaion arameer(s)

More information

We are estimating the density of long distant migrant (LDM) birds in wetlands along Lake Michigan.

We are estimating the density of long distant migrant (LDM) birds in wetlands along Lake Michigan. Ch 17 Random ffecs and Mxed Models 17. Random ffecs Models We are esmang he densy of long dsan mgran (LDM) brds n welands along Lake Mchgan. μ + = LDM per hecaren h weland ~ N(0, ) The varably of expeced

More information

The Matrix Padé Approximation in Systems of Differential Equations and Partial Differential Equations

The Matrix Padé Approximation in Systems of Differential Equations and Partial Differential Equations C Pesano-Gabno, C Gonz_Lez-Concepcon, MC Gl-Farna The Marx Padé Approxmaon n Sysems of Dfferenal Equaons and Paral Dfferenal Equaons C PESTANO-GABINO, C GONZΑLEZ-CONCEPCION, MC GIL-FARIÑA Deparmen of Appled

More information

Chapter 6: AC Circuits

Chapter 6: AC Circuits Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.

More information

CHAPTER FOUR REPEATED MEASURES IN TOXICITY TESTING

CHAPTER FOUR REPEATED MEASURES IN TOXICITY TESTING CHAPTER FOUR REPEATED MEASURES IN TOXICITY TESTING 4. Inroducon The repeaed measures sudy s a very commonly used expermenal desgn n oxcy esng because no only allows one o nvesgae he effecs of he oxcans,

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Homework 2 Solutions

Homework 2 Solutions Mah 308 Differenial Equaions Fall 2002 & 2. See he las page. Hoework 2 Soluions 3a). Newon s secon law of oion says ha a = F, an we know a =, so we have = F. One par of he force is graviy, g. However,

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The

More information

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes Quanave Cenral Dogma I Reference hp//book.bonumbers.org Inaon ranscrpon RNA polymerase and ranscrpon Facor (F) s bnds o promoer regon of DNA ranscrpon Meenger RNA, mrna, s produced and ranspored o Rbosomes

More information

Bayesian Model Selection for Structural Break Models *

Bayesian Model Selection for Structural Break Models * Baesan Model Selecon for Srucural Brea Models * Andrew T. Levn Federal eserve Board Jere M. Pger Unvers of Oregon Frs Verson: Noveber 005 Ths verson: Aprl 007 Absrac: We ae a Baesan approach o odel selecon

More information

Conservation of Momentum. The purpose of this experiment is to verify the conservation of momentum in two dimensions.

Conservation of Momentum. The purpose of this experiment is to verify the conservation of momentum in two dimensions. Conseraion of Moenu Purose The urose of his exerien is o erify he conseraion of oenu in wo diensions. Inroducion and Theory The oenu of a body ( ) is defined as he roduc of is ass () and elociy ( ): When

More information