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

Size: px
Start display at page:

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

Transcription

1 Opn Journal of Sascs Publshd Onln Ocobr 04 n Scs hp://scrporg/ournal/os hp://ddoorg/0436/os Mur ao Esmaors Usng Mul-Aular Varabls and Arbus for To-Phas Samplng Paul Mang Waru John Kung u Jams Kahr Dparmn of Sascs and Acuaral Scnc Knaa Unvrs Narob Kna Emal: Warupaul@ahoocom ohnkungu08@ahoocom cvd 5 Jul 04; rvsd 7 Augus 04; accpd Spmbr 04 Coprgh 04 b auhors and Scnfc sarch Publshng Inc Ths ork s lcnsd undr h Crav Commons Arbuon Inrnaonal Lcns (CC BY hp://cravcommonsorg/lcnss/b/40/ Absrac In hs papr hav proposd hr classs of mur rao smaors for smang populaon man b usng nformaon on aular varabls and arbus smulanousl n o-phas samplng undr full paral and no nformaon cass and analzd h proprs of h smaors A smulad sud as carrd ou o compar h prformanc of h proposd smaors h h sng smaors of fn populaon man I has bn found ha h mur rao smaor n full nformaon cas usng mulpl aular varabls and arbus s mor ffcn han man pr un rao smaor usng on aular varabl and on arbu rao smaor usng mulpl aular varabl and mulpl aular arbus and mur rao smaors n boh paral and no nformaon cas n o-phas samplng A mur rao smaor n paral nformaon cas s mor ffcn han mur rao smaors n no nformaon cas Kords ao Esmaor Mulpl Aular Varabls Mulpl Aular Arbus To-Phas Samplng B-Sral Corrlaon Coffcn Inroducon Th hsor of usng aular nformaon n surv samplng s as old as hsor of h surv samplng Th ork of Nman [] ma b rfrrd o as h nal orks hr aular nformaon has bn usd Cochran [] usd aular nformaon n sngl-phas samplng o dvlop h rao smaor for smaon of populaon man In h rao smaor h sud varabl and h aular varabl had a hgh posv corrlaon and h rgrsson ln as passng hrough h orgn Hansn and Hurz [3] also suggsd h us of aular Ho o c hs papr: Waru PM Kung u J and Kahr J (04 Mur ao Esmaors Usng Mul-Aular Varabls and Arbus for To-Phas Samplng Opn Journal of Sascs hp://ddoorg/0436/os

2 P M Waru al nformaon n slcng h sampl h varng probabls If rgrsson ln s sll lnar bu dos no pass hrough h orgn h rgrsson smaor s usd Wason [4] usd h rgrsson smaor of laf ara on laf gh o sma h avrag ara of h lavs on a plan Olkn [5] as h frs prson o us nformaon on mor han on aular varabl hch as posvl corrlad h h varabl undr sud usng a lnar combnaon of rao smaor basd on ach aular varabl a [6] suggsd a mhod of usng mul-aular nformaon n sampl surv Sngh [7] proposd a rao-cum-produc smaor and s mul-varabl prsson Th concp of doubl samplng as frs proposd b Nman [] n samplng human populaons hn h man of aular varabl as unknon I as lar ndd o mul-phas b obson [8] Ahmad [9] proposd a gnralzd mulvara rao and rgrsson smaors for mul-phas samplng hl Zahoor Muhhamad and Munr [0] suggsd a gnralzd rgrsson-cum-rao smaor for o-phas samplng usng mulpl aular varabls Jha Sharma and Grovr [] proposd a faml of smaors usng nformaon on aular arbu Th usd knon nformaon of populaon proporon possssng an arbu (hghl corrlad h sud varabl Y Th arbu ar normall usd hn h aular varabls ar no avalabl g amoun of mlk producd and a parcular brd of co or amoun of ld of ha and a parcular var of ha Jha Sharma and Grovr [] usd h nformaon on aular arbus n rao smaor n smang populaon man of h varabl of nrs usng knon arbus such as coffcn of varaon coffcn kuross and pon b-sral corrlaon coffcn Th smaor prformd br han h usual sampl man and Nak and Gupa [] smaor Jha Sharma and Grovr [] also usd h aular arbu n rgrsson produc and rao p ponnal smaor follong h ork of Bahl and Tua [3] Hanf Ha and Shahbaz [4] proposd a gnral faml of smaors usng mulpl aular arbu n sngl- and doubl-phas samplng Th smaor had a smallr MSE compard o ha of Jha Sharma and Grovr [] Th also ndd hr ork o rao smaor hch as gnralzaon of Nak and Gupa [] smaor n sngl- and doubl-phas samplngs h full nformaon paral nformaon and no nformaon Kung u and Odongo [5] and [6] proposd rao-cum-produc smaors usng mulpl aular arbus n sngland o-phas samplng Mon Shahbaz and Hanf [7] proposd a class of mur rao and rgrsson smaors for sngl-phas samplng for smang populaon man b usng nformaon on aular varabls and arbus smulanousl In hs papr ll nd h mur rao smaor proposd b Mon Shahbaz and Hanf [7] n sngl-phas samplng o o-phas samplng undr full paral and no nformaon cas srags nroducd b Samuddn and Hanf [8] and also ncorpora Arora and Bans [9] approach n rng don h man suard rror Prlmnars Noaon and Assumpon Consdr a populaon of N uns L Y b h varabl for hch an o sma h populaon man and X X Xk ar k aular varabls For o-phas samplng dsgn l n and n ( n < n ar sampl h szs for frs and scond phas rspcvl ( and ( dno h aular varabls form frs and scond phas sampls rspcvl and dno h varabl of nrs from scond phas X and C h dno h populaon mans and coffcn of varaon of aular varabls rspcvl and dnos h populaon corrlaon coffcn of Y and X Furhr l θ θ ( θ < θ n N n N hr and ( ( Y + X + ( ( ( ( ( + ( ( ( and X k ar samplng rror and ar vr small W assum ha ( ( ( ( ( (0 E E E 0 ( 777

3 P M Waru al Consdr a sampl of sz n dran b smpl random samplng hou rplacmn from a populaon of sz N L and dnos h obsrvaons on varabl and r rspcvl for h h un hr k+ k+ In dfnng h arbus assum compl dchoom so ha L N A and a h h f un of populaon possss aular arbu ( 0 ohrs n b h oal numbr of uns n h populaon and sampl rspcvl pos- A a sssng arbu L P and p ( b h corrspondng proporon of uns possssng a spcfc N n arbus and s h man of h man varabl a scond phas L p ( and p ( dno h h aular arbu form frs and scond phas sampls rspcvl and dno h varabl of nrs from scond phas Th man of man varabl of nrs a scond phas ll b dnod b Also l us dfn hr z L Also v z and ( ( hn X ( hn ( ( p P p P (3 ( ( ( ( ar samplng rror and ar vr small W assum ha ( ( ( ( ( E E E 0 ( ( X ( ( v Thrfor v + Smlarl X X X X ( ( ( ( ( ( ( ( ( ( ( v + ( ( + X X X W shall ak v o rm of ordr ( ( ( ( + X X Smlarl ( ( X + X n as ( ( v hnc X Th coffcn of varaon and corrlaon coffcn ar gvn b u ( + P ( ( (4 P S S S S C C C Y X P SS Sz S S z Pb and Pb SS SS SS z (5 Thn for smpl random samplng hou rplacmn for boh frs and scond phass r b usng phas s opraon of pcaons as: 778

4 P M Waru al ( θ ( ( ( ( ( ( ( ( θ θ θ θ ( ( θ ( ( θ Pb ( ( ( ( ( ( θ ( ( ( ( ( ( θ θ θ ( ( ( ( ( ( ( ( ( ( θ θ PC E θ XXC C ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ; θ θ (( ( ( ( ( ( ( θ θ XC C ;( ( ( ( ( ( ( ( ; θ θ ( ( ( ( ( ( ( ; θ θ E Y C E P C E X C E YX C C E YP C C E YX C C E X C E E θ PPC C E θ θ YX C C Pb E PPC C E X E XXC C E PPC C A A ( C ( T (7 Ad A A ( Arora and La [ 9] Th follong noaons ll b usd n drvng h man suar rrors of proposd smaors : Drmnan of populaon corrlaon mar of varabls and r : Drmnan of h mnor of p corrspondng o h h lmn of : Dnos h mulpl coffcn of drmnaon of on r and r r r : Dnos h mulpl coffcn of drmnaon of on and : Drmnan of populaon corrlaon mar of varabls r and r r : Drmnan of populaon corrlaon mar of varabls and : Drmnan of h corrlaon mar of r and r : Drmnan of h corrlaon mar of and : Drmnan of h mnor corrspondng o r r ( and : Drmnan of h mnor corrspondng o of h corrlaon mar of of h corrlaon mar of Man pr Un n To-Phas Samplng ( (6 (8 and (9 Th sampl man usng smpl random samplng hou rplacmn n o-phas samplng s gvn b s gvn b n n (0 779

5 P M Waru al hl s varanc s gvn ( θ Var ( Y C 3 ao Esmaor Usng Aular Varabl n To-Phas Samplng L n n b h sampl man of h aular varabl n o-phas samplng Th rao smaor hn nformaon on on aular varabls s avalabl for populaon (full nformaon cas s: V α X Th man suar rror of V can b rn as: ( V θy ( C α C αcc ( MSE + (3 C hr α and ar h opmum valu and h corrlaon coffcn rspcvl C 4 ao Esmaor Usng Mulpl Aular Varabls n To-Phas Samplng Th rao smaor b Ha [9] hn nformaon on k aular varabls s avalabl for populaon (full nformaon cas s: Th opmum valus of unknon consans ar β β βk X X X (4 k MV ( ( ( k Th man suar rror of MV can b rn as: + C k β ( C k ( MV θy C ( k MSE (5 (6 5 ao Esmaor Usng Aular Arbu n To-Phas Samplng In ordr o hav an sma of h populaon man Y of h sud varabl assumng h knoldg of h populaon proporon P Nak and Gupa [] dfnd rao smaor of populaon man hn h pror nformaon of populaon proporon of uns possssng h sam arbu s varabl Nak and Gupa [] proposd h follong smaor: P p A Th MSE of A up o h frs ordr of appromaon ar gvn rspcvl b α ( A θy ( C α CP αcc P Pb (7 MSE + (8 C hr α and Pb ar h opmum valu and h b-sral corrlaon coffcn rspcvl C 6 ao Esmaor Usng Mulpl Aular Arbus n To-Phas Samplng Th rao smaors b Hanf Ha and Shahbaz [4] for o-phas samplng usng nformaon on mulpl 780

6 P M Waru al aular arbus s gvn b Th opmum valus of unknon consans ar γ γ γ P P P (9 MA p ( p ( p ( C + γ ( C Th MSE of h MA up o h frs ordr of appromaon s gvn b In gnral hs smaors hav a bas of ordr h uan bas s s of ordr 3 Mhodolog n ( MA θy C ( Pb MSE (0 ( n Snc h sandard rror of h smas s of ordr n and bcoms nglgbl as n bcoms larg 3 Mur ao Esmaors Usng Mul-Aular Varabl and Arbus for To-Phas Samplng (Full Informaon Cas If sma a sud varabl hn nformaon on all aular varabls s avalabl from populaon s ulzd n h form of hr mans B akng h advanag of mur rao smaors chnu for o-phas samplng a gnralzd smaor for smang populaon man of sud varabl Y h h us of mul aular varabls and arbus ar suggsd as: MVAF( 30 α α αk βk+ βk+ β X X X P P P (30 k k+ k+ ( ( ( p k ( p k ( p + k+ ( Usng (0 (3 and (4 n (30 and gnorng h scond and hghr rms for ach panson of produc and afr smplfcaon r + Y Y Y (3 MVAF( 30 Th man suard rror of MVAF( 30 s gvn b k h+ k ( ( α β X k+ P k h+ k ( ( MSE ( MVAF( 30 E( MVAF( 30 Y E α Y β Y X k+ P W dffrna h Euaon (3 parall h rspc o hn ua o zro usng (6 (7 and (9 g (3 α ( k and β ( k+ k+ α + C ( (33 C C + β ( C Usng normal uaon ha s usd o fnd h opmum valus gvn (3 can r (34 78

7 P M Waru al k h+ k ( ( MSE ( MVAF( 30 E α Y β Y X k+ P Takng pcaon and usng (6 n (35 g (35 k k+ h Y Y MSE ( MVAF( 30 θy C α XCC P β CC Pb X k+ P Subsung h opmum (33 and (34 n (36 g (36 k k h C + C MSE( MVAF( 30 θy C + ( CC ( + CC Pb C C Usng (8 g (37 k k h + MSE( MVAF( 30 θy C + ( + ( Pb k+ (38 MSE ( ( MVAF( 30 θy C ( ( ( ( θ MVA 30 Y C ( MSE 3 Mur ao Esmaor Usng Mul-Aular Varabl and Arbus n To-Phas Samplng (Paral Informaon Cas (39 (30 In hs cas suppos hav no nformaon on all s and aular varabls bu onl for r and g aular varabls from populaon Consdrng mur rao chnu of smang chnu h populaon man of sud varabl Y can b smad for o-phas samplng usng mul-aular varabls and arbus as: MVAP( 3 γ k+ ( k + Pk + α β α β αs βs αr+ αr+ αk ( X ( X ( r X ( s ( r s ( + + k ( ( ( ( ( r ( r ( s ( + s+ ( k λk γk λk γh λh γh γh γ p p( k P p( k h P p( p( p( + + h h+ h+ p ( k + p ( p k + ( k+ p ( p k ( h p ( p h ( p h ( p + + h+ ( (3 Usng (0 (3 and (4 n (3 and gnorng h scond and hghr rms for ach panson of produc and afr smplfcaon r + Y Y + Y + Y MVAP( 3 r ( r ( s+ r k ( ( k+ h ( ( ( α β α γ X X X P + k+ h h+ ( ( ( Yλ Yγ P h+ P Man suard rror of MVAP( 3 smaor s gvn b (3 78

8 P M Waru al MVAP( 3 EE + Y Y + Y + Y r ( r ( s+ r k ( ( k+ h ( ( ( α β α γ X X X P k+ h h+ ( ( ( Yλ + Yγ P h+ P W dffrna h Euaon (33 h rspc o α ( r β ( r α ( + + γ ( + + λ ( + + ( r r k k k h k k h o zro and us (6 (7 and (9 Th opmum valus ar as follos + C m α ( C m ( r ; C + p γ ( C m k+ k+ h; + C r β ( ( r ; C C λ C ( + k+ k+ h; + C s α ( r+ r+ k; C s + C γ ( ψ h+ h+ C p Usng normal uaon ha ar usd o fnd h opmum valus gvn (33 can r MSE ( MVAP( 3 EE + Y Y + Y r ( r ( s+ r k ( ( ( α β α X X X k+ h ( k+ h ( h+ m ( ( ( + Yγ Yλ + Yγ P P h+ P Takng pcaon and usng (6 n (35 g MSE + ( MVAP( 3 ( θ θ γ PC C Pb θ λ PC C Pb + ( θ θ γ PC C Pb (33 γ h+ h+ and ua Y Y Y θ + θ θ α θ β + θ θ α X X X r r r+ s k Y C ( X CC ( X CC X CC r+ Y Y Y P P P + k + k h k+ k+ h+ Usng h opmum valu (34 n (36 g (34 (35 (36 783

9 P M Waru al MSE ( MVAP( 3 Y C + r r + + θ ( θ θ ( θ ( + + r+ s k + k h + + r+ k+ ( θ θ ( ( θ θ ( θ + θ θ γ + k h + + ( Pb ( ( Pb k+ h + Pb (37 MSE MSE r r + + ( MVAP( 3 Y C θ + ( θ θ ( θ ( ( MVAP( 3 r+ s k + k h ( θ θ ( + ( θ θ ( r+ k+ θ + θ θ γ + k h + + ( Pb ( ( Pb k+ h+ r r s k k h + + Y C ( θ θ + ( + ( ( + Pb r+ k + r + k h + ( γ ( ( Pb + θ + + h+ Pb k+ ( MSE ( ( θ θ ( MVAP( 3 Y C ( + ( ( ( r + k h θ r + + ( ( + k ( ( + ( Pb Pb (38 (39 (30 784

10 P M Waru al Usng (8 n (3 g Smplfng (3 g MSE ( ( ( ( Y C MVAP 3 ( θ θ + θ ( ( ( ( ( ( ( ( ( MVAP 3 θ θ ( + θ MSE Y C ( ( ( ( ( ( ( MVAP 3 θ ( + θ MSE Y C (3 (3 (33 33 Mur ao Esmaor Usng Mul-Aular Varabl and Arbus n To-Phas Samplng (No Informaon Cas If sma a sud varabl hn nformaon on all aular varabls s unavalabl from populaon s ulzd n h form of hr mans B akng h advanag of mur rao chnu for o-phas samplng a gnralzd smaor for smang populaon man of sud varabl Y h h us of mul aular varabls and arbus ar suggsd as: MVAN( 3 α α αs γk+ γk+ γ ( ( ( p( p ( p k k k ( + + ( ( ( p k ( k + p ( p k+ ( (34 Usng (0 (3 and (4 n (34 and gnorng h scond and hghr rms for ach panson of produc and afr smplfcaon r k ( k+ h ( ( ( MVAN( 3 + Yα + Yγ (35 X P Man suard rror of MVAN( 3 smaor s gvn b MSE k ( k+ h ( ( ( ( MVAN( 3 E + Yα + Yγ X P W dffrna h Euaon (37 parall h rspc o α ( k and hn ua o zro usng (6 (7 and (9 g (36 β ( k+ k+ + C k α ( (37 C C + β ( (38 C Usng normal uaon ha ar usd o fnd h opmum valus gvn (36 can r k ( k+ h ( ( ( MSE ( MVAN( 3 E + Yα + Yγ X k+ P Takng pcaon and usng (6 n (39 g (39 k k+ h ( ( Y C MVAN 3 θ + ( θ θ α CC ( + θ θ β CC Pb k+ MSE (

11 P M Waru al k k+ h MSE( MVAN( 3 θy C θ + ( θ θ ( + ( θ θ ( Pb ( ( ( MSE( MVAN( 3 θy C ( θ θ + ( + θ ( θ + ( ( MSE θ θ ( ( Y C MVAN 3 + ( ( + θ ( ( ( Usng (8 n (334 g Smplfng (335 g MSE ( ( ( Y C MVAN 3 ( θ θ + θ ( ( ( ( Y C ( ( MVAN 3 θ θ ( + θ MSE ( ( ( ( ( MVAN 3 θ ( + θ MSE Y C (33 (33 (333 (334 (335 ( Bas and Conssnc of Mur ao Esmaors Ths mur rao smaors usng mulpl aular varabls and arbus n o-phas samplng ar basd Hovr hs bass ar nglgbl for larg sampls ha s ( n 30 I s asl shon ha h mur rao smaors ar conssn smaors usng mulpl aular varabls snc h ar lnar combnaons of conssn smaors follos ha h ar also conssn 4 Smulaon sul and Dscusson W carrd ou daa smulaon prmns o compar h prformanc of mur rao smaors usng mulpl aular varabls and arbus n o-phas samplng h rao smaor usng on aular varabl and on aular arbu or rao smaor usng mulpl aular varabl or mulpl aular arbus n o-phas samplng smaors for fn populaon All h rsuls r oband afr carrng ou o hundrd smulaons and akng hr avrag Sud varabl N 800 n 96 n 34 man 50 sandard dvaon 6 For rao smaor h aular varabl s posvl corrlad h h sud varabl and h ln passs hrough h orgn N 500 n 96 n 34 man 9 sandard dvaon 67 N 500 n 96 n 34 man 38 sandard dvaon 86 Corrlaon coffcns For rao smaor h aular arbus s posvl corrlad h h sud varabl and h ln passs hrough h orgn N 500 n 96 n 34 man

12 P M Waru al N 500 n 96 man 0486 Corrlaon coffcns In ordr o valua h ffcnc gan could achv b usng h proposd smaors hav calculad h varanc of man pr un and h man suard rror of all smaors hav consdrd W hav hn calculad prcn rlav ffcnc of ach smaor n rlaon o varanc of man pr un W hav hn compard h prcn rlav ffcnc of ach smaor h smaor h h hghs prcn rlav ffcnc s consdrd o b h mor ffcn han h ohr smaors Th prcn rlav ffcnc s calculad usng h follong formula ( Y ˆ ( ˆ Var ff 00 (40 MSE Tabl shos prcn rlav ffcnc of proposd smaor h rspc o man pr un smaor for sngl-phas samplng I s vr clar from Tabl ha our proposd mur rao smaor usng mulpl aular varabls and mulpl aular arbus smulanousl s h mos ffcn compard o rao smaor usng on aular varabl and on aular arbu or rao smaor usng mulpl aular varabl or mulpl aular arbus n o-phas samplng Tabl compars h ffcnc of full nformaon cas and paral cas o no nformaon cas and full o paral nformaon cas of proposd mur rao smaors I s obsrvd ha h full nformaon cas and paral nformaon cas ar mor ffcn han no nformaon cas bcaus h hav hghr prcn rlav ffcnc han no nformaon cas In addon h full nformaon cas s mor ffcn han h paral nformaon cas bcaus has a hghr prcn rlav ffcnc han paral nformaon cas 5 Concluson Th proposd mur rao smaor undr full nformaon cas s rcommndd for smang h fn populaon man snc s h mos ffcn smaor compard o man pr un rao smaor usng on aular varabl rao smaor usng on aular arbu rao smaor usng mulpl aular varabl and rao smaor usng mulpl aular arbus n o-phas samplng In cas som aular varabls or arbus ar unknon rcommnd mur rao smaor undr paral nformaon cas snc s mor ( Y ˆ Tabl lav ffcnc of sng and proposd smaor h rspc o man pr un smaor for o-phas samplng Esmaor lav prcn ffcnc h rspc o man pr un n o-phas samplng var ( 00 V 46 A 85 MV MA 33 (proposd MVAF( Tabl Comparsons of full paral and no nformaon cass for proposd rao-cum-produc smaor usng mulpl aular varabls Populaon Prcn rlav ffcnc of full and paral o no nformaon Prcn rlav ffcnc of full o paral n formaon cas Esmaor MVAN( 3 MVAP( 3 MVAF( 30 MVAP( 3 MVAF( 30 lav prcn ffcnc

13 P M Waru al ffcn han h mur rao smaor undr no nformaon cas and f all ar unknon rcommnd h mur rao smaor undr no nformaon cas o sma fn populaon man frncs [] Nman J (938 Conrbuon o h Thor of Samplng Human Populaons Journal of h Amrcan Sascal Assocaon hp://ddoorg/0080/ [] Cochran WG (940 Th Esmaon of h Ylds of h Cral Eprmns b Samplng for h ao of Gran o Toal Produc Journal of Agrculural Scnc hp://ddoorg/007/s [3] Hansn MH and Hurz WN (943 On h Thor of Samplng from Fn Populaons Annals of Mahmacal Sascs hp://ddoorg/04/aoms/ [4] Wason DJ (937 Th Esmaon of Laf Aras Journal of Agrculural Scnc hp://ddoorg/007/s x [5] Olkn I (958 Mulvara ao Esmaon for Fn Populaon Bomrka hp://ddoorg/0093/bom/45-54 [6] a D (965 On a Mhod of Usng Mul-Aular Informaon n Sampl Survs Journals of h Amrcan Sascal Assocaon hp://ddoorg/0080/ [7] Sngh MP (967 ao-cum-produc Mhod of Esmaon Mrka 34-4 hp://ddoorg/0007/bf06348 [8] obson DS (95 Mulpl Samplng of Arbus Journal of h Amrcan Sascal Assocaon hp://ddoorg/0080/ [9] Ahmad Z (008 Gnralzd Mulvara ao and grsson Esmaors for Mul-Phas Samplng Unpublshd PhD Thss Naonal Collg of Busnss Admnsraon and Economcs Lahor [0] Zahoor A Muhhamad H and Munr A (009 Gnralzd grsson-cum-ao Esmaors for To Phas Samplng Usng Mulpl Aular Varabls Paksan Journal of Sascs [] Jha HS Sharma MK and Grovr LK (006 A Faml of Esmaor of Populaon Man Usng Informaon on Aular Arbus Paksan Journal of Sascs [] Nak VD and Gupa PC (996 A No on Esmaon of Man h Knon Populaon of Aular Characr Journal of h Indan Soc of Agrculural Sascs [3] Bahl S and Tua K (99 ao and Produc Tp Esmaor Informaon and Opmzaon Scncs [4] Hanf M Ha IU and Shahbaz MQ (009 On a N Faml of Esmaor Usng Mulpl Aular Arbus World Appld Scnc Journal 49-4 [5] Kung u J and Odongo L (04 ao-cum-produc Esmaor Usng Mulpl Aular Arbus n Sngl Phas Samplng Opn Journal of Sascs hp://ddoorg/0436/os [6] Kung u J and Odongo L (04 ao-cum-produc Esmaor Usng Mulpl Aular Arbus n To-Phas Samplng Opn Journal of Sascs hp://ddoorg/0436/os [7] Mon M Shahbaz Q and Hanf M (0 Mur ao and grsson Esmaors Usng Mul-Aular Varabl and Arbus n Sngl Phas Samplng World Appld Scncs Journal [8] Smuddn M and Hanf M (007 Esmaon of Populaon Man n Sngl and To Phas Samplng h or hou Addonal Informaon Paksan Journal of Sascs [9] Arora S and Lal B (989 N Mahmacal Sascs Saa Prakashan N Dlh 788

14

IMPROVED RATIO AND PRODUCT TYPE ESTIMATORS OF FINITE POPULATION MEAN IN SIMPLE RANDOM SAMPLING

IMPROVED RATIO AND PRODUCT TYPE ESTIMATORS OF FINITE POPULATION MEAN IN SIMPLE RANDOM SAMPLING REVISTA IVESTIGAIO OPERAIOAL VOL. 6, O., 7-76, 6 IMPROVED RATIO AD PRODUT TPE ESTIMATORS OF FIITE POPULATIO MEA I SIMPLE RADOM SAMPLIG Gajndra K. Vshwaarma, Ravndra Sngh, P.. Gupa, Sarla Par Dparmn of

More information

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling Gloal Journal of Scnc Fronr Rsarch Mahmacs and Dcson Scncs Volum Issu 4 Vrson.0 ar 0 p : Doul Blnd Pr Rvwd Inrnaonal Rsarch Journal Pulshr: Gloal Journals Inc. USA Onln ISSN: 49-466 & Prn ISSN: 0975-5896

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Almost unbiased exponential estimator for the finite population mean

Almost unbiased exponential estimator for the finite population mean Almos ubasd poal smaor for f populao ma Rajs Sg, Pakaj aua, ad rmala Saa, Scool of Sascs, DAVV, Idor (M.P., Ida (rsgsa@aoo.com Flor Smaradac ar of Dparm of Mamacs, Uvrs of Mco, Gallup, USA (smarad@um.du

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

Improved Ratio Estimators for Population Mean Based on Median Using Linear Combination of Population Mean and Median of an Auxiliary Variable

Improved Ratio Estimators for Population Mean Based on Median Using Linear Combination of Population Mean and Median of an Auxiliary Variable rcan Journal of Opraonal Rsarch : -7 DOI:.59/j.ajor.. Iprov Rao saors for Populaon an as on an Usng Lnar Cobnaon of Populaon an an an of an uxlar arabl Subhash Kuar aav San Sharan shra * lok Kuar Shukla

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

More information

Almost Unbiased Exponential Estimator for the Finite Population Mean

Almost Unbiased Exponential Estimator for the Finite Population Mean Rajs Sg, Pakaj aua, rmala Saa Scool of Sascs, DAVV, Idor (M.P., Ida Flor Smaradac Uvrs of Mco, USA Almos Ubasd Epoal Esmaor for F Populao Ma Publsd : Rajs Sg, Pakaj aua, rmala Saa, Flor Smaradac (Edors

More information

innovations shocks white noise

innovations shocks white noise Innovaons Tm-srs modls ar consrucd as lnar funcons of fundamnal forcasng rrors, also calld nnovaons or shocks Ths basc buldng blocks sasf var σ Srall uncorrlad Ths rrors ar calld wh nos In gnral, f ou

More information

9. Simple Rules for Monetary Policy

9. Simple Rules for Monetary Policy 9. Smpl Ruls for Monar Polc John B. Talor, Ma 0, 03 Woodford, AR 00 ovrvw papr Purpos s o consdr o wha xn hs prscrpon rsmbls h sor of polc ha conomc hor would rcommnd Bu frs, l s rvw how hs sor of polc

More information

Supplementary Figure 1. Experiment and simulation with finite qudit. anharmonicity. (a), Experimental data taken after a 60 ns three-tone pulse.

Supplementary Figure 1. Experiment and simulation with finite qudit. anharmonicity. (a), Experimental data taken after a 60 ns three-tone pulse. Supplmnar Fgur. Eprmn and smulaon wh fn qud anharmonc. a, Eprmnal daa akn afr a 6 ns hr-on puls. b, Smulaon usng h amlonan. Supplmnar Fgur. Phagoran dnamcs n h m doman. a, Eprmnal daa. Th hr-on puls s

More information

ELEN E4830 Digital Image Processing

ELEN E4830 Digital Image Processing ELEN E48 Dgal Imag Procssng Mrm Eamnaon Sprng Soluon Problm Quanzaon and Human Encodng r k u P u P u r r 6 6 6 6 5 6 4 8 8 4 P r 6 6 P r 4 8 8 6 8 4 r 8 4 8 4 7 8 r 6 6 6 6 P r 8 4 8 P r 6 6 8 5 P r /

More information

The Variance-Covariance Matrix

The Variance-Covariance Matrix Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o

More information

Lecture 4 : Backpropagation Algorithm. Prof. Seul Jung ( Intelligent Systems and Emotional Engineering Laboratory) Chungnam National University

Lecture 4 : Backpropagation Algorithm. Prof. Seul Jung ( Intelligent Systems and Emotional Engineering Laboratory) Chungnam National University Lcur 4 : Bacpropagaon Algorhm Pro. Sul Jung Inllgn Sm and moonal ngnrng Laboraor Chungnam Naonal Unvr Inroducon o Bacpropagaon algorhm 969 Mn and Papr aac. 980 Parr and Wrbo dcovrd bac propagaon algorhm.

More information

Boosting and Ensemble Methods

Boosting and Ensemble Methods Boosng and Ensmbl Mhods PAC Larnng modl Som dsrbuon D ovr doman X Eampls: c* s h arg funcon Goal: Wh hgh probably -d fnd h n H such ha rrorh,c* < d and ar arbrarly small. Inro o ML 2 Wak Larnng

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of

More information

Advanced Queueing Theory. M/G/1 Queueing Systems

Advanced Queueing Theory. M/G/1 Queueing Systems Advand Quung Thory Ths slds ar rad by Dr. Yh Huang of Gorg Mason Unvrsy. Sudns rgsrd n Dr. Huang's ourss a GMU an ma a sngl mahn-radabl opy and prn a sngl opy of ah sld for hr own rfrn, so long as ah sld

More information

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl

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

Theoretical Seismology

Theoretical Seismology Thorcal Ssmology Lcur 9 Sgnal Procssng Fourr analyss Fourr sudd a h Écol Normal n Pars, augh by Lagrang, who Fourr dscrbd as h frs among Europan mn of scnc, Laplac, who Fourr rad lss hghly, and by Mong.

More information

CIVL 8/ D Boundary Value Problems - Triangular Elements (T6) 1/8

CIVL 8/ D Boundary Value Problems - Triangular Elements (T6) 1/8 CIVL 8/7 -D Boundar Valu Problm - rangular Elmn () /8 SI-ODE RIAGULAR ELEMES () A quadracall nrpolad rangular lmn dfnd b nod, hr a h vrc and hr a h mddl a ach d. h mddl nod, dpndng on locaon, ma dfn a

More information

Implementation of the Extended Conjugate Gradient Method for the Two- Dimensional Energized Wave Equation

Implementation of the Extended Conjugate Gradient Method for the Two- Dimensional Energized Wave Equation Lonardo Elcronc Jornal of raccs and Tchnolos ISSN 58-078 Iss 9 Jl-Dcmbr 006 p. -4 Implmnaon of h Endd Cona Gradn Mhod for h Two- Dmnsonal Enrd Wav Eqaon Vcor Onoma WAZIRI * Snda Ass REJU Mahmacs/Compr

More information

Homework: Introduction to Motion

Homework: Introduction to Motion Homwork: Inroducon o Moon Dsanc vs. Tm Graphs Nam Prod Drcons: Answr h foowng qusons n h spacs provdd. 1. Wha do you do o cra a horzona n on a dsancm graph? 2. How do you wak o cra a sragh n ha sops up?

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g

More information

(heat loss divided by total enthalpy flux) is of the order of 8-16 times

(heat loss divided by total enthalpy flux) is of the order of 8-16 times 16.51, Rok Prolson Prof. Manl Marnz-Sanhz r 8: Convv Ha ransfr: Ohr Effs Ovrall Ha oss and Prforman Effs of Ha oss (1) Ovrall Ha oss h loal ha loss r n ara s q = ρ ( ) ngrad ha loss s a S, and sng m =

More information

Institute of Actuaries of India

Institute of Actuaries of India Insiu of Acuaris of India ubjc CT3 Probabiliy and Mahmaical aisics Novmbr Examinaions INDICATIVE OLUTION Pag of IAI CT3 Novmbr ol. a sampl man = 35 sampl sandard dviaion = 36.6 b for = uppr bound = 35+*36.6

More information

t=0 t>0: + vr - i dvc Continuation

t=0 t>0: + vr - i dvc Continuation hapr Ga Dlay and rcus onnuaon s rcu Equaon >: S S Ths dffrnal quaon, oghr wh h nal condon, fully spcfs bhaor of crcu afr swch closs Our n challng: larn how o sol such quaons TUE/EE 57 nwrk analys 4/5 NdM

More information

SIMEON BALL AND AART BLOKHUIS

SIMEON BALL AND AART BLOKHUIS A BOUND FOR THE MAXIMUM WEIGHT OF A LINEAR CODE SIMEON BALL AND AART BLOKHUIS Absrac. I s shown ha h paramrs of a lnar cod ovr F q of lngh n, dmnson k, mnmum wgh d and maxmum wgh m sasfy a cran congrunc

More information

Convergence of Quintic Spline Interpolation

Convergence of Quintic Spline Interpolation Inrnaonal Journal o ompur Applcaons 97 8887 Volum 7 No., Aprl onvrgnc o Qunc Spln Inrpolaon Y.P. Dub Dparmn O Mamacs, L.N..T. Jabalpur 8 Anl Sukla Dparmn O Mamacs Gan Ganga ollg O Tcnog, Jabalpur 8 ASTRAT

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug

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

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule Lcur : Numrical ngraion Th Trapzoidal and Simpson s Rul A problm Th probabiliy of a normally disribud (man µ and sandard dviaion σ ) vn occurring bwn h valus a and b is B A P( a x b) d () π whr a µ b -

More information

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 )

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 ) AR() Procss Th firs-ordr auorgrssiv procss, AR() is whr is WN(0, σ ) Condiional Man and Varianc of AR() Condiional man: Condiional varianc: ) ( ) ( Ω Ω E E ) var( ) ) ( var( ) var( σ Ω Ω Ω Ω E Auocovarianc

More information

Jones vector & matrices

Jones vector & matrices Jons vctor & matrcs PY3 Colást na hollscol Corcagh, Ér Unvrst Collg Cork, Irland Dpartmnt of Phscs Matr tratmnt of polarzaton Consdr a lght ra wth an nstantanous -vctor as shown k, t ˆ k, t ˆ k t, o o

More information

On the Derivatives of Bessel and Modified Bessel Functions with Respect to the Order and the Argument

On the Derivatives of Bessel and Modified Bessel Functions with Respect to the Order and the Argument Inrnaional Rsarch Journal of Applid Basic Scincs 03 Aailabl onlin a wwwirjabscom ISSN 5-838X / Vol 4 (): 47-433 Scinc Eplorr Publicaions On h Driais of Bssl Modifid Bssl Funcions wih Rspc o h Ordr h Argumn

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

More information

Frequency Response. Response of an LTI System to Eigenfunction

Frequency Response. Response of an LTI System to Eigenfunction Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc

More information

Analysis of decentralized potential field based multi-agent navigation via primal-dual Lyapunov theory

Analysis of decentralized potential field based multi-agent navigation via primal-dual Lyapunov theory Analyss of dcnralzd ponal fld basd mul-agn navgaon va prmal-dual Lyapunov hory Th MIT Faculy has mad hs arcl opnly avalabl. Plas shar how hs accss bnfs you. Your sory mars. Caon As Publshd Publshr Dmarogonas,

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

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

Chapter 9 Transient Response

Chapter 9 Transient Response har 9 Transn sons har 9: Ouln N F n F Frs-Ordr Transns Frs-Ordr rcus Frs ordr crcus: rcus conan onl on nducor or on caacor gornd b frs-ordr dffrnal quaons. Zro-nu rsons: h crcu has no ald sourc afr a cran

More information

UNIT #5 EXPONENTIAL AND LOGARITHMIC FUNCTIONS

UNIT #5 EXPONENTIAL AND LOGARITHMIC FUNCTIONS Answr Ky Nam: Da: UNIT # EXPONENTIAL AND LOGARITHMIC FUNCTIONS Par I Qusions. Th prssion is quivaln o () () 6 6 6. Th ponnial funcion y 6 could rwrin as y () y y 6 () y y (). Th prssion a is quivaln o

More information

Microscopic Flow Characteristics Time Headway - Distribution

Microscopic Flow Characteristics Time Headway - Distribution CE57: Traffic Flow Thory Spring 20 Wk 2 Modling Hadway Disribuion Microscopic Flow Characrisics Tim Hadway - Disribuion Tim Hadway Dfiniion Tim Hadway vrsus Gap Ahmd Abdl-Rahim Civil Enginring Dparmn,

More information

Ergodic Capacity of a SIMO System Over Nakagami-q Fading Channel

Ergodic Capacity of a SIMO System Over Nakagami-q Fading Channel DUET Journal Vol., Issu, Jun Ergodc apac of a SIO Ssm Ovr Nakagam-q Fadng hannl d. Sohdul Islam * and ohammad akbul Islam Dp. of Elcrcal and Elcronc Engnrng, Islamc Unvrs of Tchnolog (IUT, Gazpur, Bangladsh

More information

General Article Application of differential equation in L-R and C-R circuit analysis by classical method. Abstract

General Article Application of differential equation in L-R and C-R circuit analysis by classical method. Abstract Applicaion of Diffrnial... Gnral Aricl Applicaion of diffrnial uaion in - and C- circui analysis by classical mhod. ajndra Prasad gmi curr, Dparmn of Mahmaics, P.N. Campus, Pokhara Email: rajndraprasadrgmi@yahoo.com

More information

State Observer Design

State Observer Design Sa Obsrvr Dsgn A. Khak Sdgh Conrol Sysms Group Faculy of Elcrcal and Compur Engnrng K. N. Toos Unvrsy of Tchnology Fbruary 2009 1 Problm Formulaon A ky assumpon n gnvalu assgnmn and sablzng sysms usng

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

4.1 The Uniform Distribution Def n: A c.r.v. X has a continuous uniform distribution on [a, b] when its pdf is = 1 a x b

4.1 The Uniform Distribution Def n: A c.r.v. X has a continuous uniform distribution on [a, b] when its pdf is = 1 a x b 4. Th Uniform Disribuion Df n: A c.r.v. has a coninuous uniform disribuion on [a, b] whn is pdf is f x a x b b a Also, b + a b a µ E and V Ex4. Suppos, h lvl of unblivabiliy a any poin in a Transformrs

More information

Boyce/DiPrima 9 th ed, Ch 2.1: Linear Equations; Method of Integrating Factors

Boyce/DiPrima 9 th ed, Ch 2.1: Linear Equations; Method of Integrating Factors Boc/DiPrima 9 h d, Ch.: Linar Equaions; Mhod of Ingraing Facors Elmnar Diffrnial Equaions and Boundar Valu Problms, 9 h diion, b William E. Boc and Richard C. DiPrima, 009 b John Wil & Sons, Inc. A linar

More information

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis Safy and Rlably of Embddd Sysms (Schrh und Zuvrlässgk ngbr Sysm) Sochasc Rlably Analyss Safy and Rlably of Embddd Sysms Conn Dfnon of Rlably Hardwar- vs. Sofwar Rlably Tool Asssd Rlably Modlng Dscrpons

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

2.1. Differential Equations and Solutions #3, 4, 17, 20, 24, 35

2.1. Differential Equations and Solutions #3, 4, 17, 20, 24, 35 MATH 5 PS # Summr 00.. Diffrnial Equaions and Soluions PS.# Show ha ()C #, 4, 7, 0, 4, 5 ( / ) is a gnral soluion of h diffrnial quaion. Us a compur or calculaor o skch h soluions for h givn valus of h

More information

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12 EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral

More information

Charging of capacitor through inductor and resistor

Charging of capacitor through inductor and resistor cur 4&: R circui harging of capacior hrough inducor and rsisor us considr a capacior of capacianc is conncd o a D sourc of.m.f. E hrough a rsisr of rsisanc R, an inducor of inducanc and a y K in sris.

More information

SCRIBE: JAKE LEVINSON

SCRIBE: JAKE LEVINSON GL n REPRESENTATION THEORY NOTES FOR 12-03 SCRIBE: JAKE LEVINSON As th th last lctur, ths on s basd on John Stmbrdg s papr: A local charactrzaton of smpl-lacd crstals, Trans. Amr. Math. Soc. 355 (2003),

More information

FAULT TOLERANT SYSTEMS

FAULT TOLERANT SYSTEMS FAULT TOLERANT SYSTEMS hp://www.cs.umass.du/c/orn/faultolransysms ar 4 Analyss Mhods Chapr HW Faul Tolranc ar.4.1 Duplx Sysms Boh procssors xcu h sam as If oupus ar n agrmn - rsul s assumd o b corrc If

More information

Ratio-Product Type Exponential Estimator For Estimating Finite Population Mean Using Information On Auxiliary Attribute

Ratio-Product Type Exponential Estimator For Estimating Finite Population Mean Using Information On Auxiliary Attribute Raio-Produc T Exonnial Esimaor For Esimaing Fini Poulaion Man Using Informaion On Auxiliar Aribu Rajsh Singh, Pankaj hauhan, and Nirmala Sawan, School of Saisics, DAVV, Indor (M.P., India (rsinghsa@ahoo.com

More information

Comparative Study of Finite Element and Haar Wavelet Correlation Method for the Numerical Solution of Parabolic Type Partial Differential Equations

Comparative Study of Finite Element and Haar Wavelet Correlation Method for the Numerical Solution of Parabolic Type Partial Differential Equations ISS 746-7659, England, UK Journal of Informaon and Compung Scnc Vol., o. 3, 6, pp.88-7 Comparav Sudy of Fn Elmn and Haar Wavl Corrlaon Mhod for h umrcal Soluon of Parabolc Typ Paral Dffrnal Equaons S.

More information

Wave Superposition Principle

Wave Superposition Principle Physcs 36: Was Lcur 5 /7/8 Wa Suroson Prncl I s qu a common suaon for wo or mor was o arr a h sam on n sac or o xs oghr along h sam drcon. W wll consdr oday sral moran cass of h combnd ffcs of wo or mor

More information

Engineering Circuit Analysis 8th Edition Chapter Nine Exercise Solutions

Engineering Circuit Analysis 8th Edition Chapter Nine Exercise Solutions Engnrng rcu naly 8h Eon hapr Nn Exrc Soluon. = KΩ, = µf, an uch ha h crcu rpon oramp. a For Sourc-fr paralll crcu: For oramp or b H 9V, V / hoo = H.7.8 ra / 5..7..9 9V 9..9..9 5.75,.5 5.75.5..9 . = nh,

More information

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis (Schrh und Zuvrlässgk ngbr Sysm) Sochasc Rlably Analyss Conn Dfnon of Rlably Hardwar- vs. Sofwar Rlably Tool Asssd Rlably Modlng Dscrpons of Falurs ovr Tm Rlably Modlng Exampls of Dsrbuon Funcons Th xponnal

More information

Partition Functions for independent and distinguishable particles

Partition Functions for independent and distinguishable particles 0.0J /.77J / 5.60J hrodynacs of oolcular Syss Insrucors: Lnda G. Grffh, Kbrly Haad-Schffrl, Moung G. awnd, Robr W. Fld Lcur 5 5.60/0.0/.77 vs. q for dsngushabl vs ndsngushabl syss Drvaon of hrodynac Proprs

More information

Vertical Sound Waves

Vertical Sound Waves Vral Sond Wavs On an drv h formla for hs avs by onsdrn drly h vral omonn of momnm qaon hrmodynam qaon and h onny qaon from 5 and hn follon h rrbaon mhod and assmn h snsodal solons. Effvly h frs ro and

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

Multivariate Ratio Estimation With Known Population Proportion Of Two Auxiliary Characters For Finite Population

Multivariate Ratio Estimation With Known Population Proportion Of Two Auxiliary Characters For Finite Population Multvarate Rato Estmaton Wth Knon Populaton Proporton Of To Auxlar haracters For Fnte Populaton *Raesh Sngh, *Sachn Mal, **A. A. Adeara, ***Florentn Smarandache *Department of Statstcs, Banaras Hndu Unverst,Varanas-5,

More information

CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 259] B-Trees

CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 259] B-Trees CPSC 211 Daa Srucurs & Implmnaions (c) Txas A&M Univrsiy [ 259] B-Trs Th AVL r and rd-black r allowd som variaion in h lnghs of h diffrn roo-o-laf pahs. An alrnaiv ida is o mak sur ha all roo-o-laf pahs

More information

Applying Software Reliability Techniques to Low Retail Demand Estimation

Applying Software Reliability Techniques to Low Retail Demand Estimation Applyng Sofwar Rlably Tchnqus o Low Ral Dmand Esmaon Ma Lndsy Unvrsy of Norh Txas ITDS Dp P.O. Box 30549 Dnon, TX 7603-549 940 565 3174 lndsym@un.du Robr Pavur Unvrsy of Norh Txas ITDS Dp P.O. Box 30549

More information

Structural Optimization Using Metamodels

Structural Optimization Using Metamodels Srucural Opmzaon Usng Meamodels 30 Mar. 007 Dep. o Mechancal Engneerng Dong-A Unvers Korea Kwon-Hee Lee Conens. Numercal Opmzaon. Opmzaon Usng Meamodels Impac beam desgn WB Door desgn 3. Robus Opmzaon

More information

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23 BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu

More information

NAME: ANSWER KEY DATE: PERIOD. DIRECTIONS: MULTIPLE CHOICE. Choose the letter of the correct answer.

NAME: ANSWER KEY DATE: PERIOD. DIRECTIONS: MULTIPLE CHOICE. Choose the letter of the correct answer. R A T T L E R S S L U G S NAME: ANSWER KEY DATE: PERIOD PREAP PHYSICS REIEW TWO KINEMATICS / GRAPHING FORM A DIRECTIONS: MULTIPLE CHOICE. Chs h r f h rr answr. Us h fgur bw answr qusns 1 and 2. 0 10 20

More information

Economics 201b Spring 2010 Solutions to Problem Set 3 John Zhu

Economics 201b Spring 2010 Solutions to Problem Set 3 John Zhu Economics 20b Spring 200 Solutions to Problm St 3 John Zhu. Not in th 200 vrsion of Profssor Andrson s ctur 4 Nots, th charactrization of th firm in a Robinson Cruso conomy is that it maximizs profit ovr

More information

Elementary Differential Equations and Boundary Value Problems

Elementary Differential Equations and Boundary Value Problems Elmnar Diffrnial Equaions and Boundar Valu Problms Boc. & DiPrima 9 h Ediion Chapr : Firs Ordr Diffrnial Equaions 00600 คณ ตศาสตร ว ศวกรรม สาขาว ชาว ศวกรรมคอมพ วเตอร ป การศ กษา /55 ผศ.ดร.อร ญญา ผศ.ดร.สมศ

More information

Determination of effective atomic numbers from mass attenuation coefficients of tissue-equivalent materials in the energy range 60 kev-1.

Determination of effective atomic numbers from mass attenuation coefficients of tissue-equivalent materials in the energy range 60 kev-1. Journal of Physcs: Confrnc Srs PAPER OPEN ACCESS Drmnaon of ffcv aomc numbrs from mass anuaon coffcns of ssu-quvaln marals n h nrgy rang 6 kv-.33 MV To c hs arcl: Noorfan Ada B. Amn al 7 J. Phys.: Conf.

More information

Comparison of the performance of best linear unbiased predictors (BLUP)

Comparison of the performance of best linear unbiased predictors (BLUP) Comparon of h prformanc of b lnar unbad prdcor (BLUP) Pkang Yao Synh Spn 130 Wrgh Lan Ea W Chr, PA 19380 USA yao.pr@ynh.com Edward J. Sank III Dparmn of Publc Halh 401 Arnold Hou Unvry of Maachu 711 Norh

More information

Robust decentralized control with scalar output of multivariable structurally uncertain plants with state delay 1

Robust decentralized control with scalar output of multivariable structurally uncertain plants with state delay 1 rprns of h 8h IFAC World Congrss lano Ial Augus 8 - Spmbr obus dcnralzd conrol wh scalar oupu of mulvarabl srucurall uncran plans wh sa dla Elzava arshva Absrac h problm of a robus conrol ssm dsgn for

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

Conventional Hot-Wire Anemometer

Conventional Hot-Wire Anemometer Convnonal Ho-Wr Anmomr cro Ho Wr Avanag much mallr prob z mm o µm br paal roluon array o h nor hghr rquncy rpon lowr co prormanc/co abrcaon roc I µm lghly op p layr 8µm havly boron op ch op layr abrcaon

More information

A MATHEMATICAL MODEL FOR NATURAL COOLING OF A CUP OF TEA

A MATHEMATICAL MODEL FOR NATURAL COOLING OF A CUP OF TEA MTHEMTICL MODEL FOR NTURL COOLING OF CUP OF TE 1 Mrs.D.Kalpana, 2 Mr.S.Dhvarajan 1 Snior Lcurr, Dparmn of Chmisry, PSB Polychnic Collg, Chnnai, India. 2 ssisan Profssor, Dparmn of Mahmaics, Dr.M.G.R Educaional

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

The Fourier Transform

The Fourier Transform /9/ Th ourr Transform Jan Baptst Josph ourr 768-83 Effcnt Data Rprsntaton Data can b rprsntd n many ways. Advantag usng an approprat rprsntaton. Eampls: osy ponts along a ln Color spac rd/grn/blu v.s.

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao

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

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g

More information

1. Inverse Matrix 4[(3 7) (02)] 1[(0 7) (3 2)] Recall that the inverse of A is equal to:

1. Inverse Matrix 4[(3 7) (02)] 1[(0 7) (3 2)] Recall that the inverse of A is equal to: Rfrncs Brnank, B. and I. Mihov (1998). Masuring monary policy, Quarrly Journal of Economics CXIII, 315-34. Blanchard, O. R. Proi (00). An mpirical characrizaion of h dynamic ffcs of changs in govrnmn spnding

More information

CONTINUOUS TIME DYNAMIC PROGRAMMING

CONTINUOUS TIME DYNAMIC PROGRAMMING Eon. 511b Sprng 1993 C. Sms I. Th Opmaon Problm CONTINUOUS TIME DYNAMIC PROGRAMMING W onsdr h problm of maxmng subj o and EU(C, ) d (1) j ^ d = (C, ) d + σ (C, ) dw () h(c, ), (3) whr () and (3) hold for

More information

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

Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling Oen Journal of ascs 04 4 355-366 Publshed Onlne Auus 04 n ces. h://www.scr.or/journal/ojs h://d.do.or/0.436/ojs.04.45035 Mure eresson Esaors Usn Mul-Aular Varables and Arbues n Two-Phase aln John Kun u

More information

Transient Analysis of Two-dimensional State M/G/1 Queueing Model with Multiple Vacations and Bernoulli Schedule

Transient Analysis of Two-dimensional State M/G/1 Queueing Model with Multiple Vacations and Bernoulli Schedule Inrnaonal Journal of Compur Applcaons (975 8887) Volum 4 No.3, Fbruary 22 Transn Analyss of Two-dmnsonal Sa M/G/ Quung Modl wh Mulpl Vacaons and Brnoull Schdul Indra Assoca rofssor Dparmn of Sascs and

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

GPC From PeakSimple Data Acquisition

GPC From PeakSimple Data Acquisition GPC From PakSmpl Data Acquston Introducton Th follong s an outln of ho PakSmpl data acquston softar/hardar can b usd to acqur and analyz (n conjuncton th an approprat spradsht) gl prmaton chromatography

More information

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation.

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation. MAT 444 H Barclo Spring 004 Homwork 6 Solutions Sction 6 Lt H b a subgroup of a group G Thn H oprats on G by lft multiplication Dscrib th orbits for this opration Th orbits of G ar th right costs of H

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

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Gaussian Random Process and Its Application for Detecting the Ionospheric Disturbances Using GPS

Gaussian Random Process and Its Application for Detecting the Ionospheric Disturbances Using GPS Journal of Global Posonng Sysms (005) Vol. 4, No. 1-: 76-81 Gaussan Random Procss and Is Applcaon for Dcng h Ionosphrc Dsurbancs Usng GPS H.. Zhang 1,, J. Wang 3, W. Y. Zhu 1, C. Huang 1 (1) Shangha Asronomcal

More information

Problem 1: Consider the following stationary data generation process for a random variable y t. e t ~ N(0,1) i.i.d.

Problem 1: Consider the following stationary data generation process for a random variable y t. e t ~ N(0,1) i.i.d. A/CN C m Sr Anal Profor Òcar Jordà Wnr conomc.c. Dav POBLM S SOLIONS Par I Analcal Quon Problm : Condr h followng aonar daa gnraon proc for a random varabl - N..d. wh < and N -. a Oban h populaon man varanc

More information

10.5 Linear Viscoelasticity and the Laplace Transform

10.5 Linear Viscoelasticity and the Laplace Transform Scn.5.5 Lnar Vclacy and h Lalac ranfrm h Lalac ranfrm vry uful n cnrucng and analyng lnar vclac mdl..5. h Lalac ranfrm h frmula fr h Lalac ranfrm f h drvav f a funcn : L f f L f f f f f c..5. whr h ranfrm

More information

EXERCISE - 01 CHECK YOUR GRASP

EXERCISE - 01 CHECK YOUR GRASP DIFFERENTIAL EQUATION EXERCISE - CHECK YOUR GRASP 7. m hn D() m m, D () m m. hn givn D () m m D D D + m m m m m m + m m m m + ( m ) (m ) (m ) (m + ) m,, Hnc numbr of valus of mn will b. n ( ) + c sinc

More information