Variance Estimation After Imputation
|
|
- Abigayle Owen
- 6 years ago
- Views:
Transcription
1 Suve Metodolog, Jue Vol 7, o, pp Sttstcs Cd, Ctlogue o 00 Vce Estmto Afte mputto Je Kwg Km Abstct mputto s commol used to compeste fo tem oespose Vce estmto fte mputto s geeted cosdeble dscusso d sevel vce estmtos ve bee poposed We popose vce estmto bsed o pseudo dt set used ol fo vce estmto Stdd complete dt vce estmtos ppled to te pseudo dt set led to cosstet estmtos fo le estmtos ude vous mputto metods, cludg wtout eplcemet ot deck mputto d wt eplcemet ot deck mputto Te smptotc equvlece of te poposed metod d te dusted ckkfe metod of Ro d Stte (995) s llustted Te poposed metod s dectl pplcble to vce estmto fo two pse smplg Ke Wods: Two pse smplg; tem oespose; Detemstc mputto; Rdom mputto toducto mputto, setg vlues fo mssg tems, s commol used fo dlg mssg suve dt A dvtge of mputto s ts coveece Tt s, we c ppl stdd complete dt pogms fo computg pot estmtes to te mputed dt set Rub (996), F (996), d Ro (996) evewed vous ssues o mputto All mputto metods use some tpe of model Afte desgtg model, we c use ete detemstc mputto o dom mputto bsed o te model Ude dom mputto, mssg vlues e mputed b te use of some fom of pobblt smplg We cll ts ddtol dom mecsm te mputto mecsm O te ote d, detemstc mputto does ot toduce ddtol dom mecsm We te set of espodets s vewed s dom smple fom te ogl smple, te selecto mecsm of te espodets s clled te espose mecsm Te espose mecsm s ofte egded s te secod pse of smplg See Sädl d Swesso (987) fo detls Wt sutble mputto model d metod, te bs due to oespose c be getl educed eltve to usg ol te obseved dt Howeve, t s well kow tt vce estmto wc uses te mputed dt s f t wee obseved dt s cosstet Vous metods ve bee poposed fo vce estmto fte mputto Rub d Sceke (986) d Rub (987) dvocte multple mputto Multple mputto cetes multple dt sets d clcultes te complete dt sttstcs fo ec mputed dt set Te vce estmto s clculted b combg two tems, te wtdtset vce tem d te betwee dtset vce tem Multple mputto pples stdd vce estmtos to ec dt set to compute wt dtset vce tems d pples te stdd pot estmtos to compute betwee mputed dtset vce tem Ts metod eques te mputto metod to be pope Tt s, te mputto sould stsf codtos 3 Rub (987, pges 8 9) Tese codtos e ot lws es to ceve (Fo emple, see F 99) Eve te multple mputto metods descbed Scfe (997) e ot sow to be pope te sese of Rub As oted b Ro (996), some commol used mputto metods, cludg ot deck mputto d egesso mputto, e ot pope Ro d So (99) d Ro d Stte (995) poposed dusted ckkfe vce estmto Te suggested pocedue s pplcble to umbe of mputto metods d smple desgs Te ctul clculto usg stdd complete dt softwe s ot es becuse specl computtos e pefomed to dust te mputed vlues fo ec pseudo eplcte Also, Sädl (99) poposed vce estmto metod tt eplctl uses te model cosdeed fo mputto Essetll, Rub s metod geetes sevel pseudo dt sets fo vce estmto d pples te stdd vce estmtos to ec dt set to compute te wtdtset vce tems, wle Ro s metod d Sädl s metod ppl specl vce estmto to te mputed dt set ts ppe, metod to cete sgle pseudo dt set fo vce estmto s poposed secto, te ew metod s toduced two pse smplg set up secto 3, we llustte etesos of te suggested metod to te dom mputto metod secto 4, we eted te suggested metod to comple smplg desgs secto 5, compsos e mde wt te dusted ckkfe vce estmto secto 6, lmted smulto stud s peseted Some cocludg emks e mde secto 7 Outles of some poofs e gve te pped Je Kwg Km, Westt, 650 Resec Boulevd, Rockvlle, Mld, 0850, USA
2 76 Km: Vce Estmto Afte mputto A Vce Estmto Metod We outle vce estmto pocedue pplcble fo two pse smples d fo mputed smples Te pocedue eques septe dt set fo vce estmto ddto to te tbulto dt set To toduce te pocedue d to llustte te cocepts, cosde two pse smple et te secod pse be smple dom smple of sze selected fom te fst pse, wc s smple dom smple of sze selected fom fte populto et te egesso estmto of te me of cctestc be wee (, ) = (, ), = =, µ = + ( ) β, () = = = β = ( ) ( ) ( ) d te secod pse uts e deed fom oe to t s well kow (eg, Coc 977, equto 5) tt te vce of te egesso estmto s, ppomtel, V{ µ } = [ ρ + ( ρ )] σ, () wee ρ s te populto coelto betwee d d σ, s te populto vce of A estmto of te vce s, b clsscl egesso teo, = V { µ } = ( ) ( ) ( ) ( ) = + (3) wee ( ) = + β fo =,,,, d = = Obseve tt s ltetve w of wtg µ () et d Te, / = [ ( ) ( ) ] (4) c,,,, = = c ( ), =,,, = (5) V { µ } = ( ) ( ) (6) wee s te me of te, s well s te me of te, becuse te sum of s zeo Equto (6) s te opetol fom of te suggested estmto Te vce estmto dt set cots te pseudo obsevto To te etet tt te model fo mputto mtces tt of two pse smplg, equto (6) s pplcble to mputed dt set Fo emple, f we ssume tt mssg dt e mssg t dom d use egesso to mpute te mssg vlue wt, te equto (6) s mmedtel pplcble Of couse, egesso mputto o two pse smplg c use vecto 3 Etesos to Rdom mputto A modete eteso of te metod descbed secto ebles us to estmte te vce of smple me usg dom mputto fct, ltetve ppoces e possble As oe ppoc, ssume tt te mputto model s te egesso model = β + e (7) wee te fst elemet of eve s equl to d te e e ucoelted (0, σ e ) dom vbles Assume te model s estmted d tt te mputed vlues e = + e, = +, +,, (8) wee = β wt β = ( = ) = d e s cose t dom fom te set e = { e = ; =,,, } Te estmto of te me of s µ = (9) = wee = f =,,, f te e e cose wt eplcemet wt equl pobblt fom te set e, te te vce µ s, ppomtel, V{ µ } = [ R + ( + m) ( R )] σ, (0) wee m = d R s te squed multple coelto coeffcet betwee d Te cese vce due to usg dom mputto wt e, te t usg e 0, s m( R ) σ Teefoe, estmto of te vce of te mputed smple me s gve b (6) wee te c of (4) s / c = [ ( ) ( + m)( p ) ], () d p s te dmeso of β We ve = V { µ } = ( ) ( ) = + ( + m)( p) ( ) () Sttstcs Cd, Ctlogue o 00
3 Suve Metodolog, Jue wee = = Te estmto of te vce usg c of equto () s estmto of te ucodtol vce, te vege ove ll possble mputed smple Devtos of (0) d () e gve Apped A To cosde ltetve vce estmto ppoc, we ssume tt dom selecto pocedue s used fo mputto but plce o estcto o te pocedue, ote t tt te pobbltes of selecto e vesel popotol to te pobblt tt te vlue espods ddto, we ecod te umbe of tmes e vlue s used s doo te mputto et wt,,, = = c ( ) =,,, (3) / = [ ( ) ( ) ] ( + ) (4) c p d wee d s te umbe of tmes e s used s doo Te / tem [ ( ) ( p ) ] s used to dust fo te effect of estmtg p egesso pmetes Te, te vce estmto (6) c be wtte s = V { µ } = ( ) ( ) = + ( p) ( + d ) ( ) (5) f te mputto metod s smple dom smplg wt eplcemet, te, codtol o te smple d te espodets, m E{( + d ) } = + (6) wee te otto s used ee to deote te epectto wt espect to te mputto mecsm geeted b dom mputto Te eqult (6) estblses te equvlece of () to (5) ude wt eplcemet selecto t s sow Apped B tt V { µ } (5) s lso vld estmto we doos e selected wtout eplcemet Sce te poposed vce estmto metod s te codtol vce gve te elzed mputed smple, t s wde pplcblt 4 Comple Smplg Desgs 4 Detemstc mputto Te suggested metod s pplcble to comple smplg desgs s well s to smple dom smplg Assume tt te full smple estmto of te me of c be wtte s = = w, wee w s te smplg wegt of ut te smple Assume tt w = = f te fst elemets e obseved d te emg elemets e mssg, te te estmto of te me of ude egesso mputto s wee = w + w = = + (7) = β, w w = = β = Hee w s te smplg wegt of ut te secodpse smple d s defed b P ( s te secod pse smple s w = te fst pse smple) w Also, w = = f we ssume tt te secod pse smple s dom smple of sze fom te fst pse smple, te w = w Ude cet codtos we c wte te estmto (7) s = w (8) = Te epesetto (8) olds f ( w ) w s te colum spce of te mt X = (,, ) becuse te we ve * = w ( ) = 0 fom = w ( ) = 0 We ssume sequece of smples d fte popultos suc s tt descbed Fulle (998) Defe = = w d (, ) = w = (, ) We lso dopt te sme ssumptos s Fulle (998) Tt s d E(,, ) = ( µ, µ, µ ), (9) V O (0) {( β β ),,, } = ( ), wee ( µ, µ ) = = (, ) d = Fo =,,,, defe β = ( ) = f ut espods we smpled = 0 otewse, d = (,,, ) Te eteded defto of s dscussed b F (99) d used So d Steel (999) ow, let wee l = = w () w w ( ) () = + Sttstcs Cd, Ctlogue o 00
4 78 Km: Vce Estmto Afte mputto wt = β Te, we ve = l + ( )( β β ) B (9) d (0), we ve = l + Op ( ) d V ( Y ) = V ( Y ) + o( ) ow, l V ( Y ) = V[ E( Y )] + E[ V ( Y ) ] (3) l l l Te fst tem o te gt sde of (3) s 0 becuse E( l Y ) = 0 ude model (7) To estmte te secod tem (3), ote tt codtol o, l s le estmto Hece, te stdd vce estmto metod ppled to te pseudo dt set Y { ; =,,, } wll ubsedl estmte te vce of l = w = Sce te set Y s ot obsevble, we c use te set Y { ; =,,, }, wee = + w w ( ) (4) to get cosstet vce estmto To llustte tt te set Y c be used to ppomte te vce estmto, ssume tt te full smple vce estmto of c be wtte s V = c ( ) = ( ) t wee s te umbe of eplctos, c s te ( ) ( ) eplcto fcto, d = = w M s te t epl ( ) cte of Te tem M s te eplcto multple t ppled to te wegt of ut t te eplcto Fo emple, ude smple dom smplg, te ckkfe multple s ( ) ( ) f M = 0 f = Assume tt te eplcte vce estmto ppled to te set Y to get ( ) = V = c ( ) *( ) ( ) wee = w M = wt beg defed (4) Te, we ve = + ( + )( β β ) (5) ( ) ( ) ( ) ( ) l l wee ( ) ( ) ( ) wm w = w = (, ) (, ) t s sow Apped C tt = ( ) ( l l ) + p ( ) = V V c o (6) Teefoe, te stdd ckkfe vce estmto ppled to te pseudo dt set Y c be used to ppomte te stdd ckkfe vce estmto ppled to te pseudo dt set Y s 4 Rdom mputto Te gumets fo vce estmto wt dom mputto e qute sml to tose fo detemstc mputto descbed te pevous subsecto Fst, defe te mputto dcto fucto f ut s used s doo fo ut d = 0 otewse (7) Te, te estmto of te me of usg dom mputto s wee d = w (8) = = + ( + d ) ( ) (9) = ( ) = d d w w (30) f te ogl smple wegts e te sme, te d s te umbe of tmes tt ut s used s doo We ssume tt E[ ( + d ) F ] = (3) wee F = {(,, ); =,,, } Te epectto (3) s wt espect to te ot dstbuto of te espose mecsm d te mputto mecsm Te, we ve E( F ) = f we ssume equl espose pobblt, te, b (3), te pobblt of selecto of doos sould be popotol to te wegts Ts s te Ro d So (99) setup fo dom mputto ow, let = w [ + ( + d )( )] (3) l = wee = β Te, we lso ve = l + ( d )( β β ) wee d = = w ( + d ) B te ssumpto (3), we ve E( d F ) = 0 Ude mld / codtos, d = Op ( ) d = l + Op ( ) ow, V ( Y ) = V[ E( Y, d)] + E[ V ( Y, d )] wee d = ( d, d,, d ) Codtol o d d, te estmto l s le estmto Hece, te pseudo dt = + ( + d )( ) (33) c be used to estmte te vce of Sttstcs Cd, Ctlogue o 00
5 Suve Metodolog, Jue Compsos wt Adusted Jckkfe Metod Ro d Stte (995) poposed dusted ckkfe vce estmto fo te to mputto poblem Ude te setup descbed secto 4, te to mputed estmto of µ s µ = w [ + ( ) ] = wt = R d R = ( = w ) = w Te Ro d Stte (995) vce estmto s V ( ) = c µ µ = ( ), (34) wee te dusted ckkfe eplcte t te s wee ( ) ( ) ( ) ( ) w M = t eplcto µ = (35) ( ) f R = = R f = 0 (36) ( ) ( ) ( ) wt R = ( = w M ) = w M Te dusted vlues (36) te Ro d Stte (995) metod c lso be egded s pseudo dt fo vce estmto ote tt te clculto of te pseudo dt (36) eques ( ) eclculto of R fo ec wt = We modf te clculto of te pseudo vlues (5) to f = 0 = + c ( ) f =, (37) wee = = w, = = w d c = Te tem ( / ) s seted to mpove te codtol popetes of V J gve te fst pse smple Te esultg vce estmto s ppomtel equvlet to te dusted ckkfe vce estmto (34) To see ts, ote tt te dusted vlues (35) c be wtte te fom ( ) wm ( ) ( ) ( ) = ( ) S wm Z ( ) = ( ) T wm = µ = = :, wee A = : B deotes tt we defe B to be A Also, defe Z = = w, S = = w, d T = = w Te b te fst ode Tlo epso, ( ) ( ) S S ( ) S Z = Z + ( Z Z ) ( ) T T T ( ) Z ( ) ( ) ( ZS + S S T T ) T T ( ) S Z ( ) ( ) S = Z + S T T T T ote tt te gt sde of (38) s ectl equl to = ( ) S Z S w M + T T T (38) Tus, te pseudo dt fo vce estmto c be wtte s S Z S = +, T T T wc educes to (37) Hece, te poposed metod s ectl fst ode Tlo lezto of te Ro d Stte metod te cse of to mputto Teefoe, we c epect ou poposed metod to ve te sme smptotc popetes s te Ro d Stte metod up to te ode of Te vce estmto metod usg te pseudo dt set clculted b (37) s es to mplemet becuse we c dectl use estg softwe, wc s moe dffcult wt te Ro d So (99) o Ro d Stte (995) metod Futemoe, f we clculte te pseudo dt b (3), te te dt set woks fo wtout eplcemet ot deck mputto s well s fo wt eplcemet ot deck mputto 6 A Smulto Stud Te pecedg teo ws tested smulto stud usg tfcl, fte populto, fom wc epeted smples wee dw Te populto s = 3 stt, clustes sttum, d 0 ultmte uts ec cluste Te vlues of te populto pmetes wee cose to coespod to el popultos ecouteed te US tol Assessmet of Eductol Pogess Stud (Hse d Teppg 985) d e lsted Tble Te fte populto uts e wee d = + e, d µ σ = = ~ (, ),,,,,,,,, Sttstcs Cd, Ctlogue o 00
6 80 Km: Vce Estmto Afte mputto d ρ e ~ σ = ρ 0,,,,, 0 So, Ce d Ce (998) lso used te sme populto te smulto stud Te vlue of te t cluste coelto ρ cosdeed te smulto s ρ = 03 Smultos wt ote vlues of ρ poduced sml esults d e ot lsted ee fo bevt Tble Pmetes of te Fte Populto fo Smulto H µ σ µ σ We cosde sttfed cluste smplg desg, wee = clustes e selected wt eplcemet fom sttum wt equl pobblt d ll of te ultmte uts te selected clustes e te smple Te smplg fcto s 64% Fo ec smpled ut, espose dcto vble s geeted fom d ~ Beoull ( p ), d tt s depedet of Te vlue of p cosdeed te smulto e p = 09, 08, 07, 06, d 05 A set of 5,000 smples wee selected usg te sme smplg desg ec of te selected smples, tee mputto metods e cosdeed; [M] Wt eplcemet wegted ot deck mputto cosdeed b Ro d So (99), wee mssg vlue s mputed b vlue doml selected fom te espodets wt eplcemet wt pobblt popotol to te suve wegts [M] Wtout eplcemet wegted ot deck mputto, wc s te sme s [M] epect tt te selecto ws pefomed usg wtout eplcemet smple Te wtout eplcemet selecto of doos s ced out sstemtcll usg te metod descbed b Hse, Huwtz, d Mdow (953, pge 343) fom te espodets soted b dom ode [M3] Ovell me mputto, wee te wegted me of te espodets te smple s mputed Hece, ll te mputto metods use sgle mputto cell tt collpses ll te stt ec mputed dt set we computed tee vce estmtos V, ve vce estmto tetg te mputed dt s f t wee obseved dt, V, te dusted ckkfe vce estmto of Ro d So (99) fo [M] d [M] d of Ro d Stte (995) fo [M3], d V, te ckkfe vce estmto bsed o te pseudo dt Te pseudo dt set s costucted b (9) fo [M] d [M] d b (4) fo [M3] Te complete smple vce estmto used stdd ckkfe fo sttfed cluste smplg, wc cluste s deleted fo ec eplcto ote tt te stdd ckkfe s cosstet estmto of te vce ude te model wt ozeo tcluste coelto Tus, te stdd ckkfe metod bsed o te pseudo dt c be pplcble to te dt set cosdeed Te pot estmtos of te populto me e ubsed ude te tee dffeet mputto scemes d e ot lsted ee Tble pesets te eltve bs of te tee vce estmtos, te stdd eo of te eltve bs of te vce estmtos, d te smple coelto coeffcet betwee te Ro s dusted ckkfe vce estmto d te ew vce estmto bsed o te 5,000 smples Te eltve bs of V s estmto of te vce of s clculted b [V ( )] [ ( B EB V ) V B ( )], wee te subscpt B deotes te dstbuto geeted b te Mote Clo smulto Te coelto coeffcets of te two vce estmtos e computed to gve mesue te eltve let bevo of te two vce estmtos Tble Reltve Bs of te Vce Estmto, Stdd Eo of te Reltve Bs, d Smple Coelto Coeffcet Betwee te Ro s Vce Estmto d te ew Vce Estmto Bsed o 5,000 Smples Respose mputto Rte (p) Rel Bs 00 (SE 00) Co Metod ve Ro ew Coeff M 740 (0) 6 (03) 70 (04) 0967 M 750 (00) 4 (0) 08 (03) 0974 M3 803 (03) 6 (05) 5 (04) 000 M 3445 (0) 065 (03) 049 (05) 0939 M 389 (0) 49 (04) 09 (03) 0947 M (0) 59 (03) 59 (03) 000 M 4896 (0) 0 (99) 04 (04) 09 M 4476 (0) 53 (05) 076 (05) 090 M3 50(0) 53 (05) 5 (04) 000 M 5980 (0) 58 (05) 7 (06) 089 M 5486 (03) 70 (07) 075 (07) 0899 M3 64 (00) 035 (04) 035 (0) 000 M 6975 (99) 084 (03) (03) 0873 M 5990 (0) 507 (07) 7 (06) 087 M (97) 99 (00) 98 (00) 000 Sttstcs Cd, Ctlogue o 00
7 Suve Metodolog, Jue 00 8 Tble suppots ou teo te followg ws As s well kow, te ve vce estmto seousl udeestmtes te tue vce Te dusted ckkfe vce estmto pefoms well fo [M] d [M3], but ot fo [M] Te teo fo te dusted ckkfe metod ssumes tt ot deck mputtos e doe usg te wt eplcemet selecto wc s ot used [M] As te espose te deceses Tble, te eltve bs of te dusted ckkfe becomes lge Te ew metod bsed o te pseudo dt pefoms well eve fo te wtout eplcemet mputto [M] As ws dscussed t te ed of secto 3, sgle fomul (9) c be used s te pseudo dt fo lge clss of mputto metods 3 As s obseved te coelto coeffcets, te bevos of te dusted ckkfe vce estmto d te poposed vce estmto e ve sml fo me mputto [M3] Ts s becuse te two vce estmtos e smptotcll equvlet, s dscussed secto 5 7 Cocludg Remks We ve descbed metods of mkg pseudo dt to be used fo vce estmto Geell spekg, te pseudo dt c be descbed s,,, = = c g ( ) =,,,, (39) wee s te pedcted vlue of ude te model used fo mputto f c g =, te te vce estmto tets te mputed vlues s obsevtos A sutble coce of c g > leds to cosstet vce estmto f te mputto metod s detemstc d te espodets e egded s dom smple fom te ogl smple, te c = > Fo two pse smplg wt comple desg, c, = w w wee w s te smplg wegt of te ut fo te fst pse smple d w s te smplg wegt of te ut fo te secod pse smple Te g (39) s te dustemet mde to mpove te codtol popetes gve te ul vble Fo to mputto, g = ( ) wee = = w d = = w Fo egesso mputto wt scl, = + k k k = g ( ) w ( ) ( ) ete cse, we ve = w g = Wle ts ppe ws ude evew, So d Steel (999) lso povded sml metods te cse of detemstc mputto Ou metod s moe geel te sese tt we lso cosdeed dom mputto d toduced c tem to mpove fte smple popetes Ackowledgemets Te uto tks s tess dvse We A Fulle fo vluble dscussos Te uto lso tks Pmel Abbtt, F J Bedt, ou Rzzo, Rcd Vllt, d te efeees fo elpful commets, wc getl mpoved te ppe Most of ts wok ws doe wle te uto ws gdute studet t ow Stte Uvest d ws fuded pt b coopetve geemet 68 3A75 43 betwee te USDA tul Resouces Cosevto Sevce d ow Stte Uvest d b Coopetve Ageemet 43 3AEU betwee ow Stte Uvest, te tol Agcultul Sttstcs Sevce d te US Bueu of Cesus Apped A Poof of Equto (0) d () Te estmto µ (9) c be wtte s µ = + ( + d ) e = = (A) wee d s te umbe of tmes tt ut s used s doo Ude te equl pobblt d wt eplcemet mputto mecsm, we ve d E ( d ) = m ( ) f = m Cov ( d, d ) = m f wee te subscpt deotes te vto due to te mputto mecsm t follows tt E ( µ ) = = d V ( µ ) = m e Hece, = V ( µ ) = V + E m e = = (A) ow, b sml gumet sml to te oe ledg to (), we ve Sttstcs Cd, Ctlogue o 00
8 8 Km: Vce Estmto Afte mputto V = [ R + ( R )] σ = (A3) Sce = ( ) β + o p (), we ppl clsscl egesso teo to get d E ( p) e = ( R ) σ, = E ( ) ( ) = R σ = (A4) (A5) Teefoe, (0) s poved d te estmto () s cosstet fo te vce (0) B Vldt of (5) Ude te Wtout Replcemet mputto Mecsm We ssume tt m = k + t wee k d t e oegtve teges d t < et te estmto of te me of ve te fom (A) et te mputto be pefomed suc tt t of te espodets e used k + tmes fo mputto d t uts e used k tmes fo mputto Te t of te espodets tt e used k + tmes e cose b smple dom smplg wtout eplcemet Te, d E ( d ) = k + t = m ( ) f = t t Cov ( d, d ) = t f So, b sml gumets s te poof of (A), we ve V ( µ ) = V ( ) + E t e = Hece, usg (A3) d (A4), we ve (B) V{ µ } = [ R + ( + t)( R )] σ (B) ow, codtol o te elzed smple d te espodets, we ve t t E {( + d ) } = + ( { µ }) = ( ) ( ) = E V = + [ + t( t)]( p) ( ) Teefoe, usg (A4) d (A5), we ve te ppomte ubsedess of te V { µ } ude te wtout eplcemet mputto mecsm C Poof of Equto (6) ( ) ( ) ( ) Fst, defe R ( = )( β β ) d R = ( )( β β ) Fom te eqult (5), ( ) = ( ) = + + = V c A B C ( ) wee A c ( ( ) = = l l ), B = = c ( R R ), ( ) ( ) d C = = c ( l l )( R R ) Hece, b te ssumpto (0), (6) follows becuse A = Op ( ), B = op ( ), d C = op ( ) Te lst popet comes fom te Cuc Scwtz eqult, C A B Refeeces Coc, WG (977) Smplg Tecques ew Yok: Jo Wle & Sos, c F, RE (99) A desg bsed pespectve o mssg dt vce Poceedgs of te Bueu of te Cesus Aul Resec cofeece, F, RE (99) We e feeces fom multple mputto vld? Poceedgs of te Secto o Suve Resec Metods, Amec Sttstcl Assocto, 7 3 F, RE (996) Altetve pdgms fo te lss of mputed suve dt Joul of te Amec Sttstcl Assocto, 9, Fulle, WA (998) Replcto vce estmto fo two pse smples Sttstc Sc, 8, Hse, M, Huwtz, W d Mdows, WG (953) Smple Suve Metods d Teo, Vol, ew Yok: Jo Wle & Sos, c Hse, M, d Teppg, BJ (985) Estmto fo Vce AEP Upublsed memodum, Westt, Wsgto, DC Ro, JK (996) O vce estmto wt mputed suve dt Joul of te Amec Sttstcl Assocto, 9, Ro, JK, d So, J (99) Jckkfe vce estmto wt suve dt ude ot deck mputto Bometk, 79, 8 8 so tt V { µ } (5) stsfes Sttstcs Cd, Ctlogue o 00
9 Suve Metodolog, Jue Ro, JK, d Stte, RR (995) Vce estmto ude twopse smplg wt pplcto to mputto fo mssg dt Bometk, 8, Rub, DB (987) Multple mputto fo oespose Suves ew Yok: Jo Wle & Sos, c Rub, DB (996) Multple mputto fte 8+ es Joul of te Amec Sttstcl Assocto, 9, Rub, DB, d Sceke, (986) Multple mputto fo tevl estmto fom smple dom smples wt goble oespose Joul of te Amec Sttstcl Assocto, 8, Sädl, C E (99) Metods fo estmtg te pecso we mputto s bee used Suve Metodolog, 8, 4 5 Sädl, C E, d Swesso, B (987) A geel vew of estmto fo two pses of selecto wt pplctos to twopse smplg d oespose tetol Sttstcl Revew, 55, Scfe, J (997) Alss of complete Multvte Dt Cpm & Hll So, J, Ce, Y d Ce, Y (998) Blced epeted eplcto fo sttfed multstge suve dt ude mputto Joul of te Amec Sttstcl Assocto, 93, So, J, d Steel, P (999) Vce estmto fo suve dt wt composte mputto d oeglgble smplg fcto Joul of te Amec Sttstcl Assocto, 94, Sttstcs Cd, Ctlogue o 00
Chapter Linear Regression
Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use
More informationChapter 17. Least Square Regression
The Islmc Uvest of Gz Fcult of Egeeg Cvl Egeeg Deptmet Numecl Alss ECIV 336 Chpte 7 Lest que Regesso Assocte Pof. Mze Abultef Cvl Egeeg Deptmet, The Islmc Uvest of Gz Pt 5 - CURVE FITTING Descbes techques
More informationDescribes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates.
CURVE FITTING Descbes techques to ft cuves (cuve fttg) to dscete dt to obt temedte estmtes. Thee e two geel ppoches fo cuve fttg: Regesso: Dt ehbt sgfct degee of sctte. The stteg s to deve sgle cuve tht
More information8. SIMPLE LINEAR REGRESSION. Stupid is forever, ignorance can be fixed.
CIVL 33 Appomto d Ucett J.W. Hule, R.W. Mee 8. IMPLE LINEAR REGREION tupd s foeve, goce c be fed. Do Wood uppose we e gve set of obsevtos (, ) tht we beleve to be elted s f(): Lookg t the plot t ppes tht
More informationGCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS
GCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS FORMULA BOOKLET Fom Septembe 07 Issued 07 Mesuto Pue Mthemtcs Sufce e of sphee = 4 Ae of cuved sufce of coe = slt heght Athmetc Sees S l d
More informationSome Unbiased Classes of Estimators of Finite Population Mean
Itertol Jourl O Mtemtcs Ad ttstcs Iveto (IJMI) E-IN: 3 4767 P-IN: 3-4759 Www.Ijms.Org Volume Issue 09 etember. 04 PP-3-37 ome Ubsed lsses o Estmtors o Fte Poulto Me Prvee Kumr Msr d s Bus. Dertmet o ttstcs,
More informationProfessor Wei Zhu. 1. Sampling from the Normal Population
AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple
More informationSOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE
D I D A C T I C S O F A T H E A T I C S No (4) 3 SOE REARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOIAL ASYPTOTE Tdeusz Jszk Abstct I the techg o clculus, we cosde hozotl d slt symptote I ths ppe the
More informationAS and A Level Further Mathematics B (MEI)
fod Cmbdge d RSA *3369600* AS d A evel Futhe Mthemtcs B (MEI) The fomto ths booklet s fo the use of cddtes followg the Advced Subsd Futhe Mthemtcs B (MEI)(H635) o the Advced GCE Futhe Mthemtcs B (MEI)
More informationDifference Sets of Null Density Subsets of
dvces Pue Mthetcs 95-99 http://ddoog/436/p37 Pulshed Ole M (http://wwwscrpog/oul/p) Dffeece Sets of Null Dest Susets of Dwoud hd Dsted M Hosse Deptet of Mthetcs Uvest of Gul Rsht I El: hd@gulc h@googlelco
More information= y and Normed Linear Spaces
304-50 LINER SYSTEMS Lectue 8: Solutos to = ad Nomed Lea Spaces 73 Fdg N To fd N, we eed to chaacteze all solutos to = 0 Recall that ow opeatos peseve N, so that = 0 = 0 We ca solve = 0 ecusvel backwads
More informationSOLVING SYSTEMS OF EQUATIONS, DIRECT METHODS
ELM Numecl Alyss D Muhem Mecmek SOLVING SYSTEMS OF EQUATIONS DIRECT METHODS ELM Numecl Alyss Some of the cotets e dopted fom Luee V. Fusett Appled Numecl Alyss usg MATLAB. Petce Hll Ic. 999 ELM Numecl
More informationFor use in Edexcel Advanced Subsidiary GCE and Advanced GCE examinations
GCE Edecel GCE Mthemtcs Mthemtcl Fomule d Sttstcl Tles Fo use Edecel Advced Susd GCE d Advced GCE emtos Coe Mthemtcs C C4 Futhe Pue Mthemtcs FP FP Mechcs M M5 Sttstcs S S4 Fo use fom Ju 008 UA08598 TABLE
More informationGCE AS and A Level MATHEMATICS FORMULA BOOKLET. From September Issued WJEC CBAC Ltd.
GCE AS d A Level MATHEMATICS FORMULA BOOKLET Fom Septeme 07 Issued 07 Pue Mthemtcs Mesuto Suce e o sphee = 4 Ae o cuved suce o coe = heght slt Athmetc Sees S = + l = [ + d] Geometc Sees S = S = o < Summtos
More information( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi
Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)
More informationTransmuted Generalized Lindley Distribution
Itetol Joul of Memtcs Teds d Techology- olume9 Numbe Juy 06 Tsmuted Geelzed Ldley Dstbuto M. Elghy, M.Rshed d A.W.Shwk 3, Buydh colleges, Deptmet of Memtcl Sttstcs, KSA.,, 3 Isttute of Sttstcl Studes d
More informationSEPTIC B-SPLINE COLLOCATION METHOD FOR SIXTH ORDER BOUNDARY VALUE PROBLEMS
VOL. 5 NO. JULY ISSN 89-8 RN Joul of Egeeg d ppled Sceces - s Resech ulshg Netok RN. ll ghts eseved..pouls.com SETIC -SLINE COLLOCTION METHOD FOR SIXTH ORDER OUNDRY VLUE ROLEMS K.N.S. Ks Vsdhm d. Mul Ksh
More informationCURVE FITTING LEAST SQUARES METHOD
Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued
More informationXII. Addition of many identical spins
XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.
More informationRECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S
Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets
More informationUnequal probability inverse sampling
Ctlogue o. 12-001-X ISSN 1492-0921 Suvey Methodology Uequl pobblty vese smplg by Yves Tllé Relese dte: Decembe 20, 2016 How to obt moe fomto Fo fomto bout ths poduct o the wde ge of sevces d dt vlble fom
More informationInductance of Cylindrical Coil
SEBIN JOUN OF EETI ENGINEEING Vol. No. Jue 4 4-5 Iductce of ldcl ol G.. vd. Dol N. Păduu stct: Te cldcl coeless d coe cols e used stumet tsfomes d m ote electomgetc devces. I te ppe usg te septo of vles
More informationME 501A Seminar in Engineering Analysis Page 1
Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt
More information10.3 The Quadratic Formula
. Te Qudti Fomul We mentioned in te lst setion tt ompleting te sque n e used to solve ny qudti eqution. So we n use it to solve 0. We poeed s follows 0 0 Te lst line of tis we ll te qudti fomul. Te Qudti
More informationsuch that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1
Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9
More information148 CIVIL ENGINEERING
STRUTUR NYSS fluee es fo Bems d Tusses fluee le sows te vto of effet (eto, se d momet ems, foe tuss) used movg ut lod oss te stutue. fluee le s used to deteme te posto of movele set of lods tt uses te
More informationANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY)
ANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY) Floet Smdche, Ph D Aocte Pofeo Ch of Deptmet of Mth & Scece Uvety of New Mexco 2 College Rod Gllup, NM 873, USA E-ml: md@um.edu
More informationECONOMETRIC ANALYSIS ON EFFICIENCY OF ESTIMATOR ABSTRACT
ECOOMETRIC LYSIS O EFFICIECY OF ESTIMTOR M. Khohev, Lectue, Gffth Uvet, School of ccoutg d Fce, utl F. K, tt Pofeo, Mchuett Ittute of Techolog, Deptet of Mechcl Egeeg, US; cuetl t Shf Uvet, I. Houl P.
More informationOn EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx
Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.
More informationInsurance Risk EC for XL contracts with an inflation stability clause
suce Rs E fo L cotcts wth flto stlt cluse 40 th t. AT oll. Mdd Jue 9-0 opght 008 FR Belgum V "FRGlol". Aged upemposed flto / stlt cluse suce Rs olutos o-lfe Rs / tdd ppoch o-lfe Rs / tochstc ppoch goss
More informationThe formulae in this booklet have been arranged according to the unit in which they are first
Fomule Booklet Fomule Booklet The fomule ths ooklet hve ee ge ccog to the ut whch the e fst touce. Thus cte sttg ut m e eque to use the fomule tht wee touce peceg ut e.g. ctes sttg C mght e epecte to use
More informationSynthesis of Stable Takagi-Sugeno Fuzzy Systems
Sytess of Stle Tk-Sueo Fuzzy Systems ENATA PYTELKOVÁ AND PET HUŠEK Deptmet of Cotol Eee Fculty of Electcl Eee, Czec Teccl Uvesty Teccká, 66 7 P 6 CZECH EPUBLIC Astct: - Te ppe dels t te polem of sytess
More informationChapter #2 EEE State Space Analysis and Controller Design
Chpte EEE8- Chpte # EEE8- Stte Spce Al d Cotolle Deg Itodcto to tte pce Obevblt/Cotollblt Modle ede: D D Go - d.go@cl.c.k /4 Chpte EEE8-. Itodcto Ae tht we hve th ode te: f, ', '',.... Ve dffclt to td
More informationRandom variables and sampling theory
Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred
More informationPROGRESSION AND SERIES
INTRODUCTION PROGRESSION AND SERIES A gemet of umbes {,,,,, } ccodig to some well defied ule o set of ules is clled sequece Moe pecisely, we my defie sequece s fuctio whose domi is some subset of set of
More informationChapter 2 Intro to Math Techniques for Quantum Mechanics
Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...
More informationExponential Generating Functions - J. T. Butler
Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle
More informationSpectral Continuity: (p, r) - Α P And (p, k) - Q
IOSR Joul of Mthemtcs (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X Volume 11, Issue 1 Ve 1 (J - Feb 215), PP 13-18 wwwosjoulsog Spectl Cotuty: (p, ) - Α P Ad (p, k) - Q D Sethl Kum 1 d P Mhesw Nk 2 1
More informationThe formulae in this booklet have been arranged according to the unit in which they are first
Fomule Booklet Fomule Booklet The fomule ths ooklet hve ee ge og to the ut whh the e fst toue. Thus te sttg ut m e eque to use the fomule tht wee toue peeg ut e.g. tes sttg C mght e epete to use fomule
More informationGauss-Quadrature for Fourier-Coefficients
Copote Techology Guss-Qudtue o Foue-Coecets A. Glg. Hezog. Pth P. Retop d U. Weve Cotet: The tusve method The o-tusve method Adptve Guss qudtue Embeddg to optmzto Applcto u ü tee Gebuch / Copyght Semes
More information6.6 The Marquardt Algorithm
6.6 The Mqudt Algothm lmttons of the gdent nd Tylo expnson methods ecstng the Tylo expnson n tems of ch-sque devtves ecstng the gdent sech nto n tetve mtx fomlsm Mqudt's lgothm utomtclly combnes the gdent
More informationFairing of Parametric Quintic Splines
ISSN 46-69 Eglad UK Joual of Ifomato ad omputg Scece Vol No 6 pp -8 Fag of Paametc Qutc Sples Yuau Wag Shagha Isttute of Spots Shagha 48 ha School of Mathematcal Scece Fuda Uvesty Shagha 4 ha { P t )}
More informationArea and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]
Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles
More informationOptimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek
Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt
More informationMTH 146 Class 7 Notes
7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg
More information4. Runge-Kutta Formula For Differential Equations
NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More informationMinimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses
Mmzg sphecal abeatos Explotg the exstece of cojugate pots sphecal leses Let s ecall that whe usg asphecal leses, abeato fee magg occus oly fo a couple of, so called, cojugate pots ( ad the fgue below)
More informationMathematical Statistics
7 75 Ode Sttistics The ode sttistics e the items o the dom smple ed o odeed i mitude om the smllest to the lest Recetl the impotce o ode sttistics hs icesed owi to the moe equet use o opmetic ieeces d
More informationLecture 5 Single factor design and analysis
Lectue 5 Sngle fcto desgn nd nlss Completel ndomzed desgn (CRD Completel ndomzed desgn In the desgn of expements, completel ndomzed desgns e fo studng the effects of one pm fcto wthout the need to tke
More informationChapter Simpson s 1/3 Rule of Integration. ( x)
Cpter 7. Smpso s / Rule o Itegrto Ater redg ts pter, you sould e le to. derve te ormul or Smpso s / rule o tegrto,. use Smpso s / rule t to solve tegrls,. develop te ormul or multple-segmet Smpso s / rule
More informationNumerical Methods for Eng [ENGR 391] [Lyes KADEM 2007] Direct Method; Newton s Divided Difference; Lagrangian Interpolation; Spline Interpolation.
Nuecl Methods o Eg [ENGR 39 [Les KADEM 7 CHAPTER V Itepolto d Regesso Topcs Itepolto Regesso Dect Method; Newto s Dvded Deece; Lgg Itepolto; ple Itepolto Le d o-le Wht s tepolto? A ucto s ote gve ol t
More informationare positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.
Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called
More informationOn the Trivariate Polynomial Interpolation
WSEAS RANSACIONS o MAHEMAICS Sle Sf O the vte Polol Itepolto SULEYMAN SAFAK Dvso of Mthetcs Fclt of Eee Do Elül Uvest 56 ıtepe c İ URKEY. sle.sf@de.ed.t Abstct: hs ppe s coceed wth the fole fo copt the
More informationThe z-transform. LTI System description. Prof. Siripong Potisuk
The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put
More information4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula
NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul
More informationSt John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION
St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of
More informationPubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS
PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be
More informationThe Shape of the Pair Distribution Function.
The Shpe of the P Dstbuton Functon. Vlentn Levshov nd.f. Thope Deptment of Phscs & stonom nd Cente fo Fundmentl tels Resech chgn Stte Unvest Sgnfcnt pogess n hgh-esoluton dffcton epements on powde smples
More informationCBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.
CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.
More information5 - Determinants. r r. r r. r r. r s r = + det det det
5 - Detemts Assote wth y sque mtx A thee s ume lle the etemt of A eote A o et A. Oe wy to efe the etemt, ths futo fom the set of ll mtes to the set of el umes, s y the followg thee popetes. All mtes elow
More informationThe Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof
MATEC Web of Cofeeces ICIEA 06 600 (06) DOI: 0.05/mateccof/0668600 The ea Pobablty Desty Fucto of Cotuous Radom Vaables the Real Numbe Feld ad Its Estece Poof Yya Che ad Ye Collee of Softwae, Taj Uvesty,
More informationMinimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index
Joual of Multdscplay Egeeg Scece ad Techology (JMEST) ISSN: 359-0040 Vol Issue, Febuay - 05 Mmum Hype-Wee Idex of Molecula Gaph ad Some Results o eged Related Idex We Gao School of Ifomato Scece ad Techology,
More informationStudying the Problems of Multiple Integrals with Maple Chii-Huei Yu
Itetol Joul of Resech (IJR) e-issn: 2348-6848, - ISSN: 2348-795X Volume 3, Issue 5, Mch 26 Avlble t htt://tetoljoulofesechog Studyg the Poblems of Multle Itegls wth Mle Ch-Hue Yu Detmet of Ifomto Techology,
More informationSuper-Mixed Multiple Attribute Group Decision Making Method Based on Hybrid Fuzzy Grey Relation Approach Degree *
Supe-Med Multple Attbute Goup Decso Mkg Method Bsed o Hybd Fuzzy Gey Relto Appoch Degee Gol K Fe Ye b Cete of Ntul Scece vesty of Sceces Pyogyg DPR Koe b School of Busess Adstto South Ch vesty of Techology
More informationLecture 10: Condensed matter systems
Lectue 0: Codesed matte systems Itoducg matte ts codesed state.! Ams: " Idstgushable patcles ad the quatum atue of matte: # Cosequeces # Revew of deal gas etopy # Femos ad Bosos " Quatum statstcs. # Occupato
More informationSOLUTIONS ( ) ( )! ( ) ( ) ( ) ( )! ( ) ( ) ( ) ( ) n r. r ( Pascal s equation ). n 1. Stepanov Dalpiaz
STAT UIU Pctice Poblems # SOLUTIONS Stepov Dlpiz The followig e umbe of pctice poblems tht my be helpful fo completig the homewo, d will liely be vey useful fo studyig fo ems...-.-.- Pove (show) tht. (
More informationIntegration by Parts for D K
Itertol OPEN ACCESS Jourl Of Moder Egeerg Reserc IJMER Itegrto y Prts for D K Itegrl T K Gr, S Ry 2 Deprtmet of Mtemtcs, Rgutpur College, Rgutpur-72333, Purul, West Begl, Id 2 Deprtmet of Mtemtcs, Ss Bv,
More informationχ be any function of X and Y then
We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,
More informationComplete Classification of BKM Lie Superalgebras Possessing Strictly Imaginary Property
Appled Mthemtcs 4: -5 DOI: 59/m4 Complete Clssfcto of BKM Le Supelges Possessg Stctly Imgy Popety N Sthumoothy K Pydhs Rmu Isttute fo Advced study Mthemtcs Uvesty of Mds Che 6 5 Id Astct I ths ppe complete
More informationDifferential Entropy 吳家麟教授
Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set
More informationCouncil for Innovative Research
Geometc-athmetc Idex ad Zageb Idces of Ceta Specal Molecula Gaphs efe X, e Gao School of Tousm ad Geogaphc Sceces, Yua Nomal Uesty Kumg 650500, Cha School of Ifomato Scece ad Techology, Yua Nomal Uesty
More informationPreliminary Examinations: Upper V Mathematics Paper 1
relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationChapter 9 Pipeline and Parallel Recursive and Adaptive Filters
Chpte 9 Ppele d Pllel Recusve d Adptve Fltes Y-Tsug Hwg VLSI DSP Y.T. Hwg 9- Itoducto Ay equed dgtl flte spectum c be eled usg FIR o IIR fltes IIR fltes eque lowe tp ode but hve potetl stblty poblem dptve
More informationLecture 3 summary. C4 Lecture 3 - Jim Libby 1
Lecue su Fes of efeece Ivce ude sfoos oo of H wve fuco: d-fucos Eple: e e - µ µ - Agul oeu s oo geeo Eule gles Geec slos cosevo lws d Noehe s heoe C4 Lecue - Lbb Fes of efeece Cosde fe of efeece O whch
More informationChapter 7 Varying Probability Sampling
Chapte 7 Vayg Pobablty Samplg The smple adom samplg scheme povdes a adom sample whee evey ut the populato has equal pobablty of selecto. Ude ceta ccumstaces, moe effcet estmatos ae obtaed by assgg uequal
More informationLinear Algebra Concepts
Ler Algebr Cocepts Nuo Vscocelos (Ke Kreutz-Delgdo) UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) = (+ )+ 5) H 2) + = + H 6) = 3) H, + = 7) ( ) = (
More informationA Level Further Mathematics A
Ofod Cmbdge d RSA Advced GCE (H45) *336873345* A evel The fomto ths booklet s fo the use of cddtes followg the Advced GCE (H45) couse. The fomule booklet wll be pted fo dstbuto wth the emto ppes. Copes
More informationMathematically, integration is just finding the area under a curve from one point to another. It is b
Numerl Metods or Eg [ENGR 9] [Lyes KADEM 7] CHAPTER VI Numerl Itegrto Tops - Rem sums - Trpezodl rule - Smpso s rule - Rrdso s etrpolto - Guss qudrture rule Mtemtlly, tegrto s just dg te re uder urve rom
More informationGeneral Method for Calculating Chemical Equilibrium Composition
AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt
More informationSection 7.2 Two-way ANOVA with random effect(s)
Secto 7. Two-wy ANOVA wth rdom effect(s) 1 1. Model wth Two Rdom ffects The fctors hgher-wy ANOVAs c g e cosdered fxed or rdom depedg o the cotext of the study. or ech fctor: Are the levels of tht fctor
More informationSummary: Binomial Expansion...! r. where
Summy: Biomil Epsio 009 M Teo www.techmejcmth-sg.wes.com ) Re-cp of Additiol Mthemtics Biomil Theoem... whee )!!(! () The fomul is ville i MF so studets do ot eed to memoise it. () The fomul pplies oly
More informationUniversity of Pavia, Pavia, Italy. North Andover MA 01845, USA
Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *
More informationAvailable online through
Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo
More informationCOMP 465: Data Mining More on PageRank
COMP 465: Dt Mnng Moe on PgeRnk Sldes Adpted Fo: www.ds.og (Mnng Mssve Dtsets) Powe Iteton: Set = 1/ 1: = 2: = Goto 1 Exple: d 1/3 1/3 5/12 9/24 6/15 = 1/3 3/6 1/3 11/24 6/15 1/3 1/6 3/12 1/6 3/15 Iteton
More informationGREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER
Joual of ppled Mathematcs ad Computatoal Mechacs 4, 3(3), 5- GREE S FUCTIO FOR HET CODUCTIO PROBLEMS I MULTI-LYERED HOLLOW CYLIDER Stasław Kukla, Uszula Sedlecka Isttute of Mathematcs, Czestochowa Uvesty
More informationParameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data
Avlble ole wwwsceceeccom Physcs Poce 0 475 480 0 Ieol Cofeece o Mecl Physcs Bomecl ee Pmee smo Hyohess es of wo Neve Boml Dsbuo Poulo wh Mss D Zhwe Zho Collee of MhemcsJl Noml UvesyS Ch zhozhwe@6com Absc
More informationChapter 3 Supplemental Text Material
S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use
More informationIFYFM002 Further Maths Appendix C Formula Booklet
Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,
More informationRegularization of the Divergent Integrals I. General Consideration
Zozuly / Electoc Joul o Bouy Eleets ol 4 No pp 49-57 6 Reulzto o the Dveet Itels I Geel Coseto Zozuly Ceto e Ivestco Cetc e Yuct AC Clle 43 No 3 Colo Chubuá e Hlo C 97 Mé Yuctá Méco E-l: zozuly@ccy Abstct
More informationA convex hull characterization
Pue d ppled Mthets Joul 4; (: 4-48 Pulshed ole My 4 (http://www.seepulshggoup.o//p do:.648/.p.4. ove hull htezto Fo Fesh Gov Qut Deptet DISG Uvesty of Se Itly El ddess: fesh@us.t (F. Fesh qut@us.t (G.
More informationVECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles.
Seeth Edto CHPTER 4 VECTOR MECHNICS FOR ENINEERS: DYNMICS Fedad P. ee E. Russell Johsto, J. Systems of Patcles Lectue Notes: J. Walt Ole Texas Tech Uesty 003 The Mcaw-Hll Compaes, Ic. ll ghts eseed. Seeth
More informationChapter Gauss-Seidel Method
Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos
More informationˆ SSE SSE q SST R SST R q R R q R R q
Bll Evas Spg 06 Sggested Aswes, Poblem Set 5 ECON 3033. a) The R meases the facto of the vaato Y eplaed by the model. I ths case, R =SSM/SST. Yo ae gve that SSM = 3.059 bt ot SST. Howeve, ote that SST=SSM+SSE
More informationGeneralisation on the Zeros of a Family of Complex Polynomials
Ieol Joul of hemcs esech. ISSN 976-584 Volume 6 Numbe 4. 93-97 Ieol esech Publco House h://www.house.com Geelso o he Zeos of Fmly of Comlex Polyomls Aee sgh Neh d S.K.Shu Deme of hemcs Lgys Uvesy Fdbd-
More informationunder the curve in the first quadrant.
NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )
More informationFractional Integrals Involving Generalized Polynomials And Multivariable Function
IOSR Joual of ateatcs (IOSRJ) ISSN: 78-578 Volue, Issue 5 (Jul-Aug 0), PP 05- wwwosoualsog Factoal Itegals Ivolvg Geealzed Poloals Ad ultvaable Fucto D Neela Pade ad Resa Ka Deatet of ateatcs APS uvest
More informationPermutations that Decompose in Cycles of Length 2 and are Given by Monomials
Poceedgs of The Natoa Cofeece O Udegaduate Reseach (NCUR) 00 The Uvesty of Noth Caoa at Asheve Asheve, Noth Caoa Ap -, 00 Pemutatos that Decompose Cyces of Legth ad ae Gve y Moomas Lous J Cuz Depatmet
More information«A first lesson on Mathematical Induction»
Bcgou ifotio: «A fist lesso o Mtheticl Iuctio» Mtheticl iuctio is topic i H level Mthetics It is useful i Mtheticl copetitios t ll levels It hs bee coo sight tht stuets c out the poof b theticl iuctio,
More information