Relationship Pattern of Poverty and Unemployement in Indonesia with Bayesian Spline Approach
|
|
- Erica Booth
- 6 years ago
- Views:
Transcription
1 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 9 Reltoshp Ptter of Poverty d Ueployeet Idoes wth Byes Sple Approch I Nyo Budtr, Rt D, Purhd d 3 Stwko Dresto Lecturer of Sttstc Deprtet, Sepuluh Nopeber Isttute of Techology ITS Cpus, Sukollo, Surby 60 College Studet of Sttstc Deprtet, Sepuluh Nopeber Isttute of Techology ITS Cpus, Sukollo, Surby 60 3 BPS-Sttstcs Idoes Jl. Dr. Sutoo No.6-8, Jkrt 070 Abstrct Poverty s oe of fudetl probles whch becoe jor cocer of Idoes Goveret. World Poverty Cosso sd tht ueployet s oe of the cuses of poverty. A lot of ltertures stte tht there s strog correlto betwee ueployet d poverty, but to prove t eprclly, ws ot esy. To see the reltoshp ptter betwee poverty d ueployet Idoes, t c be used sple opretrc regresso odel. Sple esttor opretrc regresso c be obted by Byess pproch by usg pror Guss proper d order to choose the optl soothg preter, Geerlzed Cross Vldto (GCV) ethod s choose. Reltoshp odel of poverty d ueployet Idoes obted the for of qudrtc sple odel wth two optl kots where percetge of poverty s qudrtc curve d rse the stge whe ope ueployet rte s less th 3.87, d wll be decled whe the ope ueployet rte oved betwee 3.87 d 4.4. But fter the ope ueployet rte reched 4.4, the percetge of poverty re-pttered qudrtclly but decresed slowly. So, for the cse Idoes, udrectol reltoshp betwee poverty d ueployet the rego occurred oly prtlly, whle soe re ctully spg. Key Words: opretrc regresso odel, sple, Byess, GCV. Itroductos Poverty s oe of the fudetl ssues whch becoe jor cocer of Idoes goveret. It s evtble tht oe of poverty cuse s ueployet. Ueployet dctor selecto bsed o the fct tht the dctor s drectly relted to coe levels. World Poverty Cosso lso oted tht ueployet s jor cuse of poverty [33]. Theoretclly, the poverty rte wll ove to follow the rte of ueployet. I ths cse whe the ueployet rte crese the the poverty level wll utotclly crese. Postve reltoshp betwee poverty d ueployet re foud severl coutres. I Kore, for exple, [3] foud very strog reltoshp betwee poverty levels d ueployet rtes. Whe the ueployet rte creses, the poverty rte lso rose d whe the level of ueployet cled, the poverty levels lso fell. However, the chges betwee ueployet level d poverty re ot lwys cosstet s tht foud studes other coutry. For exple, s quoted by [0] bsed o reserch the Uted Sttes foud tht poverty does ot hve strog correlto wth ueployet. Def [0] further stted tht the reltoshp betwee ueployet d poverty s strogly flueced by how poverty s esured. O the other hd, the wek reltoshp betwee poverty d ueployet c lso be cused by wek esureet of the rte of ueployet. Ths s evdeced by the [37] bsed o ther study usg Brzl dt. So d Kkw [37] propose ew esure of ueployet bsed o ot oly the ueployed but lso those whose ergs re below the u wge set by the goveret. By odfyg the covetol ueployet rte esureets they foud tht the correlto betwee ueployet d poverty s very sgfct, whle the sze of ueployet IJBAS-IJENS Deceber 0 IJENS
2 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 0 bsed o the covetol reltoshp betwee ueployet d poverty do ot see sgfct. How does the ptter of the reltoshp betwee poverty d ueployet Idoes? A lot of lterture stte tht there s close lk betwee ueployet d poverty, but to prove t eprclly s ot esy. I purpose to odellg the reltoshp betwee poverty d ueployet Idoes, where the ptter of reltoshp s ot kow dvce, the used opretrc regresso odel. Nopretrc regresso odels tht ofte gets the tteto of the reserchers s the Kerel ([38], [5]), Sple ([3], [47], [8], [], [8]), Fourer seres [] d wvelets [3]. Aog the opretrc regresso odel bove, the sple s odel tht hs very specl d very good sttstcl d vsul terpretto becuse t hs hgh flexblty ([], [3]). Besdes, sple s ble to hdle dt chrcter / fucto propertes of sooth d fluctute the subsub-tervl ([6], [3]). It hs lso bee show by [4] whch copres Kerel soothg sple techque wth the dt of gross tol product of Turkey. Sple s polyol fucto for whch s defed sub-sub-tervls (pecewse polyol). Boudry pots of these tervls re clled kots. The kot wll coect the polyols whch re defed o the subtervls such wy to for cotuous fucto. How y kots d where kots re locted re soe of probles sple usg. Aother proble s sple bss fuctos selecto. There re severl bses fuctos of sple, cludg power cuts, cubc sple, d B- sple. Sple esttor c be obted by solvg the pelzed lest squre (PLS) optzto or ze the uber of dt tches (Goodess of ft) d the sze of curve soothess. Sple esttor opretrc regresso developed by y reserchers by tkg the vrto the Goodess of ft d sze of curve soothess. Keldorf d Whb [8], Crve d Whb [8] d Whb [46] produces turl sple esttor (the orgl). Sple esttor s recoeded for beg use o sooth dt. Cox d O Sullv [6] obt the M-type sple esttors to del wth outlers opretrc regresso. Oehlert [30] provde relxg sple esttor d [9] gves the qutle sple esttor. Budtr, et. l [8] gves weghted sple esttor to del wth equlty of vrce (heteroskedstc) opretrc regresso. Sple ts developet, t c ot resolve the proble of ferece such s cofdece tervls for regresso curve. For tht [44] provde Byes pproch for the orgl sple esttor Guss respose dt d [45] lso hve to costruct cofdece tervls for the orgl sple odel. Gu [4] hs developed the results obted by [44] for o-guss respose dt. Sth d Koh [36] usg Byes pproch esttg B- sple fuctos bvrte opretrc regresso odel wth utocorrelto error. Budtr d Subr [9] geerlze the Byes pproch of [45] for weghted sple esttor opretrc regresso. Holes d Mlck [6] usg Byes lyss ultvrte ler sple odel regresso for Guss respose dt. Berry, Crroll, d Ruppert [5] usg Byes pproch to odel the soothg regresso sple fucto d regresso P-sple wth the presece of esureet error. Adebyo [] usg Byes pproch wth B-sple bss fuctos for the odel of lourshed chldre Zb. Crceu, Ruppert d Wd [7] deostrted the use of BUGS sepretrc regresso odels. Chb d Greeberg [4] developed Byes lyss for opretrc regresso odels wth cubc sple bss fuctos d xture of Drchlet processes dstrbuted errors the dt tht [43] regrdg the credt rtg fr Stdrd d Poor's. I ths pper, opretrc regresso odel whch s used odelg the reltoshp betwee poverty d ueployet Idoes s sple odel wth Byes pproch. Pror dstrbutos used ths reserch s lted dstrbuto Guss Iproper pror.. Sple Esttor Gve the odel of opretrc regresso y = f(t ) + ε, t [,b], =,,,. The for of regresso curve s ssued ukow d s coted the Sobolev spce W [, ] b, where W [, ] b = { g ; b ( ) ( g ( t)) dt < }. Rdo error ε s ssued depedet orlly dstrbuted wth zero e d IJBAS-IJENS Deceber 0 IJENS
3 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 vrce σ. Sple esttors opretrc regresso re obted by solvg Pelzed Lest Squre (PLS) optzto : M { R(f) + λ J(f) } f W [, b ] Qutztos of R(f) d J(f) stte goodess of ft d esure of the soothess of fucto respectvely. The soothg preter λ cotrols betwee R(f) d J(f). Sple esttors opretrc regresso re developed by y reserchers by vred R(f) d J(f). Whb [47] took R(f) d J(f) qudrtc for : R(f) = ( y j f ( t j )) d j= b ( ) J(f) = [ f ( t)] dt. Accordg to [8], the for of sple esttor ˆf λ s obted by fdg the vlue of f W [, ] b whch zes for : b ( ) ( ( )) + λ ( ( )) = () y f t f t dt Optzto proble equto () c be solved usg Reproducg Kerel Hlbert Spce pproch [8]. Sple optzto c be trsfored to projecto proble o Hlbert spce [38]. The very portt propertes of Reproducg Kerel re we c detere the represetto of fte ler fuctol, such tht regresso curve f W [, b] whch s the optzto soluto of equto () c be foud. If H R = H 0 H d φ,..., φ sp H 0 spce d T s full colu rk trx of order T = Lφ, =,,, gve by { } d v=,,,, the fucto f whch zes ( y f ( t )) + λ P f R s = fˆ λ = α φ + βψ ψ = Pη v v v= =, where α α α = (,..., ) ' = ( T'M T) T' M y β = (,..., ) ' β β v = M (I - T(T'M T) T'M )y M = Σ + λi Σ = < ψ, ψ >,, j =,,3,,. { j } [47] hve show tht the sple esttor tkes for turl sple polyol. 3. Byes Approch for Sple Esttor The selecto of pror dstrbutos Byes pproch s very portt thg. Pror forto used ths reserch s proper Guss pror whch s cobed wth sple forto, usully expressed the lkelhood fucto to fd ts posteror dstrbuto. Byes pproch for pot estto s obted fro posteror e, d tervl estto s obted fro ts posteror vrce. Gve splg observto ( t, y ), ( t, y ),..., ( t, y ), obted fro stochstc process { y( t), t [, b] } the odel y f ( t ) ε, t [, b] { f ( t); t [, b] } dstrbuto, d follows = +, =,,,. hs proper Guss pror / ( ) = αvφv ( ) + β ( ) v= f t t Z t where polyol coeffcet ' α = ( α, α,..., α) ~ N(0, τ I), τ, τ, β s postve costt, d t ( t u) ( ) Z( t) = dw ( u)! s Weer process wth E( Z( t )) = 0 d Reproducg Kerel covrce Cov( Z( s), Z( t)) = { ψ ( s, t)} whch ψ ( s, t) = b ( s u) + ( t u) + [( )! ] du Aother ssupto s polyol coeffcet α v, v =,,..., wth Z( t ) ot correlted [45] d { ε (t)} s Guss process wth E( ε ( t)) = 0, d ( ε ( ), ε ( )) Cov t s σ, f t = s = 0,f t s IJBAS-IJENS Deceber 0 IJENS
4 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 If gve Guss rdo vectors y, f, ε wth zero e d follow odel y = f + ε, whch E( f ) = 0, E( ff ') = βσ f, E( ε) = 0, E( εε ') = σ I d E( fε ') = 0, the E( y) = 0 d Vr( y) = βσ f + σ I. If h hs orl dstrbuto wth E( h) = 0, E( hh ') βσ, E( hε ') = 0, d E( hf ') = βσ the hf hf f = h E( h y) = Σ ( Σ + λ I) y d Vr( h y) = β ( Σ Σ Σ Σ ) h hf f fh + σ Σ Σ A(λ) Σ Σ wth hf f f fh A(λ) = Σf ( Σf + λ I ) d Suppose T = { φ t } v λ = σ / β. ( ), =,,, / v=,, the f = Tα + β Z (t). Assued tht α ~ N( 0, τi ) d Z ~ N( 0Σ, ) utully depedet, the / / E( ff ') = E ( + β (t))( + β (t))' Tα Z Tα Z = τ TT' + βσ. Becuse E( ff ') = βσ f the τ Σ = f E( ff ')= TT ' β β + Σ or Σf = ϕττ ' + Σ wth ϕ = τ / β. If we tke h = f ( t), the / / ( hf ) = ( φα + β )( Tα + β Z ) E ' E ' Z(t) (t) ' = τφ ' T' + βψ ' d t / / ( hh ) = ( φ α + β )( φ α + β ) E ' E ' Z(t) ' Ζ(t) ' = τφ ' φ + βψ(t,t). Becuse E( hf ') = βσ hf d ( hh ) = h E ' βσ, the Σhf = E( hf ') = ϕ φ ' T' + ψ t ' d β Σh = E( hh ') = ϕ φ ' φ + ψ( t,t ). β Usg qudrtc loss fucto, we fd tht esttor f λ (t) s the posteror e f, hece by tkg h = f(t), we get fλ, ϕ (t) = E( f(t) y) =Σhf ( Σf + λ I) y = ( φ (t),..., φ (t)). ϕ '[ ϕ ' (λ)] T TT + M y + ( ψ ( t),..., ψ ( t))[ ϕ ' + ( λ)] TT M y where ϕ = τ / β d M = Σ + λi. If the lt of posteror e vlue s tke for τ, we fd tht ( ϕ + ) l ϕt' TT' M(λ) ϕ ( ) = T M T T M d ' (λ) ' (λ) ( ϕtt + M ) l ' (λ) ϕ ( ( ) ) = M (λ) I T T' M (λ) T T' M (λ) So tht l E ( f ( t) y) = f ( t). Ths result s τ τ detcl wth sple esttor tht obted fro Pelzed Log Lkelhood pproch. Sple esttor s strogly flueced by the soothg preter λ. The ethod used selectg the soothg preter λ for the sple esttor bsed o Byes pproch s GCV. 4. The Ptter of Poverty d Ueployet Relto Idoes A lot of lterture stte tht there s close reltoshp betwee ueployet d poverty, but t s ot esy to prove t eprclly [6]. Ussully the lysts use two dt sources to see ts reltoshp Idoes. Poverty dt re clculted fro the results of Suses (Ntol Socl Ecooc Survey) d ueployet dt re obted fro the Skers (Ntol Lbour Survey). The dffculty to show the reltoshp betwee ueployet d poverty Idoes eprclly, c be solved by usg sple opretrc regresso odel. Accordg to sctter plot Fgure, we c see tht the reltoshp betwee ope ueployet rte d poverty percetge hs o ptter. So tht, the level of poverty Idoes s odeled usg sple opretrc odel where kots pot s selected usg GCV ethod. λ IJBAS-IJENS Deceber 0 IJENS
5 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 3 Tble. GCV d MSE vlues t vrous kot pots of the qudrtc sple odel persetse kesk tgkt pegggur terbuk Fgure. Sctter plot betwee ope ueployet rte d poverty percetge Tble, Tble d Tble 3 show the GCV vlues of ech pot of the selected kots. For the ler sple odel, the sllest GCV vlue s correspodg to the kot pots k = 7.4, k = 7.68, d k 3 = For the qudrtc sple odel, the sllest GCV vlue s correspodg to the kot pots k = 3.87 d k = 4.4. Ad for the cubc sple odel, the sllest GCV vlue s correspodg to the kot pots k = d k = Aog those three sple odels, the sllest GCV occurs the qudrtc sple odel by usg kot pots. Tble. GCV d MSE vlues t vrous kot pots of the ler sple odel Nuber of Kot Kot Pots GCV k = k = k = k = k = k = k = 7.4, k = k = 7.4, k = k = 7.4, k = k = 7.4, k = k = 7.4, k = k = 7.4, k = k = 7.4, k = 7.68, k 3 = k = 7.4, k = 7.68, k 3 = k = 7.4, k = 7.68, k 3 = k = 7.4, k = 7.68, k 3 = k = 7.4, k = 7.68, k 3 = k = 7.4, k = 7.68, k 3 = Nuber of Kot Kot Pots GCV k = k = k = k = k = k = k = 3.87, k = k = 3.87, k = k = 3.87, k = k = 3.87, k = k = 3.87, k = k = 3.87, k = k = 3.95, k = 4.4, k 3 = k = 3.96, k = 4.4, k 3 = k = 3.97, k = 4.4, k 3 = k = 3.98, k = 4.4, k 3 = k = 3.99, k = 4.4, k 3 = k = 4.00, k = 4.4, k 3 = Tble 3. GCV d MSE vlues t vrous kot pots of the cubc sple odel persetse kesk Nuber of Kot Kot Pots GCV k = k = k = k = k = k = k = 4.059, k = k = 4.059, k = k = 4.059, k = k = 4.059, k = k = 4.059, k = k = 4.059, k = k = 4.00, k = 4.849, k 3 = k = 4.00, k = 4.850, k 3 = k = 4.00, k = 4.85, k 3 = k = 4.00, k = 4.85, k 3 = k = 4.00, k = 4.853, k 3 = k = 4.00, k = 4.854, k 3 = tgkt pegggur terbuk Fgure. The ler sple wth 3 kots (k = 7.4, k = 7.68, d k 3 = 8.49) IJBAS-IJENS Deceber 0 IJENS
6 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 4 persetse kesk persetse kesk Tgkt Pegggur Terbuk Fgure 3. The cubc sple wth kots (k = d k = 7.649) tgkt pegggur terbuk Fgure 4. The qudrtc sple wth kots (k = 3.87 d k = 4.4) Ler sple d cubc sple pproch wth three d two kots respectvely re ot optl yet whch s vsully show Fgure d Fgure 3. Dfferet fro those two prevous sple odels, qudrtc sple estto odel wth two kots s ble to optlly esttes the ptter of dt, so tht behvor of the ptter of chge ech tervl of the ope ueployet rte c be detfed d vsully show by Fgure 4. Qudrtc sple odel wth two kot pots optl t k = 3.87 d k = 4.4 s gve by yˆ( t) = t 04.56t ( t ) ( t ) t 04.56t, t < 3.87 = t 78.96t, 3.87 t < t 0.`t, 4.4 t I 00, the percetge of poverty Idoes hs qudrtc ptter d rse t the ope ueployet rte s less th 3.87, d wll decle whe the ope ueployet rte oved betwee 3.87 d 4.4. After the ope ueployet rte rech 4.4, the percetge of poverty re-ptter qudrtclly but decrese slowly. Bsed o dt fro BPS publcto, the reltoshp betwee the level of ueployet d poverty Idoes does ot follow ptter lke tht foud Kore. The fct tht there s t provcl level whe the ueployet rte creses, the poverty rte s decresg or vce vers (see Fgure ). So the cse of Idoes, the reltoshp betwee ueployet d poverty re ot lwys the se drecto ccordg to the ssupto of exstg ecooc theory, but t hs verse reltoshp. Ths wek ueployet-poverty reltoshp s evdetly see t provcl level. I 00, Idoes whch s cossts of 33 provces, there were soe provces such s Ru, South Klt d North Sulwes, whch ted to hve frly strog reltoshp betwee ueployet d poverty, but the provces such s Ppu, West Ppu, Mluku, West Sulwes, Southest Sulwes, Gorotlo, West Nus Teggr, Est Nus Teggr, d NAD, the poverty rte sees very hgh copred to the level of ueployet. Ths s becuse soe people who work those res re low pd, so tht ther coe s lted or stll below the poverty le. Ths pheoeo s ot oly flueced by the ecooc sde, but socl culturl d geogrphcl codtos lso cotrbuted gretly to the level of poverty. The poverty level Jkrt d Bte, sees too low copred to the level of ueployet. Ths pheoeo c be expled s follows. People who re ble to be ueployed household y dcte tht those household hve suffcet coe to support the ueployed. I ter of poverty, the ueployed those household does ot utotclly becoe poor becuse there re other fly ebers who hve suffcet coe to sust hs fly lve bove the poverty le. I ddto, the fcts show tht urb res there re soe people who volutrly ueployed becuse they re wtg to get the job ccordg to the ther skll or educto, d they re ot relly poor becuse they y be prt of household wth ddle to upper coe. Not oly tht, there re lso y dsgused workers who re ot regstered,.e. coercl sex workers, llegl goods seller, beggrs, etc, who durg the survey cled jobless (ueployed). Ther coes re geerlly bove the poverty le. Those thgs re the cuse of the ueployet rte s IJBAS-IJENS Deceber 0 IJENS
7 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 5 cresg rpdly but the rte of poverty s reduced. Aother explto s tht poor households re lost possble to be ueployed [3]. Osh [3] stteet c be uderstood cosderg developg coutres lke Idoes there re o socl securty for ueployed, so order to survve, the poor hve to work, whether they lke or ot, eve oly few hours week. Ths s cosstet wth [4] who sd tht the poor re those who do ot work regulrly or cotuously, or who workg prtte. Streete, et l [4] explctly stted tht ueployet ws ot stsfctory esure of poverty, becuse geerlly people who re ueployed hve better codto, whle ost people who re relly poor t s ot dle. 5. Coclusos The ptter of the reltoshp betwee poverty d ueployet Idoes c be see usg qudrtc sple odels wth two optl kots. Percetge of poverty s qudrtc curve d rse the stge whe ope ueployet rte s less th 3.87, d wll be decled whe the ope ueployet rte oved betwee 3.87 d 4.4. But fter the ope ueployet rte reched 4.4, the percetge of poverty repttered qudrtclly but decresed slowly. So, for the cse Idoes, udrectol reltoshp betwee poverty d ueployet the rego oly prtlly occurred, whle soe re ctully spg. I order to see the effect of ueployet o poverty, we lso eed to look t chrcterstcs of ech rego. It lso eeds to look t other fctors relted to the eployet,.e. feld of busess where he worked, busess sttus (forl / forl), d hours worked per week. So for further reserch c use the Byes Multvrte Sple pproch. 6. Refereces []. Adebyo, S. B., 003, Modellg chldhood lutrto Zb: dptve Byes sples pproch, Sttstcl Methods & Applctos, : 7 4. []. Atods, A., Gregorre, G. d Mckegu, W., 994, Wvelet Methods for Curve Estto, Jourl of the Aerc Sttstcl Assocto, 89, [3]. Atods, A., Bgot, J. d Spts, T., 00, Wvelet Esttors Nopretrc Regresso: A Coprtve Sulto Study, Jourl of Sttstcl Softwre, 6, -83. [4]. Ayd, D., 007. A coprso of the opretrc regresso odels usg soothg sple d kerel regresso. Proc. World Acd. Sc. Eg. Techol., 36 : pdf. [5]. Berry, S.M., Crroll, R.J., d Ruppert, D., 00. Byes soothg d regresso sples for esureet error probles. J. Aer. Sttst. Assoc. 97, [6]. BPS-Sttstcs Idoes, 009, Alyss of Poverty, Eployet d Icoe Dstrbuto, BPS, Jkrt. [7]. BPS-Sttstcs Idoes, 009, Alyss d Clculto of Poverty Level, BPS, Jkrt. [8]. Budtr, I. N., Subr, d Soejoet, Z., 997, Weghted Sple Esttor, Bullet of the Itertol Sttstcl Isttute, 5, [9]. Budtr, I. N., d Subr, 998, Byes Approch To Nopretrc Sple Curves Weghted Regresso, Jourl of Mthetcs Assocto of Idoes (MIHMI), 4, [0]. Budtr, I. N., 000, Method U, GML, CV d GCV Nopretrc Sple Regresso, Jourl of Mthetcs Assocto of Idoes (MIHMI), 6, []. Budtr, I. N., 00, Pelzed Lkelhood Type Esttor, Jourl of Nturl Ubrw Mthetcs d Nturl Sceces, Specl Issue, []. Budtr, I. N., 005, Tructed Polyol Sple Model Fly I sepretrc regresso, Ppers of the Ntol Ser o Mthetcs, Mthetcs Deprtet Dpoegoro Uversty, Serg. [3]. Budtr, I. N., 006, Sple Models Wth Optl Kots, Jourl of Bsc Scece, Mthetcs d Nturl Sceces Uversty of Jeber, 7, [4]. Chb, S., d Greeberg, E., 00. Addtve cubc sple regresso wth Drchlet process xture errors. Jourl of Ecooetrcs 56, IJBAS-IJENS Deceber 0 IJENS
8 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 6 [5]. Choudhur N, Ghosl S, d Roy A, 007, Nopretrc bry regresso usg Guss Process Pror, Sttstcl Methodology, 4, 7-43 [6]. Cox, D. D. d O Sullv, F.,996, Pelzed Type Esttor for Geerlzed Nopretrc Regresso, 983, Jourl of Multvrte Alyss, 56, [7]. Crceu, C., Ruppert, D. d Wd, M.P, 005. Byes lyss for pelzed sple regresso usg WBUGS. Jourl of Sttstcl Softwre, Volue 4, Artcle 4. [8]. Crve, P., d Whb, G., 979, Soothg Nosy Dt Wth Sple Fuctos, Nuersche Mthetk, 3, [9]. de Boor, C, 978, A Prctcl Gude to Sples, Sprger-Verlg. [0]. DeF, R., 00, The Ipct of Mcroecooc Perforce o Altertve Poverty Mesures, Socl Scece Reserch, 3: []. Eubk,R.L.,988, Sple Soothg d Nopretrc Regresso, Mercel Dekker, New York. []. Fred, J., 99, Multvrte Regresso d Sple Soothg, A. Sttst 9, -4. [3]. Gree, P. J. d Slver, B.W., 994, Nopretrk Regresso d Geerlzed Ler Models ( Roughess Pelty Approch), Chp & Hll, New York. [4]. Gu, C., (99), Pelzed Lkelhood Regresso: A Byes Alyss, Sttstc Sc, [5]. Hrdle, G., 990, Appled Nopretrk Regresso, Cbrdge Uversty Press, New York. [6]. Holes, C. C. d Mllck, B. K., 00, Byes regresso wth ultvrte ler sples, J. Roy. Sttst Soc. B, 63, (), 3-7. [7]. Keldorf, G. d Whb, G., 970, A correspodece betwee Byes estto o stochstc processes d soothg by sples, A. Mth. Sttst., 4, [8]. Keldrof, G. d Whb, G., 97, Soe Result o Tchebycheff Sple Fucto, Jourl of Mthetcl Alyss d Applcto, 33, [9]. Koeker, R., Ng., P. d Portoy, S.,994, Qutle Soothg Sple, Boetrk, 8, [30]. Oehlert, G.W.,99, Relxed Boudry Soothg Sple, The Als of Sttstcs, 0, [3]. Osh, Hrry T., 990, Eployet Geerto: The Log-Ter soluto to Poverty. As Developet Revew. Vol. 8. No.. [3]. Prk, A. Wg, S d Wu, G., 00, Regol Poverty Trgetg Ch, Jourl of Publc Ecoocs, Vol 86 pp3-53. [33]. Suders, P., 00, The Drect d Idrect Effects of Ueployet o Poverty d Iequlty, SPRC Dscusso Pper No. 8, The Socl Polcy Reserch Cetre Uversty of New South Wles, Sydey NSW 05, Austrl. [34]. Slver, B., 984, A Fst d Effcet Cross-Vldto Method for Soothg Preter Choce Sple Regresso, Jourl of the Aerc Sttstcl Assocto, 79, [35]. Slver, B., 985, Soe Aspects of the sple Soothg Approch to No Pretrc Regresso Curve Fttg (Wth Dscusso), Jourl Royl Sttstcl Socety, 47, -5. [36]. Sth, M. d Koh, R., 997, A Byes Approch Bvrte Regresso, Jourl of the Aerc Sttstcl Assocto, 9, [37]. So, H. H. d Kkw, N., 006, Mesurg the pct of prce chges o poverty: w th pplctos to Thld d Kore, Jourl of Ecooc Iequlty, Vol. 4, No., [38]. Speck, P., 980, Mx Esttes of Ler Fuctols Hlbert Spce, Upublshed Muscrpt. [39]. Speck, P., 985, Sple Soothg d Optl Rtes of Covergece Nopretrc Regresso Models, Jourl A. Sttst, 3, [40]. Speck, P., 988, Kerel soothg prtl ler odels. J. Roy. Sttst. Soc. Se. B 50 (3), [4]. Streete, Pul, Burk, S.J., Hq, M., Hcks, N., d Stewrt, F., 98, Frst Thgs Frst, Meetg Bsc Hu Needs Developg Coutres, New York: Oxford Uversty Press IJBAS-IJENS Deceber 0 IJENS
9 Itertol Jourl of Bsc & Appled Sceces IJBAS-IJENS Vol: No: 06 7 [4]. Todro, M.P Ecooc Developet, 6 th edto, New York: Log, chpter 5. [43]. Verbeek, M., 008. A Gude to Moder Ecooetrcs, thrd ed. Joh Wley & Sos, Ltd., Chchester. [44]. Whb, G., 978, Iproper prors, sple soothg d the proble of gurdg gst odel errors regresso, Jurl Royl Sttstcs Socety, B, 40, [45]. Whb, G., 983, Byes "Cofdece Itervls" for the crossvldted soothg sple, Jurl Royl Sttstcs Socety, B, 45, [46]. Whb, G., 985, A Coprso of GCV d GML for Choosg the Soothg Preter the Geerlzed Sple Soothg Proble, Jourl the Als of Sttstcs, 3, [47]. Whb, G., 990, Sple Model for Observtol Dt, Socety For Idustrl d Appled Mthetcs, Phldelph. [48]. Whb, G., 000, A Itroducto to Model Buldg wth Reproducg Kerel Hlbert Spces, Techcl Report, 00, Uversty of Wscos, 97, IJBAS-IJENS Deceber 0 IJENS
CURVE 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 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 informationDesign of Bayesian MDS Sampling Plan Based on the Process Capability Index
World Acdey of Scece, Egeerg d Techology Vol:, No:0, 07 Desg of Byes MDS Splg Pl Bsed o the Process pblty Idex Dvood Shshebor, Mohd Sber Fllh Nezhd, S Sef Itertol Scece Idex, Idustrl d Mufcturg Egeerg
More informationSome Estimators for the Population Mean Using Auxiliary Information Under Ranked Set Sampling
Jourl of Moder Appled Sttstcl Methods Volue 8 Issue Artcle 4 5--009 Soe Esttors for the Populto Me Usg Aulr Iforto Uder ked Set Splg Wld A. Abu-Deh Sult Qboos Uverst budeh@hoo.co M. S. Ahed Sult Qboos
More informationFibonacci and Lucas Numbers as Tridiagonal Matrix Determinants
Rochester Isttute of echology RI Scholr Wors Artcles 8-00 bocc d ucs Nubers s rdgol trx Deterts Nth D. Chll Est Kod Copy Drre Nry Rochester Isttute of echology ollow ths d ddtol wors t: http://scholrwors.rt.edu/rtcle
More informationON NILPOTENCY IN NONASSOCIATIVE ALGEBRAS
Jourl of Algebr Nuber Theory: Advces d Applctos Volue 6 Nuber 6 ges 85- Avlble t http://scetfcdvces.co. DOI: http://dx.do.org/.864/t_779 ON NILOTENCY IN NONASSOCIATIVE ALGERAS C. J. A. ÉRÉ M. F. OUEDRAOGO
More informationChapter 7. Bounds for weighted sums of Random Variables
Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout
More informationAn Alternative Method to Find the Solution of Zero One Integer Linear Fractional Programming Problem with the Help of -Matrix
Itertol Jourl of Scetfc d Reserch Pulctos, Volue 3, Issue 6, Jue 3 ISSN 5-353 A Altertve Method to Fd the Soluto of Zero Oe Iteger Ler Frctol Progrg Prole wth the Help of -Mtr VSeeregsy *, DrKJeyr ** *
More informationOn Solution of Min-Max Composition Fuzzy Relational Equation
U-Sl Scece Jourl Vol.4()7 O Soluto of M-Mx Coposto Fuzzy eltol Equto N.M. N* Dte of cceptce /5/7 Abstrct I ths pper, M-Mx coposto fuzzy relto equto re studed. hs study s geerlzto of the works of Ohsto
More informationAnalytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases
Itertol Jourl of Advced Reserch Physcl Scece (IJARPS) Volume, Issue 5, September 204, PP 6-0 ISSN 2349-7874 (Prt) & ISSN 2349-7882 (Ole) www.rcourls.org Alytcl Approch for the Soluto of Thermodymc Idettes
More informationStatistical Modeling and Analysis of the Correlation between the Gross Domestic Product per Capita and the Life Expectancy
Als of Dure de Jos Uerst of Glt Fsccle. Ecoocs d Appled fortcs Yers XX o /5 N-L 584-49 N-Ole 44-44X www.e.fe.ugl.ro ttstcl Mode d Alss of the Correlto etwee the Gross Doestc Product per Cpt d the Lfe Epectc
More informationDATA FITTING. Intensive Computation 2013/2014. Annalisa Massini
DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg
More informationSoo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:
Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1
More informationRegression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers
Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two
More informationOn Several Inequalities Deduced Using a Power Series Approach
It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty
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 informationTesting for multivariate normality of disturbances in the multivariate linear regression model Yan Su a, Shao-Yue Kang b
Itertol Coferece o Itellget Systes Reserch d Mechtrocs Egeerg (ISRME 05) Testg for ultvrte orlty of dsturbces the ultvrte ler regresso odel Y Su, Sho-Yue Kg b School of Mthetcs d hyscs, North Ch Electrc
More informationIntroduction to mathematical Statistics
Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs
More informationPredicting Survival Outcomes Based on Compound Covariate Method under Cox Proportional Hazard Models with Microarrays
Predctg Survvl Outcomes Bsed o Compoud Covrte Method uder Cox Proportol Hzrd Models wth Mcrorrys PLoS ONE 7(10). do:10.1371/ourl.poe.0047627. http://dx.plos.org/10.1371/ourl.poe.0047627 Tkesh Emur Grdute
More informationMore Regression Lecture Notes CE 311K - McKinney Introduction to Computer Methods Department of Civil Engineering The University of Texas at Austin
More Regresso Lecture Notes CE K - McKe Itroducto to Coputer Methods Deprtet of Cvl Egeerg The Uverst of Tes t Aust Polol Regresso Prevousl, we ft strght le to os dt (, ), (, ), (, ) usg the lest-squres
More informationCS321. Numerical Analysis
CS3 Nuercl Alss Lecture 7 Lest Sures d Curve Fttg Professor Ju Zhg Deprtet of Coputer Scece Uverst of Ketuc Legto KY 456 633 Deceer 4 Method of Lest Sures Coputer ded dt collectos hve produced treedous
More informationAnalytic hierarchy process-based Chinese sports industry structure scheme optimization selection and adjustment research
Avlble ole www.ocpr.co Jourl of hecl d Phrceutcl Reserch, 204, 6(6):2406-24 Reserch Artcle ISSN : 0975-7384 ODEN(USA) : JPR5 Alytc herrchy process-bsed hese sports dustry structure schee optzto selecto
More informationDepartment of Statistics, Dibrugarh University, Dibrugarh, Assam, India. Department of Statistics, G. C. College, Silchar, Assam, India.
A Dscrete Power Dstruto Surt Chkrort * d Dhrujot Chkrvrt Dertet of Sttstcs Drugrh Uverst Drugrh Ass Id. Dertet of Sttstcs G. C. College Slchr Ass Id. *el: surt_r@hoo.co. Astrct A ew dscrete dstruto hs
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 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 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 informationInternational Journal of Scientific and Research Publications, Volume 3, Issue 5, May ISSN
Itertol Jourl of Scetfc d Reserch Pulctos, Volue 3, Issue 5, My 13 1 A Effcet Method for Esy Coputto y Usg - Mtr y Cosderg the Iteger Vlues for Solvg Iteger Ler Frctol Progrg Proles VSeeregsy *, DrKJeyr
More informationA Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares
Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com
More informationIn Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is
Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I
More informationICS141: Discrete Mathematics for Computer Science I
Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll
More informationSUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES
Avlble ole t http://sc.org J. Mth. Comput. Sc. 4 (04) No. 05-7 ISSN: 97-507 SUM PROPERTIES OR THE K-UCAS NUMBERS WITH ARITHMETIC INDEXES BIJENDRA SINGH POOJA BHADOURIA AND OMPRAKASH SIKHWA * School of
More informationAPPLICATION OF THE CHEBYSHEV POLYNOMIALS TO APPROXIMATION AND CONSTRUCTION OF MAP PROJECTIONS
APPLICATION OF THE CHEBYSHEV POLYNOMIALS TO APPROXIMATION AND CONSTRUCTION OF MAP PROJECTIONS Pweł Pędzch Jerzy Blcerz Wrsw Uversty of Techology Fculty of Geodesy d Crtogrphy Astrct Usully to pproto of
More informationA METHOD FOR THE RAPID NUMERICAL CALCULATION OF PARTIAL SUMS OF GENERALIZED HARMONICAL SERIES WITH PRESCRIBED ACCURACY
UPB c Bull, eres D, Vol 8, No, 00 A METHOD FOR THE RAPD NUMERAL ALULATON OF PARTAL UM OF GENERALZED HARMONAL ERE WTH PRERBED AURAY BERBENTE e roue o etodă ouă etru clculul rd l suelor rţle le serlor roce
More informationOn a class of analytic functions defined by Ruscheweyh derivative
Lfe Scece Jourl ;9( http://wwwlfescecestecom O clss of lytc fuctos defed by Ruscheweyh dervtve S N Ml M Arf K I Noor 3 d M Rz Deprtmet of Mthemtcs GC Uversty Fslbd Pujb Pst Deprtmet of Mthemtcs Abdul Wl
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 informationDifferential Method of Thin Layer for Retaining Wall Active Earth Pressure and Its Distribution under Seismic Condition Li-Min XU, Yong SUN
Itertol Coferece o Mechcs d Cvl Egeerg (ICMCE 014) Dfferetl Method of Th Lyer for Retg Wll Actve Erth Pressure d Its Dstrbuto uder Sesmc Codto L-M XU, Yog SUN Key Lbortory of Krst Evromet d Geologcl Hzrd
More informationBond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index
CROATICA CHEMICA ACTA CCACAA ISSN 00-6 e-issn -7X Crot. Chem. Act 8 () (0) 9 0. CCA-5 Orgl Scetfc Artcle Bod Addtve Modelg 5. Mthemtcl Propertes of the Vrble Sum Edeg Ide Dmr Vukčevć Fculty of Nturl Sceces
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 informationA New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming
ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research
More informationA New Efficient Approach to Solve Multi-Objective Transportation Problem in the Fuzzy Environment (Product approach)
Itertol Jourl of Appled Egeerg Reserch IN 097-6 Volue, Nuer 8 (08) pp 660-66 Reserch Id Pulctos http://wwwrpulctoco A New Effcet Approch to olve Mult-Oectve Trsportto Prole the Fuzzy Evroet (Product pproch)
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 informationGeneralized Hybrid Grey Relation Method for Multiple Attribute Mixed Type Decision Making*
Geerlzed Hybrd Grey Relto Method for Multple Attrbute Med Type Decso Mkg Gol K Yuchol Jog Sfeg u b Ceter of Nturl Scece versty of Sceces Pyogyg DPR Kore b College of Ecoocs d Mgeet Ng versty of Aeroutcs
More informationChapter 4: Distributions
Chpter 4: Dstrbutos Prerequste: Chpter 4. The Algebr of Expecttos d Vrces I ths secto we wll mke use of the followg symbols: s rdom vrble b s rdom vrble c s costt vector md s costt mtrx, d F m s costt
More informationA Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions
Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationDISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE
G Tstsshvl DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE (Vol) 008 September DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE GSh Tstsshvl e-ml: gurm@mdvoru 69004 Vldvosto Rdo str 7 sttute for Appled
More informationChapter 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 information3.1 Introduction to Multinomial Logit and Probit
ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore
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 informationSolutions Manual for Polymer Science and Technology Third Edition
Solutos ul for Polymer Scece d Techology Thrd Edto Joel R. Fred Uer Sddle Rver, NJ Bosto Idols S Frcsco New York Toroto otrel Lodo uch Prs drd Cetow Sydey Tokyo Sgore exco Cty Ths text s ssocted wth Fred/Polymer
More informationLecture 3-4 Solutions of System of Linear Equations
Lecture - Solutos of System of Ler Equtos Numerc Ler Alger Revew of vectorsd mtrces System of Ler Equtos Guss Elmto (drect solver) LU Decomposto Guss-Sedel method (tertve solver) VECTORS,,, colum vector
More informationChapter 2 Intro to Math Techniques for Quantum Mechanics
Fll 4 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 informationThe Distribution of Minimizing Maximum Entropy: Alternative to Weibull distribution for wind speed
Proceedgs o the 9th WSEAS Itertol Coerece o Appled Mthetcs, Istul, urey, My 7-9, 6 (pp65-6) he Dstruto o Mzg Mxu Etropy: Altertve to Weull dstruto or wd speed ALADDI SHAMILOV, ILHA USA, YELIZ MER KAAR
More informationAn Extended Mixture Inverse Gaussian Distribution
Avlble ole t htt://wwwssstjscssructh Su Sudh Scece d Techology Jourl 016 Fculty o Scece d Techology, Su Sudh Rjbht Uversty A Eteded Mture Iverse Guss Dstrbuto Chookt Pudrommrt * Fculty o Scece d Techology,
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationTHE BETA GENERALIZED PARETO DISTRIBUTION
Jourl of Sttstcs: Advces Theory d Applctos Volue 6 Nuer / Pges -7 THE BETA GENERALIZED PARETO DISTRIBUTION M M NASSAR d N K NADA Deprtet of Mthetcs Fculty of Scece A Shs Uversty Ass Cro 566 Egypt e-l:
More informationITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS
Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss
More informationMATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications
MATH999 Drected Studes Mthemtcs Mtr Theory d Its Applctos Reserch Topc Sttory Probblty Vector of Hgher-order Mrkov Ch By Zhg Sho Supervsors: Prof. L Ch-Kwog d Dr. Ch Jor-Tg Cotets Abstrct. Itroducto: Bckgroud.
More informationKURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.
KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces
More informationPatterns of Continued Fractions with a Positive Integer as a Gap
IOSR Jourl of Mthemtcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X Volume, Issue 3 Ver III (My - Ju 6), PP -5 wwwosrjourlsorg Ptters of Cotued Frctos wth Postve Iteger s G A Gm, S Krth (Mthemtcs, Govermet
More informationAlmost Unbiased Estimation of the Poisson Regression Model
Ecoometrcs Worg Pper EWP0909 ISSN 485-644 Deprtmet of Ecoomcs Almost Ubsed Estmto of the Posso Regresso Model Dvd E. Gles Deprtmet of Ecoomcs, Uversty of Vctor Vctor, BC, Cd V8W Y & Hu Feg Deprtmet of
More informationMathematics HL and further mathematics HL formula booklet
Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Mthemtcs HL d further mthemtcs formul boolet
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 informationRelations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels
Relatos to Other Statstcal Methods Statstcal Data Aalyss wth Postve Defte Kerels Kej Fukuzu Isttute of Statstcal Matheatcs, ROIS Departet of Statstcal Scece, Graduate Uversty for Advaced Studes October
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationThe definite Riemann integral
Roberto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 4 The defte Rem tegrl Wht you eed to kow lredy: How to ppromte the re uder curve by usg Rem sums. Wht you c ler here: How to use
More informationAdvanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University
Advced Algorthmc Prolem Solvg Le Arthmetc Fredrk Hetz Dept of Computer d Iformto Scece Lköpg Uversty Overvew Arthmetc Iteger multplcto Krtsu s lgorthm Multplcto of polyomls Fst Fourer Trsform Systems of
More informationChapter Unary Matrix Operations
Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt
More informationA Study on New Sequence of Functions Involving the Generalized Contour Integral
Globl Jourl of Scece Froter Reerch Mthetc d Deco Scece Volue 3 Iue Vero. Yer 23 Type : Double Bld Peer Revewed Itertol Reerch Jourl Publher: Globl Jourl Ic. (USA Ole ISS: 2249-4626 & Prt ISS: 975-5896
More informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More informationthis is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]
Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow
More informationMULTI-CRITERIA OPTIMIZATION BASED ON THE REGRESSION EQUATION SYSTEMS IDENTIFICATION
Mthetcl Modelg MUTI-CRITERIA OPTIMIZATION BASED ON THE REGRESSION EQUATION SYSTEMS IDENTIFICATION A.P. Koteo D.A. Pshe Sr Ntol Reserch Uverst Sr Russ Sr Stte Techcl Uverst Sr Russ Astrct. Cosder the prole
More informationCooper and McGillem Chapter 4: Moments Linear Regression
Cooper d McGllem Chpter 4: Momets Ler Regresso Chpter 4: lemets of Sttstcs 4-6 Curve Fttg d Ler Regresso 4-7 Correlto Betwee Two Sets of Dt Cocepts How close re the smple vlues to the uderlg pdf vlues?
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationDiscrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationChapter 2: Probability and Statistics
Wter 4 Che 35: Sttstcl Mechcs Checl Ketcs Itroucto to sttstcs... 7 Cotuous Dstrbutos... 9 Guss Dstrbuto (D)... Coutg evets to etere probbltes... Bol Coeffcets (Dstrbuto)... 3 Strlg s Appoto... 4 Guss Approto
More informationMathematics HL and further mathematics HL formula booklet
Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core Topc : Algebr Topc
More information6.6 Moments and Centers of Mass
th 8 www.tetodre.co 6.6 oets d Ceters of ss Our ojectve here s to fd the pot P o whch th plte of gve shpe lces horzotll. Ths pot s clled the ceter of ss ( or ceter of grvt ) of the plte.. We frst cosder
More informationMathematics HL and further mathematics HL formula booklet
Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Edted 05 (verso ) Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core 3 Topc : Algebr
More informationPOWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS
IRRS 9 y 04 wwwrppresscom/volumes/vol9issue/irrs_9 05pdf OWERS OF COLE ERSERIC I-RIIGOL RICES WIH COS I-IGOLS Wg usu * Q e Wg Hbo & ue College of Scece versty of Shgh for Scece d echology Shgh Ch 00093
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 informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationCS321. Introduction to Numerical Methods
CS Itroducto to Numercl Metods Lecture Revew Proessor Ju Zg Deprtmet o Computer Scece Uversty o Ketucky Legto, KY 6 6 Mrc 7, Number Coverso A geerl umber sould be coverted teger prt d rctol prt seprtely
More informationKeywords: Heptic non-homogeneous equation, Pyramidal numbers, Pronic numbers, fourth dimensional figurate numbers.
[Gol 5: M 0] ISSN: 77-9655 IJEST INTENTIONL JOUNL OF ENGINEEING SCIENCES & ESECH TECHNOLOGY O the Hetc No-Hoogeeous Euto th Four Ukos z 6 0 M..Gol * G.Suth S.Vdhlksh * Dertet of MthetcsShrt Idr Gdh CollegeTrch
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More informationCS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department
CS473-Algorthms I Lecture 3 Solvg Recurreces Cevdet Aykt - Blket Uversty Computer Egeerg Deprtmet Solvg Recurreces The lyss of merge sort Lecture requred us to solve recurrece. Recurreces re lke solvg
More information2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission
/0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationAsymptotic Statistical Analysis on Special Manifolds (Stiefel and Grassmann manifolds) Yasuko Chikuse Faculty of Engineering Kagawa University Japan
Asyptotc Sttstcl Alyss o Specl Mfols Stefel Grss fols Ysuo Chuse Fculty of Egeerg Kgw Uversty Jp [ I ] Stefel fol V ef { -fres R ; -fre set of orthoorl vectors R } ; ' I }. { Deso of V +. Ex: O V : orthogol
More informationInternational Journal of Advancements in Research & Technology, Volume 3, Issue 10, October ISSN
Itertol Jourl of Avceets Reserch & echology, Volue 3, Issue, October -4 5 ISS 78-7763 Mofe Super Coverget Le Seres Metho for Selecto of Optl Crop Cobto for Itercroppg Iorey Etukuo Deprtet of Sttstcs, Uversty
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 informationAcoustooptic Cell Array (AOCA) System for DWDM Application in Optical Communication
596 Acoustooptc Cell Arry (AOCA) System for DWDM Applcto Optcl Commucto ml S. Rwt*, Mocef. Tyh, Sumth R. Ktkur d Vdy Nll Deprtmet of Electrcl Egeerg Uversty of Nevd, Reo, NV 89557, U.S.A. Tel: -775-78-57;
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationStats & Summary
Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More information