Notation for Mixed Models for Finite Populations

Size: px
Start display at page:

Download "Notation for Mixed Models for Finite Populations"

Transcription

1 30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p Aular varabl dvato from ma for ut z for,..., Epctd Rpo (ovr rplcatd maurmt(otato 0 ud wth aular formato (Wu w for,..., whr w ( μ ( μ w Wght for ut. Wght for rgro paramtr (Luz r w for,..., Wghd pctd rpo for ut w μ ( for,..., Rgro approach (Luz r for,..., Rgro approach (Luz r A+ B Lac of ft rgro approach (Luz r b μ μ Slop for ut bad o coctg populato ma (Luz r Wth aurmt Error,..., r d for rplcato W or W (f r for,..., r aurmt rror for th rplcato of ut clutr-otato007.doc //007 5: P

2 30- W % or W % (f r for,..., r aurmt rror for th rplcato o th ut poto + W for,..., r aurmt for th rplcato. w Wghtd rpo for w th rplcato of ut Covto: U th ubcrpt R for vctor wh th ol radom compot maurmt rror. U th ubcrpt po for pctato ovr poto maurmt rror (Luz r U th ubcrpt ξ 3 for pctato ovr maurmt rror o ut clutr. U th ubcrpt ξ for pctato wth rpct to prmutato of ut a clutr. U th ubcrpt ξ for pctato wth rpct to prmutato of th clutr. U th ubcrpt ξ for pctato wth rpct to prmutato of th ut of a mpl populato. U th ubcrpt S for pctato wth rpct to prmutato of th ut of a mpl populato (Luz r. U a upr-crpt for vctor wh ut ad maurmt rror ar radom. t U a ovr-crpt U wh radom varabl ar padd. Vctor ( ( L Epctd rpo vctor (wth rpct to maurmt L Aular rpo for ut (Wu (( ( p ( ( L Aular rpo (Luz r (( ( L Aular rpo (Luz r (( z ( z0 z z z L p ( z z ( ( w w w w L w Vctor of wght clutr-otato007.doc //007 5: P

3 30-3 ( ( L Epctd wghtd rpo vctor (wth rpct to w w w w w ( ( R maurmt L aurd rpo vctor (for r for all,...,, ( ( L aurd wghtd rpo vctor (for r for all wr w w w w (( (,...,, W W W W L W aurmt rror vctor (for r for all,...,, α Gral vctor of paramtr. Cotat vctor wth poto ad zro lwhr Paramtr ad Cotat c c Avrag rmag coffct wh prdctg a ma ug aular + varabl ( c c Ud ESE of Rgro Prdctor (Luz r C + f Samplg fracto (Wu (but th ma b dfd dffrtl -tag amplg μ Uual dfto μ Dfto ud wth aular varabl, rgro. μ Rgro problm (Luz r μ Dfto ud wth gl rgro problm μ w w A μ Bμ Rgro trcpt clutr-otato007.doc //007 5: P 3

4 30-4 B ( μ ( μ ( μ Rgro paramtr for aocato. (Luz r ( μ or ( μ Rgro problm (Luz r ( μ Rgro problm (Luz r ( μ Rgro problm (Luz r w w ( w μ ( μ ( μ Rgro (Luz r ( ( μ μ ρ ( W var R ( W var R L aurmt rror varac aocatd wth ut % aurmt rror varac (.g. trvwr, laborator ( W var po aocatd wth poto (Ed % % aurmt rror varac aocatd wth poto (Luz (Ed r (Luz r clutr-otato007.doc //007 5: P 4

5 30-5 (Luz r w w + Radom prmutato hrag cotat for mpl radom amplg wth ut maurmt rror whr r for all,...,. % + Radom prmutato hrag cotat for mpl radom L amplg wth poto maurmt rror whr r for all,..., (Ed. + + Partall codtoal radom prmutato hrag cotat for mpl % ( ρ ( + + ρ % + ( ρ ( + ρ ( ρ ( % ρ + % % ( radom amplg wth poto maurmt rror whr r for all,..., (Ed. Cotat for Rg wth ut maurmt rror (Luz r Rgro problm (Luz r Cotat for Rg wth Poto rror (Luz r Ud b Luz r for Rgro + + D c + + c Cotat for Rgro wth maurmt rror (Luz r ( D % c + c + % Cotat for Rgro wth poto rror (Luz r clutr-otato007.doc //007 5: P 5

6 30-6 u u aurmt rror varac aocatd wth th ut ralzd poto. Vctor ad atrc (otc that w could df u u + L, whch m bttr. a a 0 L 0 0 L 0 ; O 0 0 L ; L L Ja aa ; O L a 0 L 0 0 a L 0 D a O 0 0 L ac Pa a J a a Pab, a J a b 0 R 0 P whr (( r ( c D a dagoal matr wth lmt Δ P whr w ot that L L L a a a a L a. A dagoal matr. o th dagoal. Ud to ubtract ma rgro (Luz r Δ ad w w Δw ud to collap a partto radom varabl, uch a L ( 0 L 0 ( clutr-otato007.doc //007 5: P 6

7 30-7 K K ud to partto radom varabl to ampl, rmadr, uch that K K ( 0 K 0 ( rgro (Luz r g g g cotat to dtrm targt ( c c c Cotat to dtrm targt wth aular varabl (Wu. ( ( c c c c L c Parttod cotat for ampl (Wu ( ( C c c c L c Parttod Cotat to df rgro targt (Luz r (( c + ( c + c + c c L Parttod cotat for rmadr (Wu (( c + ( c + c + c C L Parttod Cotat to df rgro targt (Luz r ( C c 0 Parttod lar combato for ampl wth aular p ( ( p varabl (Wu C c 0 Parttod lar combato for rmag wth aular varabl (Wu Parttod vro of dcator vctor for poto. ( G 0 Epro mlar to dg matr wth aular ampl radom varabl p g C 0 Parttod lar combato for ampl wth rgro (Luz ( ( ( p r G 0 Epro mlar to dg matr for rmadr radom varabl. G GK Sampl lar combato to df targt b Vvaa S R S G GK Rmadr lar combato to df targt b Vvaa R ( μ μ (Wu ( μ μ 0 (Wu z p clutr-otato007.doc //007 5: P 7

8 30-8 ( μ μ μ (Luz r X ( z( z Σ z μ z μ X ΣX Σ Varac matr (Luz r o o Σ o o vr varac for rgro ud b Luz r ( p (( L Σ ρ X X X X X X Σ Squard multpl corrlato coffct of o X ( β β β β Σ L Rgro paramtr btw rpo ad aular varabl β X X p μ, o that + ad μ β β β β L β (Doubl dfto!. Prmutato Radom Varabl-,..., Poto th prmutato. U for,..., ;,..., dcator of lcto of ut poto. u for,..., ;,..., Ralzato of th radom varabl U for a prmutato S U for,..., Radom varabl rprtg labl for ut poto U for,..., Radom varabl rprtg pctd rpo for ut w w poto U for,..., Radom varabl rprtg pctd wghtd rpo X U for,..., Rgro approach (Luz r clutr-otato007.doc //007 5: P 8

9 30-9 X X μ ( Rgro approach (Luz r tar X Rgro approach (Luz r tar + + Rpo for th ut poto (for U W + r for all,..., wh thr maurmt rror (Luz r. + u W or + + W % Partall codtoal rpo (Ed (Doubl dfto! X Rgro approach (Luz r tar + + U or Rpo for th ut poto (for r for all,..., wh thr maurmt rror (Ed. w Uw % U + W % or % (wh Rpo for th ut poto wh thr W UW W % or poto (.. trvwr rror or W UW Rplcato rror for th ut poto (for r W % (f r for all,..., aurmt rror aocatd wth Poto (.. trvwr for th ut poto. W% W% X Rgro problm (Luz r ( ( U U U U L U ( ( u u u u L u Ralzato of th vctor U. clutr-otato007.doc //007 5: P 9

10 30-0 ( L U U U U t t t W Epadd Radom Varabl U U U W for,..., ad,..., Radom varabl rprtg for ut ad poto a prmutato. for,..., ad,..., Radom varabl rprtg rpo for ut ad poto a prmutato. for,..., ad,..., Radom varabl rprtg maurmt rror for th rplcato for ut ad poto a ( ( t U U U L U or prmutato. t U (( ( ( ( t U + W U + W U + W L U + W t t t W U or + ( + W t t t t vc ( t L or vc ( + W whr r for all,..., U t t t t vc L or ( t vc U ( ( W t UW UW UW L U W or t W W U whr r for all,..., Collapd Radom Varabl t t L A B L ud to collap radom varabl, a for ampl ( (( ( L or U ( ( L or U w w w w w clutr-otato007.doc //007 5: P 0

11 (( ( L or (( ( L or W wh r for all,..., (Ed UW wh r for all,..., (Luz r % + W% Rgro problm wth poto rror, (Luz r (( ( w w w w L w (( ( W L or W W W W (( ( ( ( L w w w w w W UW wh r for all,..., W % W % W % W % L W % wh r for all,...,, poto maurmt rror (Luz X U Z U z z U r Z Uz + + Z vc( X Rgro problm wth maurmt rror (Luz r Z% vc( % X Rgro problm wth poto rror (Luz r ( E + vc E + E Rdual rgro (Luz r E μ aurmt rror (ut calld Rpo Error (Luz r + + E % % μ aurmt rror (poto (Luz r E X μ Dvato from ma (Luz r Sampl ad Rmadr,..., Sampl Sampl total of Epctd Rpo Rmadr total + Sampl total of aurd rpo whr wh r for all,..., clutr-otato007.doc //007 5: P

12 30- + Rmadr total of aurd rpo whr wh r for all,..., w Sampl total for wghtd pctd rpo w w Rmadr total for wghtd pctd rpo w + w w Sampl total wghtd maurd rpo wh r for all,..., w w + Rmadr total wghtd maurd rpo wh r for all,..., Sampl avrag Rmadr avrag + X X Sampl ma of aular varabl (Wu X X Sampl ma for o aular varabl (Luz r or Sampl avrag of aurd rpo whr wh r for all,..., + Rmadr avrag of aurd rpo whr wh r for all,..., + + Sampl ma for Rgro prdctor wth ut maurmt rror (Luz + + r Rgro problm (Luz r Vctor clutr-otato007.doc //007 5: P

13 30-3 ( ( L (( ( + + L ( ( L w w w w w ( w ( + w ( + L w w w ( ( L R ( ( ( + ( + L R ( ( L wr w w w w ( w w ( ( + ( + L wr w w ( ( L ( ( L ud b Vvaa S (( ( + + L (( ( + + L ud b Vvaa R (( ( L whr ( all,..., 0 wh r for ( ( ( + ( + L whr 0 ( wh r for ( ( L w w w w w ( w ( + w ( + L w w w (( ( L w w w w w ( w w ( ( + ( + L w w w all,..., clutr-otato007.doc //007 5: P 3

14 30-4 Z Z S R Z0 Z L Zp Z0 Z L Zp O Z0 Z Z L p Z0, + Z, + L Zp, + Z0, + Z, + L Zp, + O Z0, Z, Z L p, Z vc Z Sampl (Wu S whr S ( Z 0 Z whr Z R ( 0 Z Z vc Z Rmadr (Wu R Z K RZ Sampl radom varabl rgro (Luz r + + Z K RZ Rmadr radom varabl rgro (Luz r + + ( ( X μ ( X μ ( X μ Z % % % L % L Rgro wth poto rror (Luz r Epctd Valu, Targt, Prdctor P g Targt problm wth a mpl populato θ c c Targt problm wth aular varabl, b Wu. θ G Targt doma problm, b Vvaa θ G Altratv targt doma problm, b Vvaa c T c Targt rgro problm wth rror b Luz r P ˆ g + a Bt lar ubad prdctor of targt P ˆ wz Bt lar ubad prdctor of targt wth aular varabl (Wu ˆ ( ˆ g a Z Rgro prdctor (Luz r T a Cotat to dtrm to optmall prdct th targt (, + + ( f a λ ava g V a g V g ax g X λ Fucto to mmz for ESE λ, LaGraga multplr clutr-otato007.doc //007 5: P 4

15 30-5 {, (, } wˆ V V + G GV G G GV V C c ( f β, ESE cotat wth aular ˆ a V V X X V X X V V g + V X X V X X g ESE cotat + ˆ D + a ( C + c ESE cotat for rgro wth rror (Luz r D% a% % c( % + c Rgro (Luz r ˆ R R RS S SR R + R R RS S S RS S SR R RS S S R θ G V V V V G G X V V X V V V X V V X G -optmal var ( ˆ var ( ˆ θ θ -optmalt crtra for X α E ξ X ˆθ, a lar, ubad prdctor X β S S E R XR Epctd valu ud b Vvaa for doma problm E ξ R X var X V α V, ξ V, V S VS VSR var R VRS VR Varac ud b Vvaa for doma problm. var var ξ R V V, V, V V V V, V V V V, V, V ξ R, V V V R clutr-otato007.doc //007 5: P 5

16 30-6 V ( + J + 0 V Σ P, Rgro parttod varac (Luz r + 0 V Σ P, Rgro varac (Luz r + V, Σ Rgro varac (Luz r Othr Trm Dfd for Prdctor αˆ X V X X V αˆ X V X X V ˆ X V X X V α % μ % % Wghtd ampl ma wth maurmt rror aocatd wth poto % + + % μ % Wghtd ampl ma for partall codtoal RP modl wth maurmt + rror μ + + Rgro problm (Luz r μ X X Rgro problm (Luz r ˆ μ a ud SE for Rgro Etmator of Luz r ( X μ + + Bˆ X ( μ + + Rgro problm (Luz r clutr-otato007.doc //007 5: P 6

17 30-7 B% μ + ( μx μ Rgro problm (Luz r ( ρ ( + ρ Rgro problm, (Luz r U Rgro problm (Luz r Clutrd/Stratfd Populato Ut ad Rpo,..., Clutr Labl (ud b Ed t,..., Ut labl for clutr (ud b Ed,..., J Stratum labl (ud b Vvaa {,,, } L Ut labl for tratum (ud b Vvaa,..., Doma labl ach tratum umbr of ut doma tratum umbr of ut th populato,..., r t d for rplcato t for,..., ; t,..., Epctd Rpo for ut t clutr for,..., J ;,..., Epctd Rpo for ut tratum w Wght for th ut poto clutr (Ed w w Wght pcal ca that all poto wght ar qual clutr (Ed W t for,..., ; t,..., ;,..., r Rpo rror. for clutr th rplcato of ut t clutr-otato007.doc //007 5: P 7

18 30-8 t t + Wt for,..., r Rpo for th rplcato of ut t clutr Vctor L Epctd rpo for ut clutr (( ( t (( ( L Epctd rpo for ut tratum (Vvaa (( ( L Epctd rpo for all ut ad clutr. ε L Dvato of ut t from pctd valu of clutr (( ε ( ε ε ε t (( ( ε ε ε ε L ε Vctor of dvato of ut from clutr pctd ( ( t valu W W W W L W Rpo rror vctor (for r t for all t,..., (( ( for clutr W W W W L W Rpo rror vctor for all ut ad clutr (for r t for all,..., ; t,...,. Paramtr ad Cotat m Sampl z for clutr (Ed Sampl z for tratum (Vvaa f m K 0 0 ( m atr to form ampl rpo vctor wh m ( m all,..., for K 0 0 m ( ( m m 0 ( atr to form rmadr rpo vctor wh for all,..., clutr-otato007.doc //007 5: P 8

19 30-9 ( K K K X K X wm X Ud for ampl Partall collapd uqual z modl (Ed X X K X wm w( m Ud for rmadr Partall collapd uqual z modl (Ed g c Two-tag ubalacd problm (Ed g K g g c Coffct for partall padd ampl (Ed g K g g c c Coffct for partall padd rmadr (Ed (,, g g g 0 m m g, g 0 ( m 0 g g (, G S G R Cotat to df targt tratfd doma problm for ampl (Vvaa Cotat to df targt tratfd doma problm for rmadr (Vvaa ( ( ( g g g g g L Cotat multplr to df targt wh,..., for all clutr-otato007.doc //007 5: P 9

20 30-0 ( ( g g g g L g Cotat multplr to df targt (( ( b b b b L b Vctor of cotat to df targt b b b ( μ t a for clutr (Ed d t w μ Ud partall collapd uqual clutr z (Ed β a for tratum (Vvaa μ μ β μ μ ε t t μ o that t μ + β + εt μ μ ( μ Varac of ut clutr (Ed t t v f w f Ud partall collapd padd uqual clutr (Ed ( μ Varac of ut tratum (Vvaa Avrag ut varac ovr clutr t t Avrag rplcato varac ovr ut clutr r t t clutr-otato007.doc //007 5: P 0

21 30- Othr Varac for Othr d odl Prdctor v + varac for md modl prdctor m w / v / v Wght for varac md modl prdctor Shrag cotat for md modl prdctor v δ Varac of clutr ma for Scott ad Smth prdctor v + Varac for Scott ad Smth prdctor δ m w / v / v Wght for varac Scott ad Smth prdctor mδ mδ + Shrag cotat for Scott ad Smth prdctor Varac paramtr dfd for radom prmutato modl ρ + ρt + r tra cla corrlato of clutr tra cla corrlato of ut wth rplcato rror m m + + ( r Radom prmutato hrag cotat wth rplcato rror whr ad m m for all,..., m m + Radom prmutato hrag cotat for -tag clutr amplg wth o maurmt rror whr ad m m for all,...,. clutr-otato007.doc //007 5: P

22 30- r m + m + + r Addtoal radom prmutato hrag factor wh whr ad m m for all,..., d d v + ( Partall collapd uqual clutr cotat (Ed Avrag ug partall collapd uqual clutr cotat (Ed d Cotat for partall collapd uqual clutr (Ed d Vctor ( ( μ μ μ μ L μ Clutr ma (Ed ( β β β β L Vctor of clutr ffct (Ed ( β β β β L Vctor of trata ma (Vvaa J X Fd paramtr dg matr Z Radom ffct dg matr (( w ( w w w w L Wght for poto clutr (Ed ( m 0 m t C ( 0 m m t t t g ( p g C CC Partall Collapg matr (Ed Lar cotat for targt to appl to partall collapd radom varabl (Ed t t t t Pt C CC C C Ortho-complmt of partal collapg (Ed Prmutato Radom Varabl,..., Poto prmutato of clutr,..., Poto prmutato of ut clutr clutr-otato007.doc //007 5: P

23 30-3 υ,..., Poto prmutato of ut tratum U for,..., ;,..., dcator of lcto of clutr poto. t U for,...,, t,..., ad,..., dcator of lcto of ut t poto ( U υ for,..., clutr υ,,..., ad,..., J dcator of lcto of ut poto υ tratum (Vvaa % U Epctd rpo SSU clutr t t t ( υ U Rpo of ut poto υ of tratum υ % w U or w t t t % U Epctd wghtd rpo for SSU clutr (Ed w w U U t t t + W B U β Vctor ad atrc ( U U U U L U t t t t t U U U U L U t U U U U L U Prmut ut clutr (Ed U ( ( ( ( L U U U ( ( ( ( U U U ( ( ( ( ( U U U U υ Prmut ut tratum (Vvaa. clutr-otato007.doc //007 5: P 3

24 30-4 ( ( U U U U L U ot that th a colum of U, whl U a row of U (( ( L U U U U U U U U Parttod clutr prmutato matr (Ed ( ( U U U U L U Sampl porto of clutr prmutato matr (Ed ( ( + + U U U U L U Rmadr porto of clutr prmutato matr (Ed (( ( ( (( υ % % % % L % or % U % Prmutd ut clutr (Ed ( ( U Prmutd ut tratum (Vvaa ( ( J L Vctor of trata wth prmutd ut (Vvaa (( ( L % % % % % w w w w w ( ( L Wh ( ( for all,...,. L Wh for all,...,. or ( ( U U (( E ( E U U ε Wh for all,..., (( W ( W U U W Wh (( ( B B B B L B or B Uβ + W T g Targt for all,..., ad wh,..., r t ad r t for all,..., ; t,..., ;.. T A g Targt wth maurmt rror T g t Targt (-tag uqual (Ed w clutr-otato007.doc //007 5: P 4

25 30-5 T g t Targt (-tag partall collapd (Ed p wp Epadd Radom Varabl Vctor t (( U % ( U % U % L U % w w w w w t t t E E ξ ξ w w w w w w w w (Ed t t t t t L (Ed ( ( ( ( L t t t t t E E E E E t w w w w w wp t t C w Partall Collapd Epadd radom varabl (Ed Sampl ad Rmadr umbr of ut ampl tratum (Vvaa umbr of ut doma tratum (Vvaa m umbr of ut ampl for th PSU poto ud md modl umbr of ut th PSU poto ud md modl f m amplg fracto of SSU th PSU poto ud md modl m f whr ad m m for all,..., (Ed U dcator of cluo of clutr th ampl (Ed t t m t U Partall collapd uqual clutr (Ed Uw ˆ Wghtd ampl ma for PSU (partall collapd uqual (Ed ˆ ˆ Avrag for ampl partall collapd uqual clutr (Ed clutr-otato007.doc //007 5: P 5

26 30-6 m m whr ad m m for all,..., ad,..., r t whr r t for all,..., ; t,..., m m whr ad m m for all,..., m m whr ad m m for all,..., ad,..., r t whr r t for all,..., ; t,..., m m whr ad m m for all,..., Vctor K K K K ( ( L ( ( L Epadd Radom Varabl Vctor t t 0 ( w wp t t w 0 ( t t E 0 E ( w w wp Sampl radom varabl (Ed Rmag radom varabl (Ed Parttod ampl rdual (Ed clutr-otato007.doc //007 5: P 6

27 30-7 t t Ew 0 Ew ( Parttod rmadr rdual (Ed t V w V, varξξ t w V, V Partall collapd uqual clutr z (Ed V fd fd + f v V, J ( ( fd f d 0 J ( P (Ed fd fd P f v 0 ( J 0 J ( ( fd P fd fd ( f d P 0 ( V 0 ( + 0 f v J P ( ( f d ( f d P ( f dp fd (Ed Epctd Valu E ξξ ξ 3 X μ X S Sampl porto of dg matr for tratfd doma (Vvaa X R Rmadr porto of dg matr for tratfd doma (Vvaa var var ξξ V V, V, V V V V, V V V V, V, V ξξ ξ3, V V + r m clutr-otato007.doc //007 5: P 7

28 30-8 Etmator/Prdctor m m dcat th avrag of SSU for th PSU ralzd poto a ampl. Th otato ud wth md modl. ˆ μ w wghtd ma ud md modl. T ˆ L t Prdctor for partall collapd uqual clutr (Ed w, ˆ L g + V V X X V X X V V g + V X X V X X g Pc + c f f BLUP (Ed ˆ ˆ ˆ T c c + c w ( Partall collapd uqual clutr prdctor (Ed αˆ X V X X V ( SE r ˆ ma 0, ˆ ma 0, SB SE f ˆ + m ˆ ma 0, [ SB SE] m clutr-otato007.doc //007 5: P 8

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Note on the Computation of Sample Size for Ratio Sampling

Note on the Computation of Sample Size for Ratio Sampling Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of

More information

Introduction. Generation of the Population Data

Introduction. Generation of the Population Data Ovrvw of th Smulaton Study for Prformanc of Unqual Sz Clutrd Populaton Prdctor of a Ralzd Random Clutr an (updat of c05d30.doc wth mplr notaton) Ed Stank ntroducton W dcrb a mulaton tudy mlar to th tudy

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

Estimation Theory. Chapter 4

Estimation Theory. Chapter 4 Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,

More information

Lecture 5. Estimation of Variance Components

Lecture 5. Estimation of Variance Components Lctur 5 Etmato of Varac Compot Gulhrm J. M. Roa Uvrt of Wco-Mado Mxd Modl Quattatv Gtc SISG Sattl 8 0 Sptmbr 08 Etmato of Varac Compot ANOVA Etmato Codr th data t blow rlatd to obrvato of half-b faml of

More information

Ordinary Least Squares at advanced level

Ordinary Least Squares at advanced level Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

(( ) ( ) ( ) ( ) ( 1 2 ( ) ( ) ( ) ( ) Two Stage Cluster Sampling and Random Effects Ed Stanek

(( ) ( ) ( ) ( ) ( 1 2 ( ) ( ) ( ) ( ) Two Stage Cluster Sampling and Random Effects Ed Stanek Two ag ampling and andom ffct 8- Two Stag Clu Sampling and Random Effct Ed Stank FTE POPULATO Fam Labl Expctd Rpon Rpon otation and tminology Expctd Rpon: y = and fo ach ; t = Rpon: k = y + Wk k = indx

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

ANOVA- Analyisis of Variance

ANOVA- Analyisis of Variance ANOVA- Aalii of Variac CS 700 Comparig altrativ Comparig two altrativ u cofidc itrval Comparig mor tha two altrativ ANOVA Aali of Variac Comparig Mor Tha Two Altrativ Naïv approach Compar cofidc itrval

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.

More information

HANDY REFERENCE SHEET HRP/STATS 261, Discrete Data

HANDY REFERENCE SHEET HRP/STATS 261, Discrete Data Bary prdctor Bary outcom HANDY REFERENCE SHEE HRP/SAS 6, Dscrt Data x Cotgcy abls Dsas (D No Dsas (~D Exposd (E a b Uxposd (~E c d Masurs of Assocato a /( a + b Rs Rato = c /( c + d RR * xp a /( a+ b c

More information

Note: Torque is prop. to current Stationary voltage is prop. to speed

Note: Torque is prop. to current Stationary voltage is prop. to speed DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

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

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

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

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

Lecture Notes 2. The ability to manipulate matrices is critical in economics. Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets

More information

MIXED EFFECTS MODELS

MIXED EFFECTS MODELS MIED MODELS BLUP MIED EFFECS MODELS Up to ow w dcd modl cldg fxd ffct ol. Frqtl, howvr, lar modl cota alo factor who lvl rprt a radom ampl of a poplato of all pobl factor lvl. Modl cotag both fxd ad radom

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

A Stochastic Approximation Iterative Least Squares Estimation Procedure Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar

More information

minimize c'x subject to subject to subject to

minimize c'x subject to subject to subject to z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto   {t-asano, School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp

More information

THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION

THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION Joural of Stattc: Advac Thory ad Applcato Volum 4 Numbr 5 Pag - Avalabl at http://ctfcadvac.co. DOI: http://dx.do.org/.864/jata_746 THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED

More information

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

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

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

(since y Xb a scalar and therefore equals its transpose)

(since y Xb a scalar and therefore equals its transpose) Backgroud: Prvoul w lard rort of tmator wh w orv d data {w } draw from om dtruto P th tmator ar fucto of th data w ow w wll orv data {w } { } from om dtruto P whr w thk of a th outcom/ddt varal ad a th

More information

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,

More information

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error.

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error. Drivatio of a Prdictor of Cobiatio # ad th SE for a Prdictor of a Positio i Two Stag Saplig with Rspos Error troductio Ed Stak W driv th prdictor ad its SE of a prdictor for a rado fuctio corrspodig to

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. The Multivariate Gaussian/Normal Distribution: (,,...

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. The Multivariate Gaussian/Normal Distribution: (,,... Fall 0 alss of Ertal Masurts B. Esst/rv. S. Errd h Multvarat Gaussa/oral Dstrbuto: Gv ctrd (.. ˆ 0 ) rado varabls,,... : wth: ˆ E [] ˆ 0 ˆ ( th zro {aka th ull} vctor) Suos th ar all Gaussa/orall-dstrbutd

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL 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 information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

Estimating the Population Mean From a Simple Random Sample When Some Responses. are Missing

Estimating the Population Mean From a Simple Random Sample When Some Responses. are Missing Etmatg the Populato Mea From a Smple Radom Sample Whe Some Repoe are Mg Edward J. Staek, Jgog Lu, Reca Yucel, ad Elae Puleo Departmet of Botattc ad Epdemology 40 Arold Houe Uverty of Maachuett Amhert,

More information

The expected value of a sum of random variables,, is the sum of the expected values:

The expected value of a sum of random variables,, is the sum of the expected values: Sums of Radom Varables xpected Values ad Varaces of Sums ad Averages of Radom Varables The expected value of a sum of radom varables, say S, s the sum of the expected values: ( ) ( ) S Ths s always true

More information

ε. Therefore, the estimate

ε. Therefore, the estimate Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model

More information

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR amplg Theory MODULE II LECTURE - 4 IMPLE RADOM AMPLIG DR. HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR Estmato of populato mea ad populato varace Oe of the ma objectves after

More information

Chapter 4: Model Adequacy Checking

Chapter 4: Model Adequacy Checking Catr : Modl Adquacy Cckg I t catr, w dcu om troductory act of modl adquacy cckg, cludg: Rdual Aaly, Rdual lot, Dtcto ad tratmt of outlr, T PRE tattc Ttg for lack of ft. T major aumto tat w av mad rgro

More information

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

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

More information

A Note on Estimability in Linear Models

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

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) 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 information

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING) Samplg Theory ODULE X LECTURE - 35 TWO STAGE SAPLIG (SUB SAPLIG) DR SHALABH DEPARTET OF ATHEATICS AD STATISTICS IDIA ISTITUTE OF TECHOLOG KAPUR Two stage samplg wth uequal frst stage uts: Cosder two stage

More information

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution:

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution: Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Do fv of problm

More information

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure

More information

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam.

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam. Chmtry 30 Sprg 08 Eam : Chaptr - Nam 00 Pot You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Th tak-hom lat-quar problm wll accout for pobl pot

More information

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties . Calculato o qulbrum Prorts. hrmodamc Prorts mratur, Itral rg ad Prssur Fr rg ad tro. Calculato o Damc Prorts Duso Coct hrmal Coductvt Shar scost Irard Absorto Coct k k k mratur m v Rmmbr hrmodamcs or

More information

Estimation of the Present Values of Life Annuities for the Different Actuarial Models

Estimation of the Present Values of Life Annuities for the Different Actuarial Models h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld

More information

Estimating Realized Random Effects in Mixed Models

Estimating Realized Random Effects in Mixed Models Etimating Realized Random Effect in Mixed Model (Can parameter for realized random effect be etimated in mixed model?) Edward J. Stanek III Dept of Biotatitic and Epidemiology, UMASS, Amhert, MA USA Julio

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

Lecture 12: Multilayer perceptrons II

Lecture 12: Multilayer perceptrons II Lecture : Multlayer perceptros II Bayes dscrmats ad MLPs he role of hdde uts A eample Itroducto to Patter Recoto Rcardo Guterrez-Osua Wrht State Uversty Bayes dscrmats ad MLPs ( As we have see throuhout

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

The Mathematics of Portfolio Theory

The Mathematics of Portfolio Theory The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock

More information

MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N

MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 9 4. Matrx tgrals Lt h N b th spac of Hrmta matrcs of sz N. Th r product o h N s gv by (A, B) = Tr(AB). I ths scto w wll cosdr tgrals of th form Z

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Useful Statistical Identities, Inequalities and Manipulations

Useful Statistical Identities, Inequalities and Manipulations Useful Statstcal Iettes, Iequaltes a Mapulatos Cotets Itroucto Probablty Os, Log-Os PA ( ) π Let π PA ( ) We efe the Os Rato θ If θ we say that the patet s three PA ( ) π tmes more lely to have the sease

More information

Section 2 Notes. Elizabeth Stone and Charles Wang. January 15, Expectation and Conditional Expectation of a Random Variable.

Section 2 Notes. Elizabeth Stone and Charles Wang. January 15, Expectation and Conditional Expectation of a Random Variable. Secto Notes Elzabeth Stoe ad Charles Wag Jauar 5, 9 Jot, Margal, ad Codtoal Probablt Useful Rules/Propertes. P ( x) P P ( x; ) or R f (x; ) d. P ( xj ) P (x; ) P ( ) 3. P ( x; ) P ( xj ) P ( ) 4. Baes

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.

More information

Simple Linear Regression and Correlation.

Simple Linear Regression and Correlation. Smple Lear Regresso ad Correlato. Correspods to Chapter 0 Tamhae ad Dulop Sldes prepared b Elzabeth Newto (MIT) wth some sldes b Jacquele Telford (Johs Hopks Uverst) Smple lear regresso aalss estmates

More information

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

Training Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ

Training Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ Stat 74 Estmato for Geeral Lear Model Prof. Goel Broad Outle Geeral Lear Model (GLM): Trag Samle Model: Gve observatos, [[( Y, x ), x = ( x,, xr )], =,,, the samle model ca be exressed as Y = µ ( x, x,,

More information

Logistic Regression Sara Vyrostek Senior Exercise November 16, 2001

Logistic Regression Sara Vyrostek Senior Exercise November 16, 2001 ogstc Rgrsso Sara Vrostk Sor Ercs Novmbr 6, Itroducto: I th modlg of data, aalsts dvlop rlatoshps basd upo th obsrvd valus of a st of prdctor varabls ordr to dtrm th pctd valu of th rspos varabl of trst,

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 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

Text: WMM, Chapter 5. Sections , ,

Text: WMM, Chapter 5. Sections , , Lcturs 6 - Continuous Probabilit Distributions Tt: WMM, Chaptr 5. Sctions 6.-6.4, 6.6-6.8, 7.-7. In th prvious sction, w introduc som of th common probabilit distribution functions (PDFs) for discrt sampl

More information

Almost unbiased exponential estimator for the finite population mean

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

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL IJRRAS (3) Ju 22 www.arpapr.com/volum/voliu3/ijrras 3_5.pdf ON RANKING OF ALRNAIVS IN UNCRAIN GROUP DCISION MAKING MODL Chao Wag * & Lag L Gul Uvrty of chology Gul 544 Cha * mal: wagchao244@63.com llag6666@26.com

More information