Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede

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1 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede We now reformulate the lnear Least Squares ethod n more general terms, sutable for (eventually extendng to the non-lnear case, and also such that t wll be convenent for computer codng. We are gven a set of random varables (, y( x,, y( x whose measurements may or may not be ndependent. he covarance matrx of the measurements s apror nown: V = E[( ( ] ( ( ( j( j j ( j j σ cov y x, cov, y x cov ( y( x, σ cov ( y x, y x or: V = cov ( y( x, cov ( y( x, y( x σ y( x ( We assume that the nverse of the covarance matrx of the measurements V exsts. We also assume that some sort of a theory predcton apror exsts, from whch we can obtan, estmates of the expectaton (.e. true values of the y( x; y ( x ;, y ( x ;,, y ( x ; n terms of parameters: ( ( Here, the x ( x, x,, x the measurements (, y( x,, y( x,,, are a set of apror precsely nown x-ponts assocated wth the We then replace our (usual χ. {relevant for ndependent measurements}: wth: χ ( ( y( x y( x; σ = y x ( ( y( x y( x V yj xj yj xj j = j= χ ; ; whch can be wrtten n matrx notaton as: χ ; V ; = R x; V R x; In the case of ndependent measurements, both V and V ( V δσ j y( x j = V σ y( x Also recall that the lelhood ( ( are dagonal: = and: V = δj σ V e χ L. j = σ y( x 598AE Lecture Notes 9

2 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede We defne the column vectors: y y y ( x ( x ( x and: y( x; y y y ( x ; ( x ; ( x ; and defne the column vector: We wll FIRS treat the case where the y( x; predctons are lnear n the -parameters,.e.: where the ( ; = h x = h x ( =,,, are a set of gven/apror-specfed functons, e.g. the polynomals x, etc. hus, we can treat the ( a matrx hen, the x, or the Legendre polynomals, h x as a gven/apror-specfed set of numbers, and defne ( H x h ( x, where =,,, and =,,,. column vector theory predcton y ( x, h ( x product of the H x matrx wth the Wrtng ths out explctly: (, hen, the χ sum: (, (, = can be wrtten as the = column vector : y( x; H( x H( x h x h x h x h x h x h x = = y ( x, h x h x h x =. ( ; V ( y( x; R ( ; ( ; x V R x χ = Usng: y ( x, = H( x, the χ expresson can then be wrtten as: ( H x V ( H( x R ( ; ( ; x V R x = χ 598AE Lecture Notes 9

3 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede We mnmze ths χ χ expresson by dfferentatng w.r.t. each of the -parameters: ( {( H( x V ( H( x } = = 0 ( H( x {( H( x V } { V ( y( x H( x } = ( H( x + = 0 hen: ( H( x ( H( x y( x ( H( x ( H( x H x = = for = : y( x ( H( x ( H( x H ( x H ( x δ = H ( x H ( x H ( x δ H x = = = = H ( x H ( x H ( x H ( x δ = hus: H x H ( x H x = H ( x = ( th column of matrx H( x, = : 598AE Lecture Notes 9 3

4 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede Hence, we can wrte χ ( χ ( as: Note that for any symmetrc matrx ( ( H x H x H x V H x = B ( H( x V + H x H ( x H ( x = B, and thus: ( ABC = CB ( A = CBA. he nverse covarance matrx of the measurements V s symmetrc, thus the quantty: H ( x H ( x ( H( x V H ( x =( H ( x H x H x V H x However, note that the above matrx product s ( ( ( ( = = a scalar quantty, whch s nvarant under the transpose operaton!!! 598AE Lecture Notes 9 4

5 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede hus: and hence: χ ( H ( x H ( x ( H( x V H ( x =( H ( x H x H x V H x ( H( x V = H x H ( x H ( x ( H x H x H x V H x ( = hs result s n fact the th component of an -dmensonal gradent of χ -parameters! hus, defnng the -gradent as a column vector: We can then wrte the above χ ( relaton compactly as a column vector: H ( x V H x = χ where: = : w.r.t. the 598AE Lecture Notes 9 5

6 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede Hence, we can wrte the condton for mnmzng χ ( as a column vector as: or: H ( x V H x = = χ 0 = = H x V H x V H x H x V H x But as we have already shown n the 598AE Lect. Notes 8, the elements of the U x are: symmetrc matrx hus: ( U ( x H ( x V H( x j h x hj x j = σ y( x H x V = H x V H x = H x V H x U x ( If we assume that the symmetrc matrx U x H ( x V H( x s non- sngular, (.e. { U x } H ( x V H( x det det 0 then ts symmetrc nverse U x H x V H x exsts. ultplyng the above equaton on the LHS by the matrx U( x H ( x V H( x, we obtan: ( = U x H x V = H x V H x H x V = U x G x where the matrx G x H x V. Next, we wll use error propagaton to determne the estmates of the -parameters. covarance matrx V of the 598AE Lecture Notes 9 6

7 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede In 598AE Lect. Notes 5 (p. 7, we were gven a set of N random varables x ( x, x,, xn and ther N N covarance matrx V ˆ E[( xxˆ( x xˆ ]. We then showed that for a set of K new random varables gven by the functons (,,, N (,,, u x x x u x x x U uk x x x (,,, u u the K K covarance matrx of the new varables was V ˆ [( ˆ( ˆ u E uu u u ] = V xˆ xˆ xˆ where: u( x u( x u( x x x ˆ x x xˆ N xˆ u( x u( x u( x u x x xˆ x xˆ N xˆ xˆ uk ( x uk ( x uk ( x x x xˆ x xˆ N xˆ x N N In our stuaton here, ths becomes: V V ( ( Snce = H ( x V H( x H x V s a lnear functon of y ( x hen: V V ( H x V H x H ( x V V = ( ( = H x V H x H x V H x H ( x V H( x = ( H ( x V H( x U( x ( H x V H x H ( x V ( V = H x V H x U x s the covarance matrx of the estmates hus: of the -parameters. 598AE Lecture Notes 9 7

8 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede Example: Ft to a arabola (aa Quadratc Regresson : Suppose we have 4 ndependently measured data ponts y ( x (, y( x, y3( x3, y4( x4 to be ft to a 3-parameter theory predcton for a parabola: y ( x + x+ 3x,.e. where h ( x =, h ( x = x and h3 ( x = x, and wth specfed/apror-nown -standard devaton uncertantes σ y ( x. We mae a table of numbers, as before: 3 4 x y σ ( x (Hmmm... déjà vu: we ve seen ths before! Now: 5 3 = 5 8 V and: = and: V = he theory predcton s y ( x; x x x x = + + = + +, thus: 3 3 H x x x x x = = x3 x x4 x hen: = = x x x3 x and: H ( x x x x3 x4 Vˆ = U x H x V H x = = ( same as before!!! AE Lecture Notes 9 8

9 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede ˆ = = Usng these matrx results, then: and thus: V U( x H ( x V H( x ( ( = H x V H x H x V = U x H x V = = 3.7 ( same as before!!! 7.8 It should now be clear that the above matrx technque for solvng LSQ ft problems s partcularly well-suted to wrtng computer code to carry out the needed mathematcal operatons, gven a set of matrx algebra routnes. Now we can derve some ropertes of Lnear Least Squares Estmators: ˆ From: = U x H ( x V where: he expectaton value of the matrx of U x H x V H x = V estmates s: ( E E U x H x V U x H x V E [ ] = [ ] = [ ] Recall that our theory predcton s y ( x, = H( x and that the ( ; be the true values (.e. expectaton values of the measurements y ( x. We would obtan these f we new ˆ, the true (expectaton values of the -parameters. [ ] [ ] [ ] E = E H( x = H( x E = H( x ˆ are supposed to hen: ( ( ( U( x H ( x V H( x ( H ( x V H( x ( H ( x V H( x [ ] = [ ] = [ ] E E U x H x V U x H x V E = ˆ = ˆ = ˆ s an unbased estmator of ˆ, the true (expectaton values of the -parameters. 598AE Lecture Notes 9 9

10 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede Furthermore, the above formula for nvolves no approxmatons, ambgutes (such as ±, etc. and s unque. Also, s a lnear functon of the measurements. We now show that among all unbased estmators of ˆ whch are lnear n the measurements, the LSQ estmators have mnmum varance (the Gauss-arov heorem. Let us consder a general estmator.e. t = L, and thus: tˆ Lyˆ ( x t t t whch s a lnear functon of the measurements, t = =. Suppose we now how to mae t an unbased estmator. We wll show that the dagonal elements of the covarance matrx of t are mnmal when: ( ( L= H xv H x H xv = U x H xv (.e. when: t =. roof: V = E[( t tˆ( t tˆ ] = E[( L Lyˆ x ( L Lyˆ x ] t ˆ ˆ = LE[( ( ] L = LV L or: t = = hs can also be wrtten as: V LV L L V L t V = LV L = L V L = {( H ( x V H( x ( H ( x V }( V {( H ( x V H( x ( H ( x V } { L ( H ( x V H( x ( H ( x V }( V { L ( H ( x V H( x ( H ( x V } + hs dentty s not obvous! However, t s actually qute straght forward once t s shown LH x = H x L = (the unt matrx, whch follows from ( above. that Snce (by hypothess t s an unbased estmator of ˆ, then: Et [ ] = ˆ. ˆ = [ ]. But t = L, thus: E[ t] = E[ L ] = LE[ ] = ˆ and therefore: LE Furthermore, the theory predcton s: y( x, = H( x, and we just saw that Ey [ ( x ] ˆ = H x. hus: ˆ = LE[ y ( x ] = LE[ H ( x ˆ ] = LH ( x E[ ˆ ] = LH ( x ˆ from whch we conclude that LH x =. 598AE Lecture Notes 9 0

11 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede ang the transpose of ths: H ˆ x L =. Usng these relatons and the fact that the matrces U x H x V H x = V and V are symmetrc matrces, the dentty s verfed by multplyng out all of the terms and smplfyng. Note that the matrx L and the matrx > f the matrx then H( x has only a left nverse: H ( x H( x = and f the matrx L s also a fullran matrx, then L has only a rght nverse: H x are not square matrces. Snce H x s a full-ran matrx (.e. all rows, all columns ndependent L LL R =. ( ( ( ( ( ( We rewrte: V t = L V L = C V C + D V D where: { } { } { } C H x V H x H x V and: D L C = L H x V H x H x V Note that the matrx D depends on L but C does not. Now: ( = = ˆ ˆ C V C CV C CE[( ( ] C ˆ ˆ = E[ C( ( C ] ˆ ˆ = EC [ ( C( ] Any square matrx of the form Ezz [ ] has dagonal elements whch are the squares of numbers, and whch therefore are non-negatve. (And of course here, the numbers are all Real! he same argument can be made for DV D = DV D. hus, V t = L V L = C V C + D V D can never have dagonal elements smaller than those CV C, and wll have dagonal elements that small only when the second term DV D s zero. hs wll occur when: { ( ( } ( L= H x V H x H x V. { } D L C = L H x V H x H x V = 0.e. when: hus s the estmator of mnmum varance. 598AE Lecture Notes 9

12 Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede Summary: he lnear Least Squares estmator s unbased and has mnmum varance. hese propertes followed from the lnearty of the problem (.e. the theory predcton s lnear n the -parameters. hs concluson holds for any number of measurements y ( x (.e. we don t need/requre. Fnally, ths proof dd not requre any assumptons about how the measurements y ( x were dstrbuted (.e. the form of ther actual.d.f.. hus, these conclusons are true/vald even f the uncertantes σ n the measurements y ( x are not Gaussan/normally-dstrbuted! χ s truly However, n that case, we won t be able to assume that the LSQ sum dstrbuted as the χ.d.f., but we usually pretend that t s, and hope/pray/eep our fngers crossed that we aren t mang too large a mstae n dong so! 598AE Lecture Notes 9

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