Department of Statistics, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India
|
|
- Tyrone Johns
- 6 years ago
- Views:
Transcription
1 Mnu Vaance Unbased Esaon n he Raylegh Dsbuon unde Pogessve Type II Censoed Daa wh Bnoal Reovals. Ashok Shanubhogue and N.R.Jan* Depaen of Sascs, Sada Pael Unvesy, Vallabh Vdyanaga, Gujaa, Inda e-al : a_shanubhogue@yahoo.co, Coespondng auho*: jan_nal@sfy.co. Absac Ths pape concens wh he poble of unfoly nu vaance unbased esaon of he scale paaee of Raylegh dsbuon based on pogessve Type II censoed daa wh bnoal eovals. We oban he unfoly nu vaance unbased esao (UMVUE) fo powes of he scale paaee and s funcons. The UMVUE of he vaance of hese esaos ae also gven. The UMVUE of he () ode () h oen () ean (v) vaance (v) hazad funcon (v) edan (v) p h quanle (v) p.d.f. (x) elably funcon and (x) c.d.f. of he Raylegh dsbuon ae deved. The UMVUE of p.d.f. s ulzed o oban he UMVUE of P( X < Y). An llusave nuecal exaple s pesened. Keywods: pogessve Type II censoed saple, Raylegh dsbuon, bnoal dsbuon, coplee suffcen sasc, UMVUE. Maheacs Subjec Classfcaon: 6N0, 6N0. Inoducon A Type II censoed saple s one fo whch only salles obsevaons n a saple of n es ae obseved. A genealzaon of Type II censong s a pogessve Type II censong. Unde hs schee, n uns of he sae knd ae placed on es a e zeo, and falues ae obseved. When he fs falue s obseved, a nube of suvvng uns ae andoly whdawn fo he es; a he second falue e, suvvng uns ae seleced a ando and aken ou of he expeen, and so on. A he e of h falue, he eanng n uns ae eoved. Balakshnan e.al [5] ndcaed ha such schee can ase n clncal als whee he
2 dop ou of paens ay be caused by gaon o by lack of nees. In such suaons, he pogessve censong schee wh ando eovals s equed. Fo a dealed dscusson of pogessve censong we efe o Balakshnan and Aggawala [4] and Balakshnan []. If 0, hen, hs schee educes o he Type II censong schee. Also noe ha f 0,so ha n,hs schee educes o he case of no censong ha s he case of a coplee saple. In hs pape, we use pogessve Type II censong schee wh bnoal eovals whee he nube of uns eoved a each falue e follows a bnoal dsbuon. The Raylegh dsbuon was fs deved by Lod Raylegh n connecon wh a sudy of acouscal pobles. Snce hen any nvesgaos have used he Raylegh dsbuon o soe elaed fos of n a vaey of engneeng, wave popagaon, adaon and analyss of age daa sudes. The Raylegh dsbuon s also used o odel wave heghs n oceanogaphy, and n councaon heoy o descbe houly edan and nsananeous peak powe of eceved ado sgnals. Seveal such suaons have been dscussed by Polovko [0], Longue-Hggns [9], Zh Ren e.al [], Uvason and Godzenskaya [6], Ynhu Deng e.al [8], Ey Hu [0], Takesh Yaane [7], and any ohes. The Raylegh dsbuon s a specal case of wo paaee Webull dsbuon. The hazad ae of hs dsbuon s lnealy nceasng funcon of e. Fo a evew of leaue on esang paaees of he Raylegh dsbuon one ay efe o Lee e.al [8], Dye [9], Lalha and Msha [5], K and Han [4], Balakshnan N. [3], Solan [4], Raqab and Mad [], and any ohes. Infeence fo Raylegh dsbuon based on pogessve Type II censoed daa wee dscussed by any auhos. Al Mousa e.al [] obaned he axu lkelhood and Bayes esaes fo one and wo paaees and he elably funcon of Raylegh dsbuon unde pogessve Type II censoed saples. K e.al [3] have obaned he axu lkelhood esao, Bayes esao and cedble nevals fo he scale paaee and elably funcon of he Raylegh dsbuon based on geneal pogessve Type II censoed daa. Lee [6] have obaned he UMVUE of lfee pefoance ndex based on he ype II ulply censoed saple and develop he hypohess esng pocedue. Lee e.al [7] have obaned MLE of
3 lfee pefoance ndex and developed Baysan es fo based on pogessve Type II gh censoed saple. In hs pape ou objecve s o sudy he UMVU esaon of he scale paaee and s vaous funcons of Raylegh dsbuon based on pogessve Type II censoed daa wh bnoal eovals. Ths pape s oganzed as follows. In Secon, he lkelhood funcon s gven. In Secon 3, he UMVUE of paaee of θ and s funcons ae deved. Also, he UMVUE of he () ode () h oen () ean and vaance (v) hazad funcon (v) edan (v) p h qunle (v) posve powe of elably funcon (v) p.d.f. (x) c.d.f. ae obaned. In Secon 4 usng he UMVUE of p.d.f. he UMVUE of P( X < Y) s deved. In Secon 5, an llusave nuecal exaple s gven. The odel Le he falue e dsbuon be Raylegh wh pobably densy funcon, x x exp 0 < x <, θ > 0 f( x) θ θ 0 ohewse whee θ s a scale paaee. The densy () s gven n []. The cuulave dsbuon funcon s gven by, x F( x) exp, 0< x< θ The suvval funcon s gven by, () () x S( x) exp, 0< x< θ (3) The densy gven n () can be wen as, [ hθ ] [ g( θ )] d( x) ax ( ) ( ) f( x) (4) whee ax ( ) x, hθ ( ) exp, θ d( x) x and g( θ ) θ (5) such ha ( ) ax>0 and [ ] ( ) g( θ) ax ( ) h( θ) d x dx. 0 3
4 ( X, R ),( X, R ),,( X, R ),denoe a pogessvely Type II censoed saple, Le whee X X: : n, fo,,. and X < X < < X. The condonal lkelhood funcon can be wen as, see Cohen[7], L( θ; x/ R ) c f( x) [ s( x)] (6) whee c n( n )( n ) ( n + ), and n 0 ( ) fo,,. Subsung () and (3) n (6) we ge, c L( θ; x/ R ) x exp ( ) x θ + (7) θ We assue ha X and R ae ndependen fo all.we fuhe suppose ha he nube of uns eoved a each falue e follows a bnoal dsbuon wh pobably p. Fo Tse e al. [8] he jon pobably ass funcon of,, s gven by, ( )( n ) ( ) ( n )! p ( p) PR ( ) n!! Tha s (8) ( n ) ( ) p ( n )! ( p) ( p) PR ( ) The uncondonal lkelhood funcon s, ( θ ) ( θ )!( p) L, p; x, L ; x/ R P( R ) (0) Usng (7) and (9) n (0) we can we he full lkelhood funcon as, (9) 4
5 c L( θ, p; x, ) x exp ( ) x θ + θ p ( n )! ( p) ( p) ( n ) ( )!( ) p 3 Unbased esaon Le Y X,,,, () hen Y have exponenal dsbuon wh ean θ.now consde he followng ansfoaon, Z ny Z ( n + )( Y Y ),,3,,. () In ode o deve he dsbuon of Z,,,, consde he nvese Z Z ansfoaon Y and Y,,3,. The vaables n ( n + ) Z, Z, Z defned n () ae all ndependen and dencally dsbued wh exponenal dsbuon wh ean Z, Z,, Z s, θ, see [5]. The jon densy of I can be seen ha, f ( z, θ / R ) exp z (3) θ θ z ( + ) y (4) Usng () n (4) we have z ( + ) x (5) Le T Z ( ) x (6) + 5
6 Snce (3) s a ebe of exponenal faly of dsbuons, T s a coplee suffcen sasc foθ. The dsbuon of T s gaa wh paaees and, θ whch s agan a ebe of exponenal faly of dsbuons. The p.d.f. of T s gven by, [ hθ ] [ g( θ )] B (, ) ( ) f(, θ ) (7) whee B h g θ (, ), ( θ ) exp, ( θ) θ. Jan and Dave [] have suded he poble of nu vaance unbased esaon n a class of exponenal faly of dsbuons. They have shown ha f X, X, Xn be a ando saple fo densy of he ype gven n ( 4) and he p.d.f. of s coplee suffcen sascs can be wen as he one gven n (7) hen he UMVUE of [ h( θ )] k s gven by, and he UMVUE of [ g( θ )] k s, B ( kn, ) Hkn,, > k (8) Bn (, ) B(, n+ k) Gkn, (9) B(, n) Followng he esuls deved n Jan and Dave [], we ge he UMVUE of soe poan paaec funcons as gven below. () Usng (8) he UMVUE of exp k θ s, k H + x > k K,, ( ) ( + ) x (0) Specal case : Subsung k n (0) we ge he UMVUE of exp θ as, 6
7 H, +, ( ) 0 + x > ( + ) x () Specal case : Subsung k n (0) we ge UMVUE of exp θ as, H,, ( ) + x > ( + ) x () ()Usng (0) he UMVUE of he vaance of H, K s gven by, k k Va [ H K, ] _, (3) ( + ) x ( + ) x ()Usng (9) he UMVUE of ( θ ) k s gven by, ( + ) x > k G K, k ( + ) x k k (4) + Specal case 3: Subsung k n (4) we ge he UMVUE of θ as, G ( ), + x (5) + Snce he ode of Raylegh dsbuon s θ, he equaon gven n (5) s UMVUE of ode. Specal case 4: Subsung k n (4) we ge he UMVUE of θ as, G, ( ) x + (6) 7
8 Specal case 5: The h + oen of he Raylegh dsbuon s. θ Subsung as, k n (4) we ge UMVUE of h oen of he Raylegh dsbuon + EX ( ) ( + x ) (7) + Specal case 6: Subsung n (7) we ge UMVUE of ean as, 3 EX ( ) ( + x ) (8) + Specal case 7: The vaance of Raylegh dsbuon s π θ. Is UMVUE s obaned by subsung k n (4) as follows, π ( ) x + Va [ X ] (9) x Specal case 8: Usng (4) wh k he UMVUE of hazad funcon hx ( ) can θ be obaned as, x( ) hx ( ) (30) ( + ) x Specal case 9: The edan of Raylegh dsbuon s θ ( log 4).Usng (5) he UMVUE of edan s gven by, Medan ( + ) x log 4 ( ) (3) + Specal case 0: The p h quanle of Raylegh dsbuon s ξp θ log [ ] p. Usng (5) he UMVUE of he p h quanle s gven by, 8
9 p ( ) x log [ p ] ξ + (3) + (v) Usng (4) he UMVUE of he vaance of G s gven by, K, k ( ) + x K, k Va [ G ] (33) + k + k (v) The UMVUE of densy f ( x) gven n (), fo fxed x s gven by, φ x, ( + ) x ( + ) x x ( ) x, 0 < x < ( + ) x, > (34) (v) The UMVUE of vaance of φ,, > s gven by, x 4 ( x ) x ( x ) x > 3 x ( x ) x Va [ φ x, ] x x 4 ( x ) x x x < 0 ohewse (35) whee ( + ) x (v) Consdeng x as fxed, he UMVUE of R ( x ) of elably funcon, Rx ( ) PX ( > x), x 0 s obaned as follows. Snce [ ] Rx ( ) hθ ( ) x, whee h( θ ) s gven n (5) and usng (0) wh k x he UMVUE R ( x) of R ( x) s gven by,. 9
10 x R ( x), 0 < x< ( ) x + (36) ( + ) x (v) The UMVUE of he vaance of R ( x) s gven by, x x +,0 x < < ( x ) ( x ) + + ( x ) Va [ R ( x)] ( + x ) (37) x, x ( x ) < < + ( x ) + 0 ohewse. (x) The UMVUE of cuulave dsbuon funcon gven n ( ) s, 0, x < 0 x F ( x), 0 < x< ( + ) x ( ) x +, ohewse. (38) Shanubhouge and Jan [3] have suded he poble of nu vaance unbased esaon n exponenal dsbuon unde pogessve ype II censoed daa wh bnoal eovals. They have gven he UMVUE fo paaee p and vaous funcons of p.snce he jon densy P( R ) gven n (9) s ndependen of θ one ges he sae esaos of p and s vaous funcons as gven n Shanubhouge and Jan [3]. 0
11 4 UMVU esao of P( X < Y) In he followng heoe, we deve he UMVUE of P( X < Y). Le uns (ou of n ) on X and uns (ou of n ) on Y ae ecoded whch follow Raylegh dsbuons, gven n () wh paaees θ and θ especvely. Le,, and s, s, s be coespondng eovals. We denoe, ( ) x (39) + and ( s) y. (40) + Theoe : Unde pogessve Type II censoed daa he UMVU esao of P P( X < Y) fo he densy gven n () s gven by, j (! ) (! ) ( ) ( +! ) (! ) (! ) (! ) ( ) ( ) ( ) j j 0 j j < P (4) > 0! +! whee and ae gven by (39) and (40),especvely. Poof: We have P φ φ dxdy (4) G x, y, whee G { ( x, y):0 < x<, 0 < y<, x< y} Usng (34) n (4) and le <, we have, x ( ) y ( ) x y 0 x P dydx (43) Now y ( ) y x dy (44) x Afe subsung (44) no (43) we ge,
12 x ( ) x x dx (45) 0 n n j j Fuhe splfcaon of (45) and applyng he esul ( w) ( ) we ge, Fuhe splfcaon of (46) gves, j j 0 n j ( ) w ( w) dw j 0 j 0 j j ( ) (46) (! ) (! ) ( ) ( ) j P ( ), < (47) j 0 + j! j! Slaly we can show ha fo he case > he UMVUE of P( X < Y) s, (! ) (! ) ( ) ( ) ( ) P > (48) 0! +! j w 5 Illusave exaple In hs secon we llusae he use of he esaon ehods gven n hs acle. We consde he daa on falue es of 5 ball beangs n enduance es gven n Caon [6]. The daa ae 7.88, 8.9, 33.00, 4.5, 4., 45.60, 48.48, 5.84, 5.96, 54., 55.56, 67.80, 67.80, 67.80, 68.64, 68.64, 68.88, 84., 93., 98.64, 05., 05.84, 7.9, 8.04, These obsevaons ae he nube of llon evoluons befoe falue of 5 ball beangs. Raqab and Mad [], Lee [6], and Lee a el [7], have ndcaed ha a one paaee Raylegh dsbuon fs well fo hese daa. We geneae a pogessve Type II censoed daa wh bnoal eovals fo hese daa. The pogessve censoed saple sze s 3.The dopou nubes have been geneaed usng MYSTAT sofwae as follows: fo B(, 0.05) and,,, have B(,0.05) dsbuon fo, 3, j j and se,
13 f j > 0 j 3 j j 0 o.w. Table I. Obseved es of falue and he dopous easued n he nfoave expeen x Usng he esuls gven n Secon 3, he UMVU esaes of dffeen paaec funcons of θ based on daa gven n Table I ae gven below. 3
14 Table II. The UMVU esaes of dffeen paaec funcons of θ based on daa gven n Table I S. No. Paaec funcon UMVU esae exp θ Vaance of,3 exp θ 3 4 Vaance of,3 H Va H,3 H Va H,3 5 θ G,3 6 7 Vaance of,3 θ 8 Vaance of, G,3 H, [ ] E 09 H, [ ] E Va [ G ] G,3 G, Va [ G ] E 06 9 Medan Thd quale Mean EX ( ) Vaance Va [ X ] Hazad funcon a x hx ( ) Densy a x 80.5 φ 80.5, Vaance of 80.5,3 φ Va 80.5,3 [ φ ].3043E 07 6 Relably a x 80.5 R ( x ) Vaance of R ( x) a x 80.5 Va [ R ( x )] c.d.f. a x 80.5 F ( x )
15 6 Conclusons: In hs pape we have consdeed he esaon poble of paaee and s vaous funcons of Raylegh dsbuon unde he pogessve Type II censoed daa wh bnoal eovals. We have deved an elegan expesson fo he UMVUE esaos fo ode, h oen, ean, vaance, hazad funcon, edan, p h quanle, p.d.f., elably funcon and c.d.f. of he Raylegh dsbuon. Ou ehod of obanng hese esaos s que sple han he adonal appoach. We have also obaned UMVUE of P( X < Y) by usng he UMVUE of p.d.f. These esuls educe o Type II censoed daa (whee eovals ae no allowed) and coplee saple case by subsung 0 fo,, and,,., especvely. Acknowledgen: The auhos would lke o expess deep sense of gaude o he efeee fo nsghful coens and helpful suggesons whch led o a consdeable poveen of he pape. Refeences []. M.A.M. Alousa and S.A.AL.Saghee, Sascal nfeence fo he Raylegh odel based on pogessvely ype II censoed daa, Sascs, 40,(006), pp []. N.Balakshnan, Pogessve censong ehodology: an appasal, Tes,6, (007), pp.-59. [3]. N. Balakshnan, Appoxae MLE of he scale paaee of he Raylegh dsbuon wh censong, IEEE Tans.Relb.,38, (989), pp [4]. N.Balakshnan and R.Aggawala, Pogessve Censong: Theoy, Mehods and Applcaons. Bkhause,Boson.(000). [5]. N.Balakshnan, E.Cae, U.Kaps and N.Schenk, Pogessve ype II censoed ode sascs fo exponenal dsbuons, Sascs, 35, (00) pp [6]. C.Caon, The coec ball beangs daa, Lfee Daa Analyss,8,(00), pp [7]. A.C.Cohen, Pogessvely censoed saples n lfe esng, Technoecs, 5, (963), pp
16 [8]. Ynhu Deng, Yuanyua Wang, Yuzhong Shen, Speckle educon of ulasound ages based on Raylegh ed ansoopc dffuson fle, Paen Recognon Lees, 3,(0) pp [9]. D.D. Dye,, Esaon of he scale paaee of he ch dsbuon based on saple quanles, Technoecs, 5,(973), pp [0]. Ey Hu, Yung He, Yanng Chen, Expeenal sudy on he suface sess easueen wh Raylegh wave deecon echnque, Appled Acouscs, 70,(009), pp []. P.N.Jan,and H.P.Dave, Mnu vaance unbased esaon n a class of exponenal faly of dsbuons and soe of s applcaons, Meon,48, (990), pp []. N.L.Johnson, S.Koz and N.Balakshnan, Connuous Unvaae Dsbuons, Volue I, nd edon, John Wley & Sons, New Yok (994). [3]. Chansoo K and Keunhee Han Esaon of he scale paaee of he Raylegh dsbuon unde geneal pogessve censong, Jounal of he Koean Sascal Socey, 38, (009), pp [4]. Chansoo K, Keunhee Han, Esaon of he scale paaee of he Raylegh dsbuon wh ulply ype-ii censoed saple, Jounal of Sascal Copuaon and Sulaon, 79, (009), pp [5]. S. Lalha, Anand Msha, Modfed axu lkelhood esaon fo Raylegh dsbuon, Councaons n Sascs - Theoy and Mehods, 5, (996), pp [6]. W.-C.Lee, Sascal esng fo assessng lfee pefoance ndex of he Raylegh lfee poducs, Jounal of he Chnese Insue of Indusal Engnees, 5, (008), pp [7]. W.-C.Lee, J.-W.Wu, M.-L.Hong, L.-S.Ln, R.-L.Chan, Assessng he lfee pefoance ndex of Raylegh poducs based on he Bayesan esaon unde pogessve ype II gh censoed saples, Jounal of Copuaonal and Appled Maheacs, 35, (0), pp [8]. Lee,K.R., Kapada,C.H., and Bock,D.B. On esang he scale paaee of he Ralegh dsbuon fo doubly censoed saples, Sascal Papes,, (980), pp 4-9. [9]. Lonhue-Hggns,M.S., On he Sascal dsbuon of he Heghs of sea Waves, Jounal of Mane Reseach,, (95), pp
17 [0]. A.M.Polovko, Fundaenals of Relably Theoy, Acadec Pess, New Yok,(968). []. M.Z.Raqab and M.T.Mad, Bayesan pedcon of he oal e on es usng doubly censoed Raylegh daa. Jounal of Sascal Copuaon and Sulaon, 7, (00), pp []. Zh Ren, Guangyu Wang, Qanbn Chen, Hongbn L, Modllng and Sulaon of Raylegh fadng, pah loss, and shadowng fadng fo weless oble newoks, Sulaon Modellng Pacce and Theoy, 9 (0), pp [3]. A.Shanubhouge and N.R.Jan, Mnu vaance unbased esaon n exponenal dsbuon usng pogessvely ype II censoed daa wh bnoal eovals, Advances and Applcaons n Sascs, 8, (00), pp [4]. A.A.Solan, Copason of lnex and quadeac Bayes esaos fo he Raylegh dsbuon, Councaons n Sascs-Theoy Mehods, 9, (000), pp [5]. D.R.Thoas and W.M.Wlson, Lnea ode sasc esaon fo he wo paaee Webull and exee value dsbuons fo Type II pogessvely censoed saples,technoecs,4, (97), pp [6]. S.U.Uvason and I.S.Godzenskaya, The deecon of a weak sgnal on a backgound of nefeence n he case of Raylegh dsbuon, Measueen Technques, 49, (006), pp [7]. Takesh Yaane, Applcaon of he Raylegh dsbuon o sze selecvy of sall pawn pos fo he oenal ve pawn, Macobachu npponense, Fshees Reseach, 36,(998), pp [8]. S.K.Tse,C.Yang and H. K.,Yuen, Sascal analyss of Webull dsbued lfee daa unde Type II pogessve censong wh bnoal eovals, Jounal of Appled Sascs, 7, (000), pp
I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova
The XIII Inenaonal Confeence Appled Sochasc Models and Daa Analyss (ASMDA-009) Jne 30-Jly 3, 009, Vlns, LITHUANIA ISBN 978-9955-8-463-5 L Sakalaskas, C Skadas and E K Zavadskas (Eds): ASMDA-009 Seleced
More informationMaximum Likelihood Estimation
Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon
More informationName of the Student:
Engneeng Mahemacs 05 SUBJEC NAME : Pobably & Random Pocess SUBJEC CODE : MA645 MAERIAL NAME : Fomula Maeal MAERIAL CODE : JM08AM007 REGULAION : R03 UPDAED ON : Febuay 05 (Scan he above QR code fo he dec
More informationOptimal Design of Step Stress Partially Accelerated Life Test under Progressive Type-II Censored Data with Random Removal for Gompertz Distribution
Aecan Jounal of Appled Matheatcs and Statstcs, 09, Vol 7, No, 37-4 Avalable onlne at http://pubsscepubco/ajas/7//6 Scence and Educaton Publshng DOI:069/ajas-7--6 Optal Desgn of Step Stess Patally Acceleated
More informationSTABILITY CRITERIA FOR A CLASS OF NEUTRAL SYSTEMS VIA THE LMI APPROACH
Asan Jounal of Conol, Vol. 6, No., pp. 3-9, Mach 00 3 Bef Pape SABILIY CRIERIA FOR A CLASS OF NEURAL SYSEMS VIA HE LMI APPROACH Chang-Hua Len and Jen-De Chen ABSRAC In hs pape, he asypoc sably fo a class
More informationDetermination of the rheological properties of thin plate under transient vibration
(3) 89 95 Deenaon of he heologcal popees of hn plae unde ansen vbaon Absac The acle deals wh syseac analyss of he ansen vbaon of ecangula vscoelasc ohoopc hn D plae. The analyss s focused on specfc defoaon
More information1 Constant Real Rate C 1
Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns
More informationModern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.
Moden Enegy Funconal fo Nucle and Nuclea Mae By: lbeo noosa Teas &M Unvesy REU Cycloon 008 Meno: D. Shalom Shlomo Oulne. Inoducon.. The many-body poblem and he aee-fock mehod. 3. Skyme neacon. 4. aee-fock
More informationHandling Fuzzy Constraints in Flow Shop Problem
Handlng Fuzzy Consans n Flow Shop Poblem Xueyan Song and Sanja Peovc School of Compue Scence & IT, Unvesy of Nongham, UK E-mal: {s sp}@cs.no.ac.uk Absac In hs pape, we pesen an appoach o deal wh fuzzy
More information5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )
5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma
More informationTHEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that
THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because
More informationSolution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution
Volume-03 Issue-09 Sepembe-08 ISSN: 455-3085 (Onlne) RESEARCH REVIEW Inenaonal Jounal of Muldscplnay www.jounals.com [UGC Lsed Jounal] Soluon of Non-homogeneous bulk aval Two-node Tandem Queung Model usng
More informationTHE EQUIVALENCE OF GRAM-SCHMIDT AND QR FACTORIZATION (page 227) Gram-Schmidt provides another way to compute a QR decomposition: n
HE EQUIVAENCE OF GRA-SCHID AND QR FACORIZAION (page 7 Ga-Schdt podes anothe way to copute a QR decoposton: n gen ectos,, K, R, Ga-Schdt detenes scalas j such that o + + + [ ] [ ] hs s a QR factozaton of
More information( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:
esng he Random Walk Hypohess If changes n a sees P ae uncoelaed, hen he followng escons hold: va + va ( cov, 0 k 0 whee P P. k hese escons n un mply a coespondng se of sample momen condons: g µ + µ (,,
More informationExistence and Uniqueness Results for Random Impulsive Integro-Differential Equation
Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal
More informationChapter 8. Linear Momentum, Impulse, and Collisions
Chapte 8 Lnea oentu, Ipulse, and Collsons 8. Lnea oentu and Ipulse The lnea oentu p of a patcle of ass ovng wth velocty v s defned as: p " v ote that p s a vecto that ponts n the sae decton as the velocty
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationSimulation of Non-normal Autocorrelated Variables
Jounal of Moden Appled Sascal Mehods Volume 5 Issue Acle 5 --005 Smulaon of Non-nomal Auocoelaed Vaables HT Holgesson Jönöpng Inenaonal Busness School Sweden homasholgesson@bshse Follow hs and addonal
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationCHAPTER 10: LINEAR DISCRIMINATION
HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g
More information( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is
Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002
More informationChapter 3: Vectors and Two-Dimensional Motion
Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon
More informationMonetary policy and models
Moneay polcy and odels Kes Næss and Kes Haae Moka Noges Bank Moneay Polcy Unvesy of Copenhagen, 8 May 8 Consue pces and oney supply Annual pecenage gowh. -yea ovng aveage Gowh n oney supply Inflaon - 9
More informationThe Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland
The Unque Soluon of Sochasc Dffeenal Equaons Dech Rye RyeDM@gawne.ch Mdaweg 3 CH-4500 Solohun Swzeland Phone +4132 621 13 07 Tme evesal n sysems whou an exenal df sngles ou he an-iô negal. Key wods: Sochasc
More informationGENERALIZATION OF AN IDENTITY INVOLVING THE GENERALIZED FIBONACCI NUMBERS AND ITS APPLICATIONS
#A39 INTEGERS 9 (009), 497-513 GENERALIZATION OF AN IDENTITY INVOLVING THE GENERALIZED FIBONACCI NUMBERS AND ITS APPLICATIONS Mohaad Faokh D. G. Depatent of Matheatcs, Fedows Unvesty of Mashhad, Mashhad,
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationSTUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION
Inenaional Jounal of Science, Technology & Managemen Volume No 04, Special Issue No. 0, Mach 205 ISSN (online): 2394-537 STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE
More informationScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *
Avalable onlne a wwwscencedeccom ScenceDec oceda Engneeng 69 4 85 86 4h DAAAM Inenaonal Smposum on Inellgen Manufacung and Auomaon Behavo of Inegal Cuves of he uaslnea Second Ode Dffeenal Equaons Alma
More informationLecture 5. Plane Wave Reflection and Transmission
Lecue 5 Plane Wave Reflecon and Tansmsson Incden wave: 1z E ( z) xˆ E (0) e 1 H ( z) yˆ E (0) e 1 Nomal Incdence (Revew) z 1 (,, ) E H S y (,, ) 1 1 1 Refleced wave: 1z E ( z) xˆ E E (0) e S H 1 1z H (
More informationPrediction of modal properties of circular disc with pre-stressed fields
MAEC Web of Confeences 157 0034 018 MMS 017 hps://do.og/10.1051/aecconf/0181570034 Pedcon of odal popees of ccula dsc h pe-sessed felds Mlan Naď 1* Rasslav Ďuš 1 bo Nánás 1 1 Slovak Unvesy of echnology
More informationPhysics 207 Lecture 16
Physcs 07 Lectue 6 Goals: Lectue 6 Chapte Extend the patcle odel to gd-bodes Undestand the equlbu of an extended object. Analyze ollng oton Undestand otaton about a fxed axs. Eploy consevaton of angula
More informationNormal Random Variable and its discriminant functions
Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The
More informationMultistage Median Ranked Set Sampling for Estimating the Population Median
Jounal of Mathematcs and Statstcs 3 (: 58-64 007 ISSN 549-3644 007 Scence Publcatons Multstage Medan Ranked Set Samplng fo Estmatng the Populaton Medan Abdul Azz Jeman Ame Al-Oma and Kamaulzaman Ibahm
More informationA DISCRETE PARAMETRIC MARKOV-CHAIN MODEL OF A TWO NON-IDENTICAL UNIT COLD STANDBY SYSTEM WITH PREVENTIVE-MAINTENANCE
IJRRA 7 (3) Decembe 3 www.aaess.com/volumes/vol7issue3/ijrra_7_3_6.df A DICRETE PARAMETRIC MARKOV-CHAIN MODEL OF A TWO NON-IDENTICAL UNIT COLD TANDBY YTEM WITH PREVENTIVE-MAINTENANCE Rakes Gua¹ * & Paul
More informationStress Analysis of Infinite Plate with Elliptical Hole
Sess Analysis of Infinie Plae ih Ellipical Hole Mohansing R Padeshi*, D. P. K. Shaa* * ( P.G.Suden, Depaen of Mechanical Engg, NRI s Insiue of Infoaion Science & Technology, Bhopal, India) * ( Head of,
More informationPhysics 201 Lecture 15
Phscs 0 Lecue 5 l Goals Lecue 5 v Elo consevaon of oenu n D & D v Inouce oenu an Iulse Coens on oenu Consevaon l oe geneal han consevaon of echancal eneg l oenu Consevaon occus n sses wh no ne eenal foces
More informationÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s
MÜHENDİSLİK MEKANİĞİ. HAFTA İMPULS- MMENTUM-ÇARPIŞMA Linea oenu of a paicle: The sybol L denoes he linea oenu and is defined as he ass ies he elociy of a paicle. L ÖRNEK : THE LINEAR IMPULSE-MMENTUM RELATIN
More informationMemorandum COSOR 97-??, 1997, Eindhoven University of Technology
Meoandu COSOR 97-??, 1997, Endhoven Unvey of Technology The pobably geneang funcon of he Feund-Ana-Badley ac M.A. van de Wel 1 Depaen of Maheac and Copung Scence, Endhoven Unvey of Technology, Endhoven,
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More informationNumerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)
USOD 005 uecal Sudy of Lage-aea An-Reonan Reflecng Opcal Wavegude (ARROW Vecal-Cavy Seconduco Opcal Aplfe (VCSOA anhu Chen Su Fung Yu School of Eleccal and Eleconc Engneeng Conen Inoducon Vecal Cavy Seconduco
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationA hybrid method to find cumulative distribution function of completion time of GERT networks
Jounal of Indusal Engneeng Inenaonal Sepembe 2005, Vol., No., - 9 Islamc Azad Uvesy, Tehan Souh Banch A hybd mehod o fnd cumulave dsbuon funcon of compleon me of GERT newos S. S. Hashemn * Depamen of Indusal
More information5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015)
5h Inernaonal onference on Advanced Desgn and Manufacurng Engneerng (IADME 5 The Falure Rae Expermenal Sudy of Specal N Machne Tool hunshan He, a, *, La Pan,b and Bng Hu 3,c,,3 ollege of Mechancal and
More informationLecture 2 M/G/1 queues. M/G/1-queue
Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly
More informationA DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE
S13 A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE by Hossen JAFARI a,b, Haleh TAJADODI c, and Sarah Jane JOHNSTON a a Deparen of Maheacal Scences, Unversy
More informationWater Hammer in Pipes
Waer Haer Hydraulcs and Hydraulc Machnes Waer Haer n Pes H Pressure wave A B If waer s flowng along a long e and s suddenly brough o res by he closng of a valve, or by any slar cause, here wll be a sudden
More informationN 1. Time points are determined by the
upplemena Mehods Geneaon of scan sgnals In hs secon we descbe n deal how scan sgnals fo 3D scannng wee geneaed. can geneaon was done n hee seps: Fs, he dve sgnal fo he peo-focusng elemen was geneaed o
More informationAppendix H: Rarefaction and extrapolation of Hill numbers for incidence data
Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs
More informationSCIENCE CHINA Technological Sciences
SIENE HINA Technologcal Scences Acle Apl 4 Vol.57 No.4: 84 8 do:.7/s43-3-5448- The andom walkng mehod fo he seady lnea convecondffuson equaon wh axsymmec dsc bounday HEN Ka, SONG MengXuan & ZHANG Xng *
More informationBayesian Analysis of Topp-Leone Distribution under Different Loss Functions and Different Priors
J. tat. Appl. Po. Lett. 3, No. 3, 9-8 (6) 9 http://dx.doi.og/.8576/jsapl/33 Bayesian Analysis of Topp-Leone Distibution unde Diffeent Loss Functions and Diffeent Pios Hummaa ultan * and. P. Ahmad Depatment
More informationOn Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution
ISSN 684-843 Joua of Sac Voue 5 8 pp. 7-5 O Pobaby Dey Fuco of he Quoe of Geeaed Ode Sac fo he Webu Dbuo Abac The pobaby dey fuco of Muhaad Aee X k Y k Z whee k X ad Y k ae h ad h geeaed ode ac fo Webu
More informationF-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction
ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or
More informationImpact of the Horizontal and Vertical Electromagnetic Waves on Oscillations of the Surface of Horizontal Plane Film Flow
Inenaonal Jounal of Ccus an Eleconcs hp://wwwaasog/aas/ounals/ce Ipac of he ozonal an Vecal Elecoagnec Waves on Oscllaons of he Suface of ozonal Plane Fl Flow IVAN V KAZACKOV Dep of Enegy Technology Royal
More information1 Definition of Rademacher Complexity
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the
More informationChapter Finite Difference Method for Ordinary Differential Equations
Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence
More informationGenerating Functions, Weighted and Non-Weighted Sums for Powers of Second-Order Recurrence Sequences
Geneatng Functons, Weghted and Non-Weghted Sums fo Powes of Second-Ode Recuence Sequences Pantelmon Stăncă Aubun Unvesty Montgomey, Depatment of Mathematcs Montgomey, AL 3614-403, USA e-mal: stanca@studel.aum.edu
More informationDelay-Dependent Control for Time-Delayed T-S Fuzzy Systems Using Descriptor Representation
82 Inenaonal Jounal of Conol Auomaon and Sysems Vol 2 No 2 June 2004 Delay-Dependen Conol fo me-delayed -S Fuzzy Sysems Usng Descpo Repesenaon Eun ae Jeung Do Chang Oh and Hong Bae ak Absac: hs pape pesens
More informationCentral limit theorem for functions of weakly dependent variables
Int. Statistical Inst.: Poc. 58th Wold Statistical Congess, 2011, Dublin (Session CPS058 p.5362 Cental liit theoe fo functions of weakly dependent vaiables Jensen, Jens Ledet Aahus Univesity, Depatent
More informationAccelerated Sequen.al Probability Ra.o Test (SPRT) for Ongoing Reliability Tes.ng (ORT)
cceleaed Sequen.al Pobably Ra.o Tes (SPRT) fo Ongong Relably Tes.ng (ORT) Mlena Kasch Rayheon, IDS Copygh 25 Rayheon Company. ll ghs eseved. Cusome Success Is Ou Msson s a egseed adema of Rayheon Company
More informationApproximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy
Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae
More informationCHAPTER 3 DETECTION TECHNIQUES FOR MIMO SYSTEMS
4 CAPTER 3 DETECTION TECNIQUES FOR MIMO SYSTEMS 3. INTRODUCTION The man challenge n he paccal ealzaon of MIMO weless sysems les n he effcen mplemenaon of he deeco whch needs o sepaae he spaally mulplexed
More information1 Random Variable. Why Random Variable? Discrete Random Variable. Discrete Random Variable. Discrete Distributions - 1 DD1-1
Rando Vaiable Pobability Distibutions and Pobability Densities Definition: If S is a saple space with a pobability easue and is a eal-valued function defined ove the eleents of S, then is called a ando
More informationCentral Coverage Bayes Prediction Intervals for the Generalized Pareto Distribution
Statistics Reseach Lettes Vol. Iss., Novembe Cental Coveage Bayes Pediction Intevals fo the Genealized Paeto Distibution Gyan Pakash Depatment of Community Medicine S. N. Medical College, Aga, U. P., India
More information2/24/2014. The point mass. Impulse for a single collision The impulse of a force is a vector. The Center of Mass. System of particles
/4/04 Chapte 7 Lnea oentu Lnea oentu of a Sngle Patcle Lnea oentu: p υ It s a easue of the patcle s oton It s a vecto, sla to the veloct p υ p υ p υ z z p It also depends on the ass of the object, sla
More informationgravity r2,1 r2 r1 by m 2,1
Gavtaton Many of the foundatons of classcal echancs wee fst dscoveed when phlosophes (ealy scentsts and atheatcans) ted to explan the oton of planets and stas. Newton s ost faous fo unfyng the oton of
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationPhysics 1501 Lecture 19
Physcs 1501 ectue 19 Physcs 1501: ectue 19 Today s Agenda Announceents HW#7: due Oct. 1 Mdte 1: aveage 45 % Topcs otatonal Kneatcs otatonal Enegy Moents of Ineta Physcs 1501: ectue 19, Pg 1 Suay (wth copason
More informationField due to a collection of N discrete point charges: r is in the direction from
Physcs 46 Fomula Shee Exam Coulomb s Law qq Felec = k ˆ (Fo example, f F s he elecc foce ha q exes on q, hen ˆ s a un veco n he decon fom q o q.) Elecc Feld elaed o he elecc foce by: Felec = qe (elecc
More informationSurvival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System
Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual
More informationON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT
V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com
More informationExistence and multiplicity of solutions to boundary value problems for nonlinear high-order differential equations
Jounal of pplied Matheatics & Bioinfoatics, vol.5, no., 5, 5-5 ISSN: 79-66 (pint), 79-6939 (online) Scienpess Ltd, 5 Existence and ultiplicity of solutions to bounday value pobles fo nonlinea high-ode
More informationReliability estimation in Pareto-I distribution based on progressively type II censored sample with binomial removals
Journal of Scentfc esearch Developent (): 08-3 05 Avalable onlne at wwwjsradorg ISSN 5-7569 05 JSAD elablty estaton n Pareto-I dstrbuton based on progressvely type II censored saple wth bnoal reovals Ilhan
More informationResponse of MDOF systems
Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss
More informationSome Remarks on the Boundary Behaviors of the Hardy Spaces
Soe Reaks on the Bounday Behavios of the Hady Spaces Tao Qian and Jinxun Wang In eoy of Jaie Kelle Abstact. Soe estiates and bounday popeties fo functions in the Hady spaces ae given. Matheatics Subject
More informationEfficient Bayesian Network Learning for System Optimization in Reliability Engineering
Qualy Technology & Quanave Managemen Vol. 9, No., pp. 97-, 202 QTQM ICAQM 202 Effcen Bayesan Newok Leanng fo Sysem Opmzaon n Relably Engneeng A. Gube and I. Ben-Gal Depamen of Indusal Engneeng, Faculy
More informationNew Bivariate Exponentiated Modified Weibull Distribution
Jounal of Matheats and Statsts Ognal Reseah Pape New Bvaate Exponentated Modfed Webull Dstbuton Abdelfattah Mustafa and Mohaed AW Mahoud Depatent of Matheats, Fault of Sene, Mansoua Unvest, Mansoua 556,
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationUniversity of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY
Unvesy of Calfona, Davs Dae: June xx, 009 Depamen of Economcs Tme: 5 hous Mcoeconomcs Readng Tme: 0 mnues PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE Pa I ASWER KEY Ia) Thee ae goods. Good s lesue, measued
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationBayesian Inference of the GARCH model with Rational Errors
0 Inernaonal Conference on Economcs, Busness and Markeng Managemen IPEDR vol.9 (0) (0) IACSIT Press, Sngapore Bayesan Inference of he GARCH model wh Raonal Errors Tesuya Takash + and Tng Tng Chen Hroshma
More informationThe Solutions of Initial Value Problems for Nonlinear Fourth-Order Impulsive Integro-Differential Equations in Banach Spaces
WSEAS TRANSACTIONS o MATHEMATICS Zhag Lglg Y Jgy Lu Juguo The Soluos of Ial Value Pobles fo Nolea Fouh-Ode Ipulsve Iego-Dffeeal Equaos Baach Spaces Zhag Lglg Y Jgy Lu Juguo Depae of aheacs of Ta Yua Uvesy
More informationOptimum Settings of Process Mean, Economic Order Quantity, and Commission Fee
Jounal of Applied Science and Engineeing, Vol. 15, No. 4, pp. 343 352 (2012 343 Optiu Settings of Pocess Mean, Econoic Ode Quantity, and Coission Fee Chung-Ho Chen 1 *, Chao-Yu Chou 2 and Wei-Chen Lee
More information) from i = 0, instead of i = 1, we have =
Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ
More informationCptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1
ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationInformation Fusion Kalman Smoother for Time-Varying Systems
Infoaon Fuon alan oohe fo Te-Vayng ye Xao-Jun un Z- Deng Abac-- Fo he lnea dcee e-ayng ochac conol ye wh uleno coloed eaueen noe hee dbued opal fuon alan oohe ae peened baed on he opal nfoaon fuon ule
More informationTrack Properities of Normal Chain
In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,
More informationPARAMETER ESTIMATION FOR TWO WEIBULL POPULATIONS UNDER JOINT TYPE II CENSORED SCHEME
Sept 04 Vol 5 No 04 Intenatonal Jounal of Engneeng Appled Scences 0-04 EAAS & ARF All ghts eseed wwweaas-ounalog ISSN305-869 PARAMETER ESTIMATION FOR TWO WEIBULL POPULATIONS UNDER JOINT TYPE II CENSORED
More information( ) [ ] MAP Decision Rule
Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure
More informationGMM parameter estimation. Xiaoye Lu CMPS290c Final Project
GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationPerformance Analysis for a Network having Standby Redundant Unit with Waiting in Repair
TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen
More informationIntroduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms
Course organzaon Inroducon Wee -2) Course nroducon A bref nroducon o molecular bology A bref nroducon o sequence comparson Par I: Algorhms for Sequence Analyss Wee 3-8) Chaper -3, Models and heores» Probably
More informationTime Truncated Sampling Plan under Hybrid Exponential Distribution
18118118118118117 Journal of Unceran Syses Vol.1, No.3, pp.181-197, 16 Onlne a: www.jus.org.uk Te Truncaed Saplng Plan under Hybrd Exponenal Dsrbuon S. Sapah 1,, S. M. Lalha 1 Deparen of Sascs, Unversy
More informationA GENERALIZATION OF A CONJECTURE OF MELHAM. 1. Introduction The Fibonomial coefficient is, for n m 1, defined by
A GENERALIZATION OF A CONJECTURE OF MELHAM EMRAH KILIC 1, ILKER AKKUS, AND HELMUT PRODINGER 3 Abstact A genealization of one of Melha s conectues is pesented Afte witing it in tes of Gaussian binoial coefficients,
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationSOME GENERAL NUMERICAL RADIUS INEQUALITIES FOR THE OFF-DIAGONAL PARTS OF 2 2 OPERATOR MATRICES
italian jounal of pue and applied mathematics n. 35 015 (433 44) 433 SOME GENERAL NUMERICAL RADIUS INEQUALITIES FOR THE OFF-DIAGONAL PARTS OF OPERATOR MATRICES Watheq Bani-Domi Depatment of Mathematics
More informationMotion of Wavepackets in Non-Hermitian. Quantum Mechanics
Moon of Wavepaces n Non-Herman Quanum Mechancs Nmrod Moseyev Deparmen of Chemsry and Mnerva Cener for Non-lnear Physcs of Complex Sysems, Technon-Israel Insue of Technology www.echnon echnon.ac..ac.l\~nmrod
More informationcalculating electromagnetic
Theoeal mehods fo alulang eleomagne felds fom lghnng dshage ajeev Thoapplll oyal Insue of Tehnology KTH Sweden ajeev.thoapplll@ee.kh.se Oulne Despon of he poblem Thee dffeen mehods fo feld alulaons - Dpole
More informationChapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are
Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol
More information