32 G; F ; (1) {X, X(i), i = 1, 2,..., X, (2) {M(t), t α Poisson, t ; (3) {Y, Y (i), i = 1, 2,..., Y, (4) {N(t), t β Poisson, t ; (5) {W (t), t, σ ; (6
|
|
- 意裴 甘
- 5 years ago
- Views:
Transcription
1 212 2 Chinese Journal of Applied Probability and Statistics Vol.28 No.1 Feb. 212 Poisson ( 1,, 211; 1 2,3 2 2,, 2197) ( 3,, 2197) Poisson,,.,. : :,,,,. O , ( 1 6]). 4] Cai Poisson,, 6] Fang Luo Poisson,., Poisson. M(t) N(t) S t = u + X(i) Y (i) + σw (t) = u + U t H(t) = i=1 i=1 = u + H(t) Z(t), t, S = u, (1.1) M(t) i=1 U t = M(t) i=1 N(t) X(i) i=1 X(i) + σw (t), Z(t) = Y (i) + σw (t), N(t) i=1 Y (i), t, (167132, 18711, )(BK286) (11711) (21JCXM-2-8) ,
2 32 G; F ; (1) {X, X(i), i = 1, 2,..., X, (2) {M(t), t α Poisson, t ; (3) {Y, Y (i), i = 1, 2,..., Y, (4) {N(t), t β Poisson, t ; (5) {W (t), t, σ ; (6) {X(i), i = 1, 2,..., {Y (i), i = 1, 2,..., {M(t), t, {N(t), t {W (t), t. τ (1.1), τ = inf{t : S t < inf φ = ; Φ(u) (1.1) u, Φ(u) = P{τ < S = u = P{S t < t S = u; Ψ(u) (1.1) u, Ψ(u) = 1 Φ(u). Poisson, X t = e δt( u + t ) e δs dus, t, X = u, δ, (1.2) δ, δ. T (1.2), T = inf{t : X(t) < ; ϕ(u) (1.2) u, ϕ(u) = P{T < X = u = P{X t < t X = u, ψ(u) (1.2) u, ψ(u) = 1 ϕ(u).,,,,. T s T s = inf{x t <, X h >, < h < t; T d T d = inf{t : X t =, X h >, < h < t, T = min{t s, T d, ϕ s ϕ d ϕ s (u) = P{T s < X = u, ϕ d (u) = P{T d < X = u. ψ s (u) = 1 ϕ s (u), ψ d (u) = 1 ϕ d (u). ϕ(u) = ϕ d (u) + ϕ s (u), u. (1.3)
3 Poisson 33, ϕ d () = ϕ() = 1, ϕ s () =. (1.4) ψ(u) = 1 ϕ(u) ψ(u) = ψ s (u) + ψ d (u) 1, ψ() =. (1.5), P{T s < T < = ϕ s (u)/ϕ(u); P{T d < T < = ϕ d (u)/ϕ(u)., ϕ(u, t) ϕ(u, t) = P{T < t X = u, ψ(u, t) = 1 ϕ(u, t). ϕ s (u, t) = P{T s < t X = u, ψ s (u, t) = 1 ϕ s (u, t); ϕ d (u, t) = P{T d < t X = u, ψ d (u, t) = 1 ϕ d (u, t), ϕ(u, t) = ϕ d (u, t) + ϕ s (u, t), u. (1.6) 2 ; 3 ϕ s (u) ϕ d (u), ϕ(u) ; 4, ϕ s (u), ϕ d (u) ϕ(u); 5.,, 2. EU t = EM(t)EX(t) EN(t)EY (t) + σew (t) = (αe(x) βe(y ))t > {U t, t. α (, 1/2), α Hölder, T >, ω Ω, K = K(T, ω), s < t T, W (t)(ω) W (s)(ω) K t s α, P(Ω ) = 1. 7]. 2.3 lim S t =, t a.s.. Anscombe ( 8] 2.5.3). 2.4 lim Ψ(u) = 1, a.s.. (2.1) u
4 lim S t =, t, t, U t >, T >, t > T, U t >. T 2.2 inf U t, inf U t >, a.s.. t<t t u, S t, (2.1). : 3.1 a.s.. 3., ψ s (u), u, ψ s (u) (α+β)ψ s (u) uδψ s(u) 1 2 σ2 ψ s (u) = α ψ s (+ ) = 1, ψ s () = 1, α 1 2 σ2 ψ s () = α h(t) = ue δt + σ ψ s (u+x)dg(x)+β t ψ s (x)dg(x). ψ s (u y)df (y). (3.1) (3.2) e δs dw s u. (3.3) (, t], (1.2) X t. M(t) N(t) Poisson, (, t] o(t). (1) M(t) N(t), (1 αt)(1 βt) + o(t). (2) M(t) N(t), αt(1 βt) + o(t). (3) M(t)N(t), (1 αt)βt + o(t). (4) M(t) ( N(t)) M(t) N(t), (3), y > u + h(t), ψ s (u + h(t) y) =, { ψ s (u) = (1 αt)(1 βt)e{ψ s u + h(t)] + αt(1 βt)e { +h(t) + βt(1 αt)e ψ s u + h(t) y]df (y) + o(t). ψ s u + h(t) + x]dg(x)
5 Poisson 35 { (α + β)te{ψ s u + h(t)] = E{ψ s u + h(t)] ψ s (u) + αte ψ s u + h(t) + x]dg(x) Y (t) = h(t) + u, (3.3) dψ s u + h(t)] = dψ s Y (t)] = ψ s u + h(t)] = ψ s Y (t)] { +h(t) + βte ψ s u + h(t) y]df (y) + o(t). (3.4) dy (t) = uδe δt dt + σe δt dw t, Y () = u. = ψ s (u) + + E{ψ s u + h(t)] = ψ s (u) + { uδe δt ψ sy (t)] σ2 e 2δt ψ s Y (t)] dt + σe δt ψ sy (t)]dw t. t t t { uδe δx ψ sy (x)] σ2 e 2δx ψ s Y (x)] dx σe δx ψ sy (x)]dw x. (3.4) t, t, (3.5) (α + β)ψ s (u) = uδψ s(u) σ2 ψ s (u) + α (3.1). ( uδe δx E{ψ sy (x)] + 1 ) 2 σ2 e 2δx E{ψ s Y (x)] dx. (3.5) ψ s (u + x)dg(x) + β ψ s (u y)df (y), ϕ s (u) ϕ(u) Φ(u), Φ(+ ) =, ψ s (+ ) = 1, (3.5) ψ s () = 1; (3.1), u. 3.2 ψ d (u), u, ψ d (u) = α (α + β)ψ d (u) uδψ d (u) 1 2 σ2 ψ d (u) ψ d (u + x)dg(x) + β ψ d (+ ) = 1, ψ s (u y)df (y) + βf (u), (3.6) ψ d () =, 1 2 σ2 ψ d () = α ψ d (x)dg(x). (3.7)
6 36 y) = 1, (3), y > u + h(t), ψ s (u + h(t) (3.6). ψ d (u) = (1 αt)(1 βt)e{ψ d u + h(t)] { + αt(1 βt)e ψ d u + h(t) + x]dg(x) { +h(t) + βt(1 αt)e + βt(1 αt)e{f u + h(t)] + o(t). ψ d u + h(t) y]df (y) ϕ d (u) ϕ(u) Φ(u), Φ(+ ) =, ψ d (+ ) = 1, (1.4) ψ d () = ; (3.6), u. : , u >, ψ(u) (α + β)ψ(u) uδψ (u) 1 2 σ2 ψ (u) = α (1.5), ψ(+ ) = 1, ψ() =, 1 2 σ2 ψ () = α ψ(u + x)dg(x) + β ψ(x)dg(x). ψ(u y)df (y), (3.8) ψ d (u) + ψ s (u) = ψ(u) + 1, ψ d (u) + ψ s(u) = ψ (u), ψ d (u) + ψ s (u) = ψ (u)., (3.1) (3.6), (3.8), (3.2) (3.7), (3.9) (3.9) Gλ 1, F λ 2, 3.1, u >, ψ s (u) (δλ 2 + βλ 1 αλ 2 δλ 1 uδλ 1 λ 2 )ψ s(u) + 2δ (α + β) 1 ] 2 σ2 λ 1 λ 2 uδ(λ 1 λ 2 ) ψ s (u) + uδ 1 ] 2 σ2 (λ 1 λ 2 ) ψ s (u) σ2 ψ s (u) =. (4.1)
7 Poisson 37 ψ s (+ ) = 1, ψ s () = 1, α 1 2 σ2 ψ s () = α F G, (3.1) = α (α + β)ψ s (u) uδψ s(u) 1 2 σ2 ψ s (u) ψ s (u + x)λ 1 e λ 1x dx + β (4.2), x 1 = u + x, y 1 = u y, = α ψ s (x)λ 1 e λ 1x dx. (α + β)ψ s (u) uδψ s(u) 1 2 σ2 ψ s (u) (4.3) u u ψ s (x 1 )λ 1 e λ 1(x 1 u) dx 1 + β ψ s (u y)λ 2 e λ 2y dy. (4.2) ψ s (y 1 )λ 2 e λ 2(u y 1 ) dy 1. (4.3) (α + β)ψ s(u) δψ s(u) uδψ s (u) 1 2 σ2 ψ s (u) + αλ 1 ψ s (u) βλ 2 ψ s (u) = λ 1 α (4.4) u ψ s (u + x)λ 1 e λ 1x dx λ 2 β ψ s (u y)λ 2 e λ 2y dy. (4.4) (α + β)ψ s (u) 2δψ s (u) uδψ s (u) 1 2 σ2 ψ s (u) + αλ 1 ψ s(u) = λ 2 1α βλ 2 ψ s(u) + λ 2 1αψ s (u) + λ 2 2βψ s (u) (4.2) (4.4) (4.5), ψ s (u + x)λ 1 e λ 1x dx λ 2 2β (δλ 2 + βλ 1 αλ 2 δλ 1 uδλ 1 λ 2 )ψ s(u) + 2δ (α + β) 1 ] 2 σ2 λ 1 λ 2 uδ(λ 1 λ 2 ) ψ s (u) + uδ 1 ] 2 σ2 (λ 1 λ 2 ) ψ s (u) σ2 ψ s (u) =. ψ s (u y)λ 2 e λ 2y dy. (4.5) (4.1). (3.2), ψ s (u).
8 u >, ψ d (u) Gλ 1, F λ 2, (δλ 2 + βλ 1 αλ 2 δλ 1 uδλ 1 λ 2 )ψ d (u) + 2δ (α + β) 1 ] 2 σ2 λ 1 λ 2 uδ(λ 1 λ 2 ) ψ d (u) + uδ 1 ] 2 σ2 (λ 1 λ 2 ) ψ d (u) σ2 ψ d (u) =, 4.1. (3.7), ψ d (u). 4.3 ψ d (+ ) = 1, ψ d () =, 1 2 σ2 ψ d () = α ψ d (x)λ 1 e λ1x dx , u >, ψ(u) (δλ 2 + βλ 1 αλ 2 δλ 1 uδλ 1 λ 2 )ψ (u) + 2δ (α + β) 1 ] 2 σ2 λ 1 λ 2 uδ(λ 1 λ 2 ) ψ (u) + uδ 1 ] 2 σ2 (λ 1 λ 2 ) ψ (u) σ2 ψ (u) =, ψ(+ ) = 1, ψ() =, 1 2 σ2 ψ () = α 3.1. ψ(x)λ 1 e λ 1x dx ψ s (u, t) u, t. u, ψ s (u, t) = α (α + β)ψ s (u, t) ψ s(u, t) uδ ψ s(u, t) ψ s (u + x, t)dg(x) + β 1 2 σ2 2 ψ s (u, t) 2 ψ s (u y, t)df (y). (5.1)
9 Poisson 39 ψ s (+, t) = 1, ψ s (u, + ) = ψ s (u). (, ], (1.2) X t. M(t) N(t) Poisson, (, ] (1) M(t) N(t), (1 α )(1 β ) + o( ). (2) M(t) N(t), α (1 β ) + o( ). (3) M(t)N(t), (1 α )β + o( ). (4) M(t) ( N(t)) (, ], M(t) N(t), o( ). (3), y > u + h( ), ψ s (u + h( ) y, t ) =, ψ s (u, t) = (1 α )(1 β )E{ψ s u + h( ), t ] { + α (1 β )E ψ s u + h( ) + x, t ]dg(x) { +h( ) + β (1 α )E (α + β) E{ψ s u + h( ), t ] { = E{ψ s u + h( ), t ] ψ s (u, t) + α E ψ s u + h( ) y, t ]df (y) + o( ). ψ s u + h( ) + x, t ]dg(x) { +h( ) + β E ψ s u + h( ) y, t ]df (y) + o( ). (5.2) Y (t) = h(t) + u, (3.3) dy ( ) = uδe δ d + σe δ dw, Y () = u. = dψ s u + h( ), t ] = dψ s Y ( ), t ] { uδe δ ψ sy ( ), t ] + ψ sy ( ), t ] σ2 e 2δ 2 ψ s Y ( ), t ] 2 d + σe δ ψ sy ( ), t ] dw.
10 4 = ψ s u + h( ), t ] = ψ s Y ( ), t ] { uδe δx ψ sy (x), t x] + ψ sy (x), t x] + ψ s (u, t) + σe δx ψ sy (x), t x] dw x σ2 e 2δx 2 ψ s Y (x), t ] 2 dx E{ψ s u + h( ), t ] ( { = ψ s (u, t) + uδe δx ψs Y (x), t x] { ψs Y (x), t x] E + E + 1 { 2 σ2 e 2δx 2 ψ s Y (x), t x] ) E 2 dx. (5.2),, (α + β)ψ s (u, t) = ψ s(u, t) (5.1). 1 + α t, (5.1) (3.1). + uδ ψ s(u, t) ψ s (u + x, t)dg(x) + β ψ s (u, t) =, t= σ2 2 ψ s (u, t) 2 ψ s (u y, t)df (y). 5.2 ψ d (u, t) u, t. u, ψ d (u, t) = α (α + β)ψ d (u, t) ψ d(u, t) uδ ψ d(u, t) ψ d (u + x, t)dg(x) + β 1 2 σ2 2 ψ d (u, t) 2 βf (u) ψ d (u y, t)df (y). (5.3) ψ d (+, t) = 1, ψ d (u, + ) = ψ d (u).
11 Poisson , (3), y > u+h( ), ψ d (u + h( ) y, t ) = 1, ψ d (u, t) = (1 α )(1 β )E{ψ d u + h( ), t ] { + α (1 β )E ψ d u + h( ) + x, t ]dg(x) (5.3). 2 { +h( ) + β (1 α )E + β (1 α )E{F (u + h( )) + o( ). t, (5.3) (3.6). : 5.1 ψ d (u, t) =, t= ψ d u + h( ) y, t ]df (y) , u >, ψ(u, t) = α (α + β)ψ(u, t) (1.6), ψ(u, t) ψ(u + x, t)dg(x) + β ψ d (u, t) + ψ s (u, t) = ψ(u, t) + 1, ψ d (u, t) + ψ s(u, t) = (5.1) (5.3) (5.4). ψ(u, t) uδ ψ(+, t) = 1, ψ(u, + ) = ψ(u). ψ(u, t), 1 2 σ2 2 ψ(u, t) 2 ψ(u y, t)df (y). (5.4) ψ d (u, t) + ψ s(u, t) ψ(u, t) =, 2 ψ d (u, t) ψ s (u, t) 2 = 2 ψ(u, t) 2. 3 t, (5.4) (3.9). ψ(u, t) =, t=
12 42 1] Asmussen, S., Ruin Probabilities, Singapore, World Scientific, 2. 2] Cai, J. and Dickson, D.C.M., On the expected discounted penalty function at ruin of a surplus process with interest, Insurance: Mathematics and Economics, 3(22), ] Palsen, J. and Gjessing, H.K., Ruin theory with stochastic economic environment, Advances in Applied Probability, 29(1997), ] Cai, J. and Yang, H.L., Ruin in the perturbed compound poisson risk process under interest force, Advances in Applied Probability, 37(3)(25), ] Sundt, B. and Teugels, J.L., Ruin estimates under interest force, Insurance: Mathematics and Economics, 16(1995), ],, Poisson,, 22(2)(26), ] Applebaum, D., Lévy Processes and Stochastic Calculus, England, Cambridge University Press, 24. 8] Paul, E., Claudia, K. and Thomas, M., Modelling Extremal Events for Insurance and Finance, New York: Springer-Verlag, The Survival Probability for the Perturbed Double Compound Poisson Risk Process under Constant Interest Force Wei Guanghua 1 Gao Qibing 2,3 Wang Xiaoqian 2 ( 1 Department of Basic Courses, Jinling Institute of Technology, Nanjing, 211 ) ( 2 School of Mathematics and Computer Science, Nanjing Normal University, Nanjing, 2197 ) ( 3 Department of mathematics, Southeast University, Nanjing, 2197 ) In this paper, we consider the perturbed double compound Poisson risk process under constant interest force. Exponential type upper bounds are obtained for the ultimate ruin probability of this risk model by the way of martingale. For infinite time and finite time survival probabilities, we obtain the respective integro-differential equations. When the premiums are exponentially distributed, some differential equations are derived for infinite time survival probability. Keywords: Double compound Poisson risk process, Brown motion, jump-diffusion process, survival probability, integro-differential equations. AMS Subject Classification: 6G5.
: 29 : n ( ),,. T, T +,. y ij i =, 2,, n, j =, 2,, T, y ij y ij = β + jβ 2 + α i + ɛ ij i =, 2,, n, j =, 2,, T, (.) β, β 2,. jβ 2,. β, β 2, α i i, ɛ i
2009 6 Chinese Journal of Applied Probability and Statistics Vol.25 No.3 Jun. 2009 (,, 20024;,, 54004).,,., P,. :,,. : O22... (Credibility Theory) 20 20, 80. ( []).,.,,,.,,,,.,. Buhlmann Buhlmann-Straub
More information➀ ➁ ➂ ➃ ➄ ➅ ➆ ➇ ➈ ➉ Lecture on Stochastic Processes (by Lijun Bo) 2
Stochastic Processes stoprocess@yahoo.com.cn 111111 ➀ ➁ ➂ ➃ ➄ ➅ ➆ ➇ ➈ ➉ Lecture on Stochastic Processes (by Lijun Bo) 2 : Stochastic Processes? (Ω, F, P), I t I, X t (Ω, F, P), X = {X t, t I}, X t (ω)
More information458 (25),. [1 4], [5, 6].,, ( ).,,, ;,,,. Xie Li (28),,. [9] HJB,,,, Legendre [7, 8],.,. 2. ( ), x = x x = x x x2 n x = (x 1, x 2,..., x
212 1 Chinese Journal of Applied Probability and Statistics Vol.28 No.5 Oct. 212 (,, 3387;,, 372) (,, 372)., HJB,. HJB, Legendre.,. :,,, Legendre,,,. : F83.48, O211.6. 1.,.,,. 199, Sharpe Tint (199),.,
More informationΖ # % & ( ) % + & ) / 0 0 1 0 2 3 ( ( # 4 & 5 & 4 2 2 ( 1 ) ). / 6 # ( 2 78 9 % + : ; ( ; < = % > ) / 4 % 1 & % 1 ) 8 (? Α >? Β? Χ Β Δ Ε ;> Φ Β >? = Β Χ? Α Γ Η 0 Γ > 0 0 Γ 0 Β Β Χ 5 Ι ϑ 0 Γ 1 ) & Ε 0 Α
More informationStochastic Processes (XI) Hanjun Zhang School of Mathematics and Computational Science, Xiangtan University 508 YiFu Lou talk 06/
Stochastic Processes (XI) Hanjun Zhang School of Mathematics and Computational Science, Xiangtan University hjzhang001@gmail.com 508 YiFu Lou talk 06/04/2010 - Page 1 Outline 508 YiFu Lou talk 06/04/2010
More information! # % & ( & # ) +& & # ). / 0 ) + 1 0 2 & 4 56 7 8 5 0 9 7 # & : 6/ # ; 4 6 # # ; < 8 / # 7 & & = # < > 6 +? # Α # + + Β # Χ Χ Χ > Δ / < Ε + & 6 ; > > 6 & > < > # < & 6 & + : & = & < > 6+?. = & & ) & >&
More information[9] R Ã : (1) x 0 R A(x 0 ) = 1; (2) α [0 1] Ã α = {x A(x) α} = [A α A α ]. A(x) Ã. R R. Ã 1 m x m α x m α > 0; α A(x) = 1 x m m x m +
2012 12 Chinese Journal of Applied Probability and Statistics Vol.28 No.6 Dec. 2012 ( 224002) Euclidean Lebesgue... :. : O212.2 O159. 1.. Zadeh [1 2]. Tanaa (1982) ; Diamond (1988) (FLS) FLS LS ; Savic
More informationuntitled
arctan lim ln +. 6 ( + ). arctan arctan + ln 6 lim lim lim y y ( ln ) lim 6 6 ( + ) y + y dy. d y yd + dy ln d + dy y ln d d dy, dy ln d, y + y y dy dy ln y+ + d d y y ln ( + ) + dy d dy ln d dy + d 7.
More information! Ν! Ν Ν & ] # Α. 7 Α ) Σ ),, Σ 87 ) Ψ ) +Ε 1)Ε Τ 7 4, <) < Ε : ), > 8 7
!! # & ( ) +,. )/ 0 1, 2 ) 3, 4 5. 6 7 87 + 5 1!! # : ;< = > < < ;?? Α Β Χ Β ;< Α? 6 Δ : Ε6 Χ < Χ Α < Α Α Χ? Φ > Α ;Γ ;Η Α ;?? Φ Ι 6 Ε Β ΕΒ Γ Γ > < ϑ ( = : ;Α < : Χ Κ Χ Γ? Ε Ι Χ Α Ε? Α Χ Α ; Γ ;
More information第9章 排队论
9, 9. 9.. Nt () [, t] t Nt () { Nt ( ) t [, T]} t< t< t< t + N ( ( t+ ) i+ N( t) i, N( t) i,, N( t) i N + + N ( ( t ) i ( t ) i ) (9-) { Nt ( ) t [, T)} 9- t t + t, t,, t t t { Nt ( ) t [, T] } t< t,,
More information国学思想与大学数学
Pure Mathematics 理 论 数 学, 2013, 3, 201-206 http://dx.doi.org/10.12677/pm.2013.33030 Published Online May 2013 (http://www.hanspub.org/journal/pm.html) Chinese Traditional Culture and College Mathematics
More informationΑ 3 Α 2Η # # > # 8 6 5# Ι + ϑ Κ Ι Ι Ι Η Β Β Β Β Β Β ΔΕ Β Β Γ 8 < Φ Α Α # >, 0 Η Λ Μ Ν Ο Β 8 1 Β Π Θ 1 Π Β 0 Λ Μ 1 Ρ 0 Μ ϑ Σ ϑ Τ Ο Λ 8 ϑ
! # % & ( ) % + ( ), & ). % & /. % 0 1!! 2 3 4 5# 6 7 8 3 5 5 9 # 8 3 3 2 4 # 3 # # 3 # 3 # 3 # 3 # # # ( 3 # # 3 5 # # 8 3 6 # # # # # 8 5# :;< 6#! 6 =! 6 > > 3 2?0 1 4 3 4! 6 Α 3 Α 2Η4 3 3 2 4 # # >
More informationΡ Τ Π Υ 8 ). /0+ 1, 234) ς Ω! Ω! # Ω Ξ %& Π 8 Δ, + 8 ),. Ψ4) (. / 0+ 1, > + 1, / : ( 2 : / < Α : / %& %& Ζ Θ Π Π 4 Π Τ > [ [ Ζ ] ] %& Τ Τ Ζ Ζ Π
! # % & ( ) + (,. /0 +1, 234) % 5 / 0 6/ 7 7 & % 8 9 : / ; 34 : + 3. & < / = : / 0 5 /: = + % >+ ( 4 : 0, 7 : 0,? & % 5. / 0:? : / : 43 : 2 : Α : / 6 3 : ; Β?? : Α 0+ 1,4. Α? + & % ; 4 ( :. Α 6 4 : & %
More information&! +! # ## % & #( ) % % % () ) ( %
&! +! # ## % & #( ) % % % () ) ( % &! +! # ## % & #( ) % % % () ) ( % ,. /, / 0 0 1,! # % & ( ) + /, 2 3 4 5 6 7 8 6 6 9 : / ;. ; % % % % %. ) >? > /,,
More information) & ( +,! (# ) +. + / & 6!!!.! (!,! (! & 7 6!. 8 / ! (! & 0 6! (9 & 2 7 6!! 3 : ; 5 7 6! ) % (. ()
! # % & & &! # % &! ( &! # )! ) & ( +,! (# ) +. + / 0 1 2 3 4 4 5 & 6!!!.! (!,! (! & 7 6!. 8 / 6 7 6 8! (! & 0 6! (9 & 2 7 6!! 3 : ; 5 7 6! ) % (. () , 4 / 7!# + 6 7 1 1 1 0 7!.. 6 1 1 2 1 3
More informationWL100014ZW.PDF
A Z 1 238 H U 1 92 1 2 3 1 1 1 H H H 235 238 92 U 92 U 1.1 2 1 H 3 1 H 3 2 He 4 2 He 6 3 Hi 7 3 Hi 9 4 Be 10 5 B 2 1.113MeV H 1 4 2 He B/ A =7.075MeV 4 He 238 94 Pu U + +5.6MeV 234 92 2 235 U + 200MeV
More information普通高等学校本科专业设置管理规定
普 通 高 等 学 校 本 科 专 业 设 置 申 请 表 ( 备 案 专 业 适 用 ) 学 校 名 称 ( 盖 章 ): 学 校 主 管 部 门 : 专 业 名 称 : 浙 江 外 国 语 学 院 浙 江 省 教 育 厅 金 融 工 程 专 业 代 码 : 020302 所 属 学 科 门 类 及 专 业 类 : 金 融 学 / 金 融 工 程 类 学 位 授 予 门 类 : 修 业 年 限 :
More informationVol. 15 No. 1 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Feb O21 A
5 200 2 Vol 5 No JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Feb 200 2 2 50080 2 30024 O2 A 007-2683 200 0-0087- 05 A Goodness-of-fit Test Based on Empirical Likelihood and Application ZHOU
More information4= 8 4 < 4 ϑ = 4 ϑ ; 4 4= = 8 : 4 < : 4 < Κ : 4 ϑ ; : = 4 4 : ;
! #! % & ( ) +!, + +!. / 0 /, 2 ) 3 4 5 6 7 8 8 8 9 : 9 ;< 9 = = = 4 ) > (/?08 4 ; ; 8 Β Χ 2 ΔΔ2 4 4 8 4 8 4 8 Ε Φ Α, 3Γ Η Ι 4 ϑ 8 4 ϑ 8 4 8 4 < 8 4 5 8 4 4
More information., /,, 0!, + & )!. + + (, &, & 1 & ) ) 2 2 ) 1! 2 2
! # &!! ) ( +, ., /,, 0!, + & )!. + + (, &, & 1 & ) ) 2 2 ) 1! 2 2 ! 2 2 & & 1 3! 3, 4 45!, 2! # 1 # ( &, 2 &, # 7 + 4 3 ) 8. 9 9 : ; 4 ), 1!! 4 4 &1 &,, 2! & 1 2 1! 1! 1 & 2, & 2 & < )4 )! /! 4 4 &! &,
More informationChinese Journal of Applied Probability and Statistics Vol.25 No.4 Aug (,, ;,, ) (,, ) 应用概率统计 版权所有, Zhang (2002). λ q(t)
2009 8 Chinese Journal of Applied Probability and Statistics Vol.25 No.4 Aug. 2009,, 541004;,, 100124),, 100190), Zhang 2002). λ qt), Kolmogorov-Smirov, Berk and Jones 1979). λ qt).,,, λ qt),. λ qt) 1,.
More information: ; 8 Β < : Β Δ Ο Λ Δ!! Μ Ν : ; < 8 Λ Δ Π Θ 9 : Θ = < : ; Δ < 46 < Λ Ρ 0Σ < Λ 0 Σ % Θ : ;? : : ; < < <Δ Θ Ν Τ Μ Ν? Λ Λ< Θ Ν Τ Μ Ν : ; ; 6 < Λ 0Σ 0Σ >
! # %& ( +, &. / ( 0 # 1# % & # 2 % & 4 5 67! 8 9 : ; < 8 = > 9? 8 < 9? Α,6 ΒΧ : Δ 8Ε 9 %: ; < ; ; Δ Φ ΓΗ Ιϑ 4 Κ6 : ; < < > : ; : ;!! Β : ; 8 Β < : Β Δ Ο Λ Δ!! Μ Ν : ; < 8 Λ Δ Π Θ 9 : Θ = < : ; Δ < 46
More information! /. /. /> /. / Ε Χ /. 2 5 /. /. / /. 5 / Φ0 5 7 Γ Η Ε 9 5 /
! # %& ( %) & +, + % ) # % % ). / 0 /. /10 2 /3. /!. 4 5 /6. /. 7!8! 9 / 5 : 6 8 : 7 ; < 5 7 9 1. 5 /3 5 7 9 7! 4 5 5 /! 7 = /6 5 / 0 5 /. 7 : 6 8 : 9 5 / >? 0 /.? 0 /1> 30 /!0 7 3 Α 9 / 5 7 9 /. 7 Β Χ9
More information, ( 6 7 8! 9! (, 4 : : ; 0.<. = (>!? Α% ), Β 0< Χ 0< Χ 2 Δ Ε Φ( 7 Γ Β Δ Η7 (7 Ι + ) ϑ!, 4 0 / / 2 / / < 5 02
! # % & ( ) +, ) %,! # % & ( ( ) +,. / / 01 23 01 4, 0/ / 5 0 , ( 6 7 8! 9! (, 4 : : ; 0.!? Α% ), Β 0< Χ 0< Χ 2 Δ Ε Φ( 7 Γ Β Δ 5 3 3 5 3 1 Η7 (7 Ι + ) ϑ!, 4 0 / / 2 / 3 0 0 / < 5 02 Ν!.! %) / 0
More information8 9 8 Δ 9 = 1 Η Ι4 ϑ< Κ Λ 3ϑ 3 >1Ε Μ Ε 8 > = 8 9 =
!! % & ( & ),,., / 0 1. 0 0 3 4 0 5 3 6!! 7 8 9 8!! : ; < = > :? Α 4 8 9 < Β Β : Δ Ε Δ Α = 819 = Γ 8 9 8 Δ 9 = 1 Η Ι4 ϑ< Κ Λ 3ϑ 3 >1Ε 8 9 0 Μ Ε 8 > 9 8 9 = 8 9 = 819 8 9 =
More informationΒ 8 Α ) ; %! #?! > 8 8 Χ Δ Ε ΦΦ Ε Γ Δ Ε Η Η Ι Ε ϑ 8 9 :! 9 9 & ϑ Κ & ϑ Λ &! &!! 4!! Μ Α!! ϑ Β & Ν Λ Κ Λ Ο Λ 8! % & Π Θ Φ & Ρ Θ & Θ & Σ ΠΕ # & Θ Θ Σ Ε
! #!! % & ( ) +,. /. 0,(,, 2 4! 6! #!!! 8! &! % # & # &! 9 8 9 # : : : : :!! 9 8 9 # #! %! ; &! % + & + & < = 8 > 9 #!!? Α!#!9 Α 8 8!!! 8!%! 8! 8 Β 8 Α ) ; %! #?! > 8 8 Χ Δ Ε ΦΦ Ε Γ Δ Ε Η Η Ι Ε ϑ 8 9 :!
More information/ Ν #, Ο / ( = Π 2Θ Ε2 Ρ Σ Π 2 Θ Ε Θ Ρ Π 2Θ ϑ2 Ρ Π 2 Θ ϑ2 Ρ Π 23 8 Ρ Π 2 Θϑ 2 Ρ Σ Σ Μ Π 2 Θ 3 Θ Ρ Κ2 Σ Π 2 Θ 3 Θ Ρ Κ Η Σ Π 2 ϑ Η 2 Ρ Π Ρ Π 2 ϑ Θ Κ Ρ Π
! # #! % & ( ) % # # +, % #. % ( # / ) % 0 1 + ) % 2 3 3 3 4 5 6 # 7 % 0 8 + % 8 + 9 ) 9 # % : ; + % 5! + )+)#. + + < ) ( # )# < # # % 0 < % + % + < + ) = ( 0 ) # + + # % )#!# +), (? ( # +) # + ( +. #!,
More information% %! # % & ( ) % # + # # % # # & & % ( #,. %
!!! # #! # % & % %! # % & ( ) % # + # # % # # & & % ( #,. % , ( /0 ) %, + ( 1 ( 2 ) + %, ( 3, ( 123 % & # %, &% % #, % ( ) + & &% & ( & 4 ( & # 4 % #, #, ( ) + % 4 % & &, & & # / / % %, &% ! # #! # # #
More information) Μ <Κ 1 > < # % & ( ) % > Χ < > Δ Χ < > < > / 7 ϑ Ν < Δ 7 ϑ Ν > < 8 ) %2 ): > < Ο Ε 4 Π : 2 Θ >? / Γ Ι) = =? Γ Α Ι Ρ ;2 < 7 Σ6 )> Ι= Η < Λ 2 % & 1 &
! # % & ( ) % + ),. / & 0 1 + 2. 3 ) +.! 4 5 2 2 & 5 0 67 1) 8 9 6.! :. ;. + 9 < = = = = / >? Α ) /= Β Χ Β Δ Ε Β Ε / Χ ΦΓ Χ Η Ι = = = / = = = Β < ( # % & ( ) % + ),. > (? Φ?? Γ? ) Μ
More information%% &% %% %% %% % () (! #! %!!!!!!!%! # %& ( % & ) +, # (.. /,) %& 0
!! # # %% &% %% %% %% % () (! #! %!!!!!!!%! # %& ( % & ) +, # (.. /,) %& 0 +! (%& / 1! 2 %& % & 0/ / %& + (.%.%, %& % %& )& % %& ) 3, &, 5, % &. ) 4 4 4 %& / , %& ).. % # 6 /0 % &. & %& ) % %& 0.!!! %&
More information( )
( ) * 22 2 29 2......................................... 2.2........................................ 3 3..................................... 3.2.............................. 3 2 4 2........................................
More information! + +, ) % %.!&!, /! 0! 0 # ( ( # (,, # ( % 1 2 ) (, ( 4! 0 & 2 /, # # ( &
! # %! &! #!! %! %! & %! &! & ( %! & #! & )! & & + ) +!!, + ! + +, ) % %.!&!, /! 0! 0 # ( ( # (,, # ( % 1 2 ) (, 3 0 1 ( 4! 0 & 2 /, # # ( 1 5 2 1 & % # # ( #! 0 ) + 4 +, 0 #,!, + 0 2 ), +! 0! 4, +! (!
More information8 9 < ; ; = < ; : < ;! 8 9 % ; ϑ 8 9 <; < 8 9 <! 89! Ε Χ ϑ! ϑ! ϑ < ϑ 8 9 : ϑ ϑ 89 9 ϑ ϑ! ϑ! < ϑ < = 8 9 Χ ϑ!! <! 8 9 ΧΧ ϑ! < < < < = 8 9 <! = 8 9 <! <
! # % ( ) ( +, +. ( / 0 1) ( 2 1 1 + ( 3 4 5 6 7! 89 : ; 8 < ; ; = 9 ; ; 8 < = 9! ; >? 8 = 9 < : ; 8 < ; ; = 9 8 9 = : : ; = 8 9 = < 8 < 9 Α 8 9 =; %Β Β ; ; Χ ; < ; = :; Δ Ε Γ Δ Γ Ι 8 9 < ; ; = < ; :
More information➀ ➁ ➂ ➃ Lecture on Stochastic Processes (by Lijun Bo) 2
Stochastic Processes stoprocess@yahoo.com.cn 111111 ➀ ➁ ➂ ➃ Lecture on Stochastic Processes (by Lijun Bo) 2 (Stationary Processes) X = {X t ; t I}, n 1 t 1,..., t n I, n F n (t 1,..., t n ; x 1,..., x
More information! # % & # % & ( ) % % %# # %+ %% % & + %, ( % % &, & #!.,/, % &, ) ) ( % %/ ) %# / + & + (! ) &, & % & ( ) % % (% 2 & % ( & 3 % /, 4 ) %+ %( %!
! # # % & ( ) ! # % & # % & ( ) % % %# # %+ %% % & + %, ( % % &, & #!.,/, % &, ) ) ( % %/ ) 0 + 1 %# / + & + (! ) &, & % & ( ) % % (% 2 & % ( & 3 % /, 4 ) %+ %( %! # ( & & 5)6 %+ % ( % %/ ) ( % & + %/
More information. () ; () ; (3) ; (4).. () : P.4 3.4; P. A (3). () : P. A (5)(6); B. (3) : P.33 A (9),. (4) : P. B 5, 7(). (5) : P.8 3.3; P ; P.89 A 7. (6) : P.
() * 3 6 6 3 9 4 3 5 8 6 : 3. () ; () ; (3) (); (4) ; ; (5) ; ; (6) ; (7) (); (8) (, ); (9) ; () ; * Email: huangzh@whu.edu.cn . () ; () ; (3) ; (4).. () : P.4 3.4; P. A (3). () : P. A (5)(6); B. (3) :
More informationCauchy Duhamel Cauchy Cauchy Poisson Cauchy 1. Cauchy Cauchy ( Duhamel ) u 1 (t, x) u tt c 2 u xx = f 1 (t, x) u 2 u tt c 2 u xx = f 2 (
Cauchy Duhamel Cauchy CauchyPoisson Cauchy 1. Cauchy Cauchy ( Duhamel) 1.1.......... u 1 (t, x) u tt c 2 u xx = f 1 (t, x) u 2 u tt c 2 u xx = f 2 (t, x) 1 C 1 C 2 u(t, x) = C 1 u 1 (t, x) + C 2 u 2 (t,
More information07-3.indd
1 2 3 4 5 6 7 08 11 19 26 31 35 38 47 52 59 64 67 73 10 18 29 76 77 78 79 81 84 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
More information> # ) Β Χ Χ 7 Δ Ε Φ Γ 5 Η Γ + Ι + ϑ Κ 7 # + 7 Φ 0 Ε Φ # Ε + Φ, Κ + ( Λ # Γ Κ Γ # Κ Μ 0 Ν Ο Κ Ι Π, Ι Π Θ Κ Ι Π ; 4 # Ι Π Η Κ Ι Π. Ο Κ Ι ;. Ο Κ Ι Π 2 Η
1 )/ 2 & +! # % & ( ) +, + # # %. /& 0 4 # 5 6 7 8 9 6 : : : ; ; < = > < # ) Β Χ Χ 7 Δ Ε Φ Γ 5 Η Γ + Ι + ϑ Κ 7 # + 7 Φ 0 Ε Φ # Ε + Φ, Κ + ( Λ # Γ Κ Γ #
More information. /!Ι Γ 3 ϑκ, / Ι Ι Ι Λ, Λ +Ι Λ +Ι
! # % & ( ) +,& ( + &. / 0 + 1 0 + 1,0 + 2 3., 0 4 2 /.,+ 5 6 / 78. 9: ; < = : > ; 9? : > Α
More informationDS Ω(1.1)t 1 t 2 Q = t2 t 1 { S k(x, y, z) u } n ds dt, (1.2) u us n n (t 1, t 2 )u(t 1, x, y, z) u(t 2, x, y, z) Ω ν(x, y, z)ρ(x, y, z)[u(t 2, x, y,
u = u(t, x 1, x 2,, x n ) u t = k u kn = 1 n = 3 n = 3 Cauchy ()Fourier Li-Yau Hanarck tcauchy F. JohnPartial Differential Equations, Springer-Verlag, 1982. 1. 1.1 Du(t, x, y, z)d(x, y, z) t Fourier dtn
More information2 2 Λ ϑ Δ Χ Δ Ι> 5 Λ Λ Χ Δ 5 Β. Δ Ι > Ε!!Χ ϑ : Χ Ε ϑ! ϑ Β Β Β ϑ Χ Β! Β Χ 5 ϑ Λ ϑ % < Μ / 4 Ν < 7 :. /. Ο 9 4 < / = Π 7 4 Η 7 4 =
! # % # & ( ) % # ( +, & % # ) % # (. / ). 1 2 3 4! 5 6 4. 7 8 9 4 : 2 ; 4 < = = 2 >9 3? & 5 5 Α Α 1 Β ΧΔ Ε Α Φ 7 Γ 9Η 8 Δ Ι > Δ / ϑ Κ Α Χ Ε ϑ Λ ϑ 2 2 Λ ϑ Δ Χ Δ Ι> 5 Λ Λ Χ Δ 5 Β. Δ Ι > Ε!!Χ ϑ : Χ Ε ϑ!
More information4 # = # 4 Γ = 4 0 = 4 = 4 = Η, 6 3 Ι ; 9 Β Δ : 8 9 Χ Χ ϑ 6 Κ Δ ) Χ 8 Λ 6 ;3 Ι 6 Χ Δ : Χ 9 Χ Χ ϑ 6 Κ
! # % & & ( ) +, %. % / 0 / 2 3! # 4 ) 567 68 5 9 9 : ; > >? 3 6 7 : 9 9 7 4! Α = 42 6Β 3 Χ = 42 3 6 3 3 = 42 : 0 3 3 = 42 Δ 3 Β : 0 3 Χ 3 = 42 Χ Β Χ 6 9 = 4 =, ( 9 6 9 75 3 6 7 +. / 9
More information诸病源候论
诸 病 源 候 论 巢 元 方 六 二 易 学 中 医 网 提 供 千 本 中 医 易 学 电 子 书 的 在 线 阅 读 和 下 载 服 务, 本 网 为 弘 扬 中 国 传 统 文 化, 振 兴 中 医 提 供 一 个 平 台, 尽 自 己 一 点 微 薄 之 力. 我 们 还 有 一 个 姊 妹 网 站, 蓄 德 网, 提 供 道 家 和 其 他 宗 教 的 书 籍, 包 含 整 部 道 藏,
More information= Υ Ξ & 9 = ) %. Ο) Δ Υ Ψ &Ο. 05 3; Ι Ι + 4) &Υ ϑ% Ο ) Χ Υ &! 7) &Ξ) Ζ) 9 [ )!! Τ 9 = Δ Υ Δ Υ Ψ (
! # %! & (!! ) +, %. ( +/ 0 1 2 3. 4 5 6 78 9 9 +, : % % : < = % ;. % > &? 9! ) Α Β% Χ %/ 3. Δ 8 ( %.. + 2 ( Φ, % Γ Η. 6 Γ Φ, Ι Χ % / Γ 3 ϑκ 2 5 6 Χ8 9 9 Λ % 2 Χ & % ;. % 9 9 Μ3 Ν 1 Μ 3 Φ Λ 3 Φ ) Χ. 0
More information9!!!! #!! : ;!! <! #! # & # (! )! & ( # # #+
! #! &!! # () +( +, + ) + (. ) / 0 1 2 1 3 4 1 2 3 4 1 51 0 6. 6 (78 1 & 9!!!! #!! : ;!! ? &! : < < &? < Α!!&! : Χ / #! : Β??. Δ?. ; ;
More information!! )!!! +,./ 0 1 +, 2 3 4, # 8,2 6, 2 6,,2 6, 2 6 3,2 6 5, 2 6 3, 2 6 9!, , 2 6 9, 2 3 9, 2 6 9,
! # !! )!!! +,./ 0 1 +, 2 3 4, 23 3 5 67 # 8,2 6, 2 6,,2 6, 2 6 3,2 6 5, 2 6 3, 2 6 9!, 2 6 65, 2 6 9, 2 3 9, 2 6 9, 2 6 3 5 , 2 6 2, 2 6, 2 6 2, 2 6!!!, 2, 4 # : :, 2 6.! # ; /< = > /?, 2 3! 9 ! #!,!!#.,
More information,!! #! > 1? = 4!! > = 5 4? 2 Α Α!.= = 54? Β. : 2>7 2 1 Χ! # % % ( ) +,. /0, , ) 7. 2
! # %!% # ( % ) + %, ). ) % %(/ / %/!! # %!! 0 1 234 5 6 2 7 8 )9!2: 5; 1? = 4!! > = 5 4? 2 Α 7 72 1 Α!.= = 54?2 72 1 Β. : 2>7 2 1 Χ! # % % ( ) +,.
More information( ) (! +)! #! () % + + %, +,!#! # # % + +!
!! # % & & & &! # # % ( ) (! +)! #! () % + + %, +,!#! # # % + +! ! %!!.! /, ()!!# 0 12!# # 0 % 1 ( ) #3 % & & () (, 3)! #% % 4 % + +! (!, ), %, (!!) (! 3 )!, 1 4 ( ) % % + % %!%! # # !)! % &! % () (! %
More informationECONOMIST [ 2 Malmquist Malmquist TFP TFP Malmquist TFP Malmquist DEA DEA - Malmquist [ 3 Fuss(1994 ) Bell Madden Savage (1999 ) [ 4 Malmquis
DOI:10.16158/j.cnki.51-1312/f.2010.10.010 2010. 10 DEA - Malmquist ( 710061 ) DEA - Malmquist 2003 2008 2003 2008 TFP 11. 3% TFP 1. 005 1. 107 TFP 2004 2006 2004 2006 2006 F014. 2 A 1003 5656 ( 2010 )
More informationΠ Ρ! #! % & #! (! )! + %!!. / 0% # 0 2 3 3 4 7 8 9 Δ5?? 5 9? Κ :5 5 7 < 7 Δ 7 9 :5? / + 0 5 6 6 7 : ; 7 < = >? : Α8 5 > :9 Β 5 Χ : = 8 + ΑΔ? 9 Β Ε 9 = 9? : ; : Α 5 9 7 3 5 > 5 Δ > Β Χ < :? 3 9? 5 Χ 9 Β
More information8 9 : < : 3, 1 4 < 8 3 = >? 4 =?,( 3 4 1( / =? =? : 3, : 4 9 / < 5 3, ; > 8? : 5 4 +? Α > 6 + > 3, > 5 <? 9 5 < =, Β >5
0 ( 1 0 % (! # % & ( ) + #,. / / % (! 3 4 5 5 5 3 4,( 7 8 9 /, 9 : 6, 9 5,9 8,9 7 5,9!,9 ; 6 / 9! # %#& 7 8 < 9 & 9 9 : < 5 ( ) 8 9 : < : 3, 1 4 < 8 3 = >? 4 =?,( 3 4 1( / =? =? : 3, : 4 9 / < 5 3, 5 4
More information.., + +, +, +, +, +, +,! # # % ( % ( / 0!% ( %! %! % # (!) %!%! # (!!# % ) # (!! # )! % +,! ) ) &.. 1. # % 1 ) 2 % 2 1 #% %! ( & # +! %, %. #( # ( 1 (
! # %! % &! # %#!! #! %!% &! # (!! # )! %!! ) &!! +!( ), ( .., + +, +, +, +, +, +,! # # % ( % ( / 0!% ( %! %! % # (!) %!%! # (!!# % ) # (!! # )! % +,! ) ) &.. 1. # % 1 ) 2 % 2 1 #% %! ( & # +! %, %. #(
More information# % & ) ) & + %,!# & + #. / / & ) 0 / 1! 2
!!! #! # % & ) ) & + %,!# & + #. / / & ) 0 / 1! 2 % ) 1 1 3 1 4 5 % #! 2! 1,!!! /+, +!& 2! 2! / # / 6 2 6 3 1 2 4 # / &!/ % ). 1!!! &! & 7 2 7! 7 6 7 3 & 1 2 % # ) / / 8 2 6,!!! /+, +! & 2 9! 3 1!! % %
More information2005 9 7 1 6 00 9 00 205 2 ( 010 ) 82030989 ( ) E-mail zy0989@sohu.com 3 52763225 E-mail luoqj@pku.edu.cn 62764414 E-mail c.y.wang@hotmail.com 4 3 19 57 5 Romer, David ( 1996 ), Advanced Macroeconomics
More information( ) 1 2 1 3 1 11 1 12 1 n( n 1) 2 2 1 2 4 100 1 3 4 5 6 7 8 9 10 11 12 6 1 3 7 9 11 8 8 6 7 10 13 14 15 16 18 20 21 23 24 8 4 1 5 11 1 1 2 1 100 3 1 3 2 1. 2. 3.
More informationNo. : Bloch 683 µ, Bloch B ω B µ Bloch B ω,0 B µ,0,. Bloch Bloch [6 10]. [5] D n, Bloch Bloch., C,.,. ).1 f B log U n ), f + n ) f Blog., z > 1 e e =
Vol. 38 018 ) No. J. of Math. PRC) Bloch,,, 310018) : C n Bloch Bloch.,, Bloch. : ; Bloch ; ; ; MR010) : 7B38; 7B33 : O17.56 : A : 055-7797018)0-068-11 1 U n = {z = z 1, z,, z n ) : z i < 1, i = 1,,, n}
More information% 82. 8% You & Kobayashi % 2007 %
202 2 * 5. 6 995 2008 0 2008 28% Meng 2003 2000 2009 9. 4% 47% 200 % 2% 20% 50% Gruber & Yelowitz 999 Engen & Gruber 200 Chou et al. 2003 200 200 200 Brown et al. 200 2003 2003 2006 2006 * 00084 wubzh@
More informationuntitled
4 y l y y y l,, (, ) ' ( ) ' ( ) y, y f ) ( () f f ( ) (l ) t l t lt l f ( t) f ( ) t l f ( ) d (l ) C f ( ) C, f ( ) (l ) L y dy yd π y L y cosθ, π θ : siθ, π yd dy L [ cosθ cosθ siθ siθ ] dθ π π π si
More informationVol. 36 ( 2016 ) No. 6 J. of Math. (PRC) HS, (, ) :. HS,. HS. : ; HS ; ; Nesterov MR(2010) : 90C05; 65K05 : O221.1 : A : (2016)
Vol. 36 ( 6 ) No. 6 J. of Math. (PRC) HS, (, 454) :. HS,. HS. : ; HS ; ; Nesterov MR() : 9C5; 65K5 : O. : A : 55-7797(6)6-9-8 ū R n, A R m n (m n), b R m, b = Aū. ū,,., ( ), l ū min u s.t. Au = b, (.)
More information! ΑΒ 9 9 Χ! Δ? Δ 9 7 Χ = Δ ( 9 9! Δ! Δ! Δ! 8 Δ! 7 7 Δ Δ 2! Χ Δ = Χ! Δ!! =! ; 9 7 Χ Χ Χ <? < Χ 8! Ε (9 Φ Γ 9 7! 9 Δ 99 Φ Γ Χ 9 Δ 9 9 Φ Γ = Δ 9 2
! # % ( % ) +,#./,# 0 1 2 / 1 4 5 6 7 8! 9 9 : ; < 9 9 < ; ?!!#! % ( ) + %,. + ( /, 0, ( 1 ( 2 0% ( ),..# % (., 1 4 % 1,, 1 ), ( 1 5 6 6 # 77 ! ΑΒ 9 9 Χ! Δ? Δ 9 7 Χ = Δ ( 9 9! Δ! Δ! Δ! 8 Δ!
More information1938 (Ph.D) 1940 (D.Sci) 1940 (Kai-Lai Chung) Lebesgue-Stieltjes [6] ( [22]) 1942 (1941 ) 1945 J. Neyman H. Hotelling ( ) (University of Cali
1910 9 1 1 () 1925 1928 () (E. A. Poe) 1931 1933 1934 (Osgood, 1864-1943) ( ) A note on the indices and numbers of nondegenerate critical points of biharmonic functions, 1935 1936 (University College London)
More informationPowerPoint 演示文稿
. ttp://www.reej.com 4-9-9 4-9-9 . a b { } a b { }. Φ ϕ ϕ ϕ { } Φ a b { }. ttp://www.reej.com 4-9-9 . ~ ma{ } ~ m m{ } ~ m~ ~ a b but m ~ 4-9-9 4 . P : ; Φ { } { ϕ ϕ a a a a a R } P pa ttp://www.reej.com
More informationuntitled
998 + + lim =.. ( + + ) ( + + + ) = lim ( ) = lim = lim =. lim + + = lim + = lim lim + =. ( ) ~ 3 ( + u) λ.u + = + + 8 + o = + 8 + o ( ) λ λ λ + u = + λu+ u + o u,,,! + + + o( ) lim 8 8 o( ) = lim + =
More informationϑ 3 : Α 3 Η ϑ 1 Ι Η Ι + Ι 5 Κ ϑ Λ Α ΜΛ Ν Ν Ν Ν Α Γ Β 1 Α Ο Α : Α 3. / Π Ο 3 Π Θ
# % & ( ) +,& ( + &. / 0 1 2 3 ( 4 4 5 4 6 7 8 4 6 5 4 9 :.; 8 0/ ( 6 7 > 5?9 > 56 Α / Β Β 5 Χ 5.Δ5 9 Ε 8 Φ 64 4Γ Β / Α 3 Γ Β > 2 ϑ 3 : Α 3 Η ϑ 1 Ι Η Ι + Ι 5 Κ ϑ Λ Α ΜΛ Ν Ν Ν Ν 3 3 3 Α3 3
More informationLecture #4: Several notes 1. Recommend this book, see Chap and 3 for the basics about Matlab. [1] S. C. Chapra, Applied Numerical Methods with MATLAB
Chapter Lecture #4: Several notes 1. Recommend this book, see Chap and 3 for the basics about Matlab. [1] S. C. Chapra, Applied Numerical Methods with MATLAB for Engineers and Scientists. New York: McGraw-Hill,
More informationIntroduction to Hamilton-Jacobi Equations and Periodic Homogenization
Introduction to Hamilton-Jacobi Equations and Periodic Yu-Yu Liu NCKU Math August 22, 2012 Yu-Yu Liu (NCKU Math) H-J equation and August 22, 2012 1 / 15 H-J equations H-J equations A Hamilton-Jacobi equation
More information微积分 授课讲义
2018 10 aiwanjun@sjtu.edu.cn 1201 / 18:00-20:20 213 14:00-17:00 I II Taylor : , n R n : x = (x 1, x 2,..., x n ) R; x, x y ; δ( ) ; ; ; ; ; ( ) ; ( / ) ; ; Ů(P 1,δ) P 1 U(P 0,δ) P 0 Ω P 1: 1.1 ( ). Ω
More information& &((. ) ( & ) 6 0 &6,: & ) ; ; < 7 ; = = ;# > <# > 7 # 0 7#? Α <7 7 < = ; <
! # %& ( )! & +, &. / 0 # # 1 1 2 # 3 4!. &5 (& ) 6 0 0 2! +! +( &) 6 0 7 & 6 8. 9 6 &((. ) 6 4. 6 + ( & ) 6 0 &6,: & )6 0 3 7 ; ; < 7 ; = = ;# > 7 # 0 7#? Α
More information6 Β Χ Η Ι ϑ Κ 1 1 Δ 1 =< Χ > Δ Ε > Δ <Β > 9 Φ < ; = 3Χ Χ ΓΒ 1 < ; = Κ 4 Η Λ + % # &!% () # % & #! Π? Μ Ν Ο, 0+ 1, + 2 & # 1,
! # % # & ( & # &) # + % ( &,. + 0 1 23! 4 4 4 5 5 6 4 7 8 9 : ; :
More information: 459,. (2011),, Zhu (2008). Y = Xθ + ε, (1.1) Y = (y 1,..., y n ) T, ε = (ε 1,..., ε n ) T, θ = (θ 1,..., θ p ) T, X n p, X i X i, E(ε) = 0, Var (ε)
2013 10 Chinese Journal of Applied Probability and Statistics Vol.29 No.5 Oct. 2013 (,, 213001) (,, 211189). ;, ;. :,,,. : O212.2. 1.,,,,,, Belsley (1980), Christensen (1992), Critchley (2001). Zhu Lee
More information1. PDE u(x, y, ) PDE F (x, y,, u, u x, u y,, u xx, u xy, ) = 0 (1) F x, y,,uu (solution) u (1) u(x, y, )(1)x, y, Ω (1) x, y, u (1) u Ω x, y, Ωx, y, (P
2008.9-2008.12 Laplace Li-Yau s Harnack inequality Cauchy Cauchy-Kowalevski H. Lewy Open problems F. John, Partial Differential Equations, Springer-Verlag, 1982. 2002 2008 1 1. PDE u(x, y, ) PDE F (x,
More information!! # % & ( )!!! # + %!!! &!!, # ( + #. ) % )/ # & /.
! # !! # % & ( )!!! # + %!!! &!!, # ( + #. ) % )/ # & /. #! % & & ( ) # (!! /! / + ) & %,/ #! )!! / & # 0 %#,,. /! &! /!! ) 0+(,, # & % ) 1 # & /. / & %! # # #! & & # # #. ).! & #. #,!! 2 34 56 7 86 9
More information!!! #! )! ( %!! #!%! % + % & & ( )) % & & #! & )! ( %! ),,, )
! # % & # % ( ) & + + !!! #! )! ( %!! #!%! % + % & & ( )) % & & #! & )! ( %! ),,, ) 6 # / 0 1 + ) ( + 3 0 ( 1 1( ) ) ( 0 ) 4 ( ) 1 1 0 ( ( ) 1 / ) ( 1 ( 0 ) ) + ( ( 0 ) 0 0 ( / / ) ( ( ) ( 5 ( 0 + 0 +
More information# # 4 + % ( ) ( /! 3 (0 0 (012 0 # (,!./ %
#! # # %! # + 5 + # 4 + % ( ) ( /! 3 (0 0 (012 0 # (,!./ % ,9 989 + 8 9 % % % % # +6 # % 7, # (% ) ,,? % (, 8> % %9 % > %9 8 % = ΑΒ8 8 ) + 8 8 >. 4. ) % 8 # % =)= )
More information% & :?8 & : 3 ; Λ 3 3 # % & ( ) + ) # ( ), ( ) ). ) / & /:. + ( ;< / 0 ( + / = > = =? 2 & /:. + ( ; < % >=? ) 2 5 > =? 2 Α 1 Β 1 + Α
# % & ( ) # +,. / 0 1 2 /0 1 0 3 4 # 5 7 8 / 9 # & : 9 ; & < 9 = = ;.5 : < 9 98 & : 9 %& : < 9 2. = & : > 7; 9 & # 3 2
More informationSVM OA 1 SVM MLP Tab 1 1 Drug feature data quantization table
38 2 2010 4 Journal of Fuzhou University Natural Science Vol 38 No 2 Apr 2010 1000-2243 2010 02-0213 - 06 MLP SVM 1 1 2 1 350108 2 350108 MIP SVM OA MLP - SVM TP391 72 A Research of dialectical classification
More informationuntitled
2007 Scientific and Technical Documents Publishing House 90 167 183 9 6000 2001 20052006 100100 100100 55 520105 5 (Essential Science IndicatorsESI) 14 (1)5 (2)5 (3)5 = 5 5 (4) = 5 5 (5)50% 50 49.80
More information微 分 方 程 是 经 典 数 学 的 一 个 重 要 分 支, 常 用 来 描 述 随 时 间 变 化 的 动 态 系 统, 被 广 泛 应 用 于 物 理 学 工 程 数 学 和 经 济 学 等 领 域. 实 际 上, 系 统 在 随 时 间 的 变 化 过 程 中, 经 常 会 受 到 一 些
不 确 定 微 分 方 程 研 究 综 述 李 圣 国, 彭 锦 华 中 师 范 大 学 数 统 学 院, 湖 北 4379 黄 冈 师 范 学 院 不 确 定 系 统 研 究 所, 湖 北 438 pengjin1@tsinghua.org.cn 摘 要 : 不 确 定 微 分 方 程 是 关 于 不 确 定 过 程 的 一 类 微 分 方 程, 其 解 也 是 不 确 定 过 程. 本 文 主
More information?.! #! % 66! & () 6 98: +,. / / 0 & & < > = +5 <. ( < Α. 1
!! # % # & ( & ) # +, #,., # / 0 1. 0 1 3 4 5! 6 7 6 7 67 +18 9 : : : : : : : : : :! : : < : : ?.! #! % 66! & 6 1 1 3 4.5 () 6 98: +,. / / 0 & 0 0 + & 178 5 3 0. = +5
More information% & ( ) +, (
#! % & ( ) +, ( ) (! ( &!! ( % # 8 6 7 6 5 01234% 0 / /. # ! 6 5 6 ;:< : # 9 0 0 = / / 6 >2 % % 6 ; # ( ##+, + # 5 5%? 0 0 = 0 0 Α 0 Β 65 6 66! % 5 50% 5 5 ΗΙ 5 6 Φ Γ Ε) 5 % Χ Δ 5 55 5% ϑ 0 0 0 Κ,,Λ 5!Α
More informationPowerPoint Presentation
ABC ABC or or What is an Actuary? An actuary is a business professional who analyzes the financial consequences of risk. Actuaries use mathematics, statistics, and financial theory to study uncertain future
More information國立屏東教育大學碩士班研究生共同修業要點
目 錄 壹 國 立 屏 東 大 學 碩 士 班 研 究 生 共 同 修 業 辦 法...1 貳 國 立 屏 東 大 學 應 用 數 學 系 碩 士 班 研 究 生 修 業 要 點...5 參 應 用 數 學 系 碩 士 班 課 程 結 構...9 肆 應 用 數 學 系 專 任 師 資 簡 介...15 伍 應 用 數 學 系 碩 士 班 歷 屆 研 究 生 論 文 資 料...17 附 錄 一 國
More information3978 30866 4 3 43 [] 3 30 4. [] . . 98 .3 ( ) 06 99 85 84 94 06 3 0 3 9 3 0 4 9 4 88 4 05 5 09 5 8 5 96 6 9 6 97 6 05 7 7 03 7 07 8 07 8 06 8 8 9 9 95 9 0 05 0 06 30 0 .5 80 90 3 90 00 7 00 0 3
More information! Β Β? Β ( >?? >? %? Γ Β? %? % % %? Χ Η Ιϑ Κ 5 8 Λ 9. Μ Ν Ο Χ? Π Β # % Χ Χ Θ Ρ% Ρ% Θ!??? % < & Θ
! # % & ( ) +,. / 0 1 + 2. 3 4. 56. / 7 89 8.,6 2 ; # ( ( ; ( ( ( # ? >? % > 64 5 5Α5. Α 8/ 56 5 9. > Β 8. / Χ 8 9 9 5 Δ Ε 5, 9 8 2 3 8 //5 5! Α 8/ 56/ 9. Φ ( < % < ( > < ( %! # ! Β Β? Β ( >?? >?
More informationT 1) 2) ( ) T. T 4 T. R T. T U A doi / THE ANALYSIS ON STATIC CHARACTERISTICS OF CURVED T-BEAMS IN CONS
37 1 215 2 T 1 2 22451 T. T 4 T. T. T U448.22 doi 1.652/1-879-14-31 THE NLYSIS ON STTIC CHCTEISTICS OF CUVED T-BEMS IN CONSIDETION OF SELF-EQUILIBIUM 1 GN Yanan 2 SHI Feiting School of Civil Engineering
More information92
* ** ** 9 92 % 80.0 70.0 60.0 50.0 40.0 30.0 20.0 0.0 0.0 % 60.0 50.0 40.0 30.0 20.0 0.0 0.0 990 2000 200 2002 2003 2004 2005 2006 2007 2008 2009 200 978 979 980 98 982 983 984 985 986 987 988 989 990
More informationWelch & Bishop, [Kalman60] [Maybeck79] [Sorenson70] [Gelb74, Grewal93, Maybeck79, Lewis86, Brown92, Jacobs93] x R n x k = Ax k 1 + Bu k 1 + w
Greg Welch 1 and Gary Bishop 2 TR 95-041 Department of Computer Science University of North Carolina at Chapel Hill 3 Chapel Hill, NC 27599-3175 : 2006 7 24 2007 1 8 1960 1 welch@cs.unc.edu, http://www.cs.unc.edu/
More information中国主权资产负债表风险分析
中 国 主 权 资 产 负 债 表 及 其 风 险 评 估 ( 下 ) 1 李 扬 张 晓 晶 常 欣 汤 铎 铎 李 成 摘 要 2000~2010 年, 中 国 的 国 民 资 产 负 债 表 呈 快 速 扩 张 之 势 对 外 资 产 基 础 设 施 以 及 房 地 产 资 产 迅 速 积 累, 构 成 资 产 扩 张 的 主 导 因 素 这 记 载 了 出 口 导 向 发 展 战 略 之 下
More information《分析化学辞典》_数据处理条目_1.DOC
3 4 5 6 7 χ χ m.303 B = f log f log C = m f = = m = f m C = + 3( m ) f = f f = m = f f = n n m B χ α χ α,( m ) H µ σ H 0 µ = µ H σ = 0 σ H µ µ H σ σ α H0 H α 0 H0 H0 H H 0 H 0 8 = σ σ σ = ( n ) σ n σ /
More informationMicrosoft PowerPoint - FE11
- - 郑振龙陈蓉厦门大学金融系课程网站 http://efinance.org.cn Email: zlzheng@xmu.edu.cn aronge@xmu.edu.cn BSM BSM BSM Copyright 01 Zheng, Zhenlong & Chen, Rong, XMU BSM BSM BSM Copyright 01 Zheng, Zhenlong & Chen, Rong,
More information202,., IEC1123 (1991), GB8051 (2002) [4, 5],., IEC1123,, : 1) IEC1123 N t ( ). P 0 = 0.9995, P 1 = 0.9993, (α, β) = (0.05, 0.05), N t = 72574 [4]. [6
2013 4 Chinese Journal of Applied Probability and Statistics Vol.29 No.2 Apr. 2013 (,, 550004) IEC1123,,,., IEC1123 (SMT),,,. :,,, IEC1123,. : O212.3. 1. P.,,,, [1 5]. P, : H 0 : P = P 0 vs H 1 : P = P
More information工程硕士网络辅导第一讲
< > < R R [ si t R si cos si cos si cos - sisi < si < si < < δ N δ { < δ δ > } www.tsighututor.com 6796 δ < < δ δ N δ { < < δ δ > b { < < b R} b] { b R} [ { > R} { R} } [ b { < b R} ] { b R} { R} X X Y
More informationUnited Nations ~ ~ % 2010
42 3 2018 5 Vol. 42 No. 3 May 2018 38 Population Research 2014 60 3% ~ 4% 10% 60 +
More information9 : : ; 7 % 8
! 0 4 1 % # % & ( ) # + #, ( ) + ) ( ). / 2 3 %! 5 6 7! 8 6 7 5 9 9 : 6 7 8 : 17 8 7 8 ; 7 % 8 % 8 ; % % 8 7 > : < % % 7! = = = : = 8 > > ; 7 Ε Β Β % 17 7 :! # # %& & ( ) + %&, %& ) # 8. / 0. 1 2 3 4 5
More information政治制度史研究的省思:以六朝隋唐為例
- 1 - 1 2-2 - 3 4 5 6 7 8 9-3 - 10 11 12 13 14 15 16 17 18 19-4 - 20 21 22 23 24 25 26 27-5 - 28 29 30 Ο 31-6 - 32 33 34 35 36 37 38-7 - 39 40 41 42 43-8 - 44 45 46 47 48 49 50 51 52-9 - 53 54 55-10 -
More information: Π Δ 9 Δ 9 Δ 9 7 Θ Μ 9 8 Ρ Σ # = Μ 0 ; 9 < = 5 Λ 6 # = = # Μ Μ 7 Τ Μ = < Μ Μ Ο = Ρ # Ο Ο Ο! Ο 5 6 ;9 5 5Μ Ο 6
! # % # & ( ) +, #,. # / 0. 0 2 3 4! 5 6 5 6 7 8 5 6 5 6 8 9 : # ; 9 < = 8 = > 5 0? 0 Α 6 Β 7 5ΧΔ ΕΦ 9Γ 6 Η 5+3? 3Ι 3 ϑ 3 6 ΗΚ Η Λ!Κ Η7 Μ ΒΜ 7 Ν!! Ο 8 8 5 9 6 : Π 5 6 8 9 9 5 6 Δ 9 Δ 9 Δ 9 7 Θ Μ 9 8 Ρ
More information, & % # & # # & % & + # & # # # & # % #,
! # #! % # & # & & ( ( # ) % , & % # & # # & % & + # & # # # & # % #, # % % # % # ) % # % % # % # # % # % # + # % ( ( # % & & & & & & % & & # % # % & & % % % . % # / & & # 0 ) & # % & % ( # # & & & # #
More information% % %/ + ) &,. ) ) (!
! ( ) + & # % % % %/ + ) &,. ) ) (! 1 2 0 3. 34 0 # & 5 # #% & 6 7 ( ) .)( #. 8!, ) + + < ; & ; & # : 0 9.. 0?. = > /! )( + < 4 +Χ Α # Β 0 Α ) Δ. % ΕΦ 5 1 +. # Ι Κ +,0. Α ϑ. + Ι4 Β Η 5 Γ 1 7 Μ,! 0 1 0
More information1 <9= <?/:Χ 9 /% Α 9 Δ Ε Α : 9 Δ 1 8: ; Δ : ; Α Δ : Β Α Α Α 9 : Β Α Δ Α Δ : / Ε /? Δ 1 Δ ; Δ Α Δ : /6Φ 6 Δ
! #! %&! ( )! +,!. / 1,. + 2 ( 3 4 5 6 7 8 9: : 9: : : ; ; ? =
More information