Max stable Processes & Random Fields: Representations, Models, and Prediction

Transcription

1 Max stable Processes & Random Fields: Representations, Models, and Prediction Stilian Stoev University of Michigan, Ann Arbor March 2, 2011 Based on joint works with Yizao Wang and Murad S. Taqqu.

2 1 Preliminaries 2 Representations 3 Further Examples 4 Prediction for Max stable Random Fields Future work 6 References 7 Appendix

3 Preliminaries

4 Max stable processes. Definition and Motivation. A stochastic process (field) X = {X t } t T is max stable if for all n N, { fdd {X t } t T = a n max X (i) t + b n 1 i n }t T, for some a n > 0, b n R, where X (i), i = 1, 2, are independent copies of X.

5 Max stable processes. Definition and Motivation. A stochastic process (field) X = {X t } t T is max stable if for all n N, { fdd {X t } t T = a n max X (i) t + b n 1 i n }t T, for some a n > 0, b n R, where X (i), i = 1, 2, are independent copies of X. Fact: If for i.i.d. processes ξ (i) = {ξ (i) t } t T, i = 1, 2,, we have n a n i=1 ξ t (i) + b n fdd X t, as n, for some a n > 0, b n R, then the limit X = {X t } t T is max stable.

6 Extreme Value Distributions The following is the counterpart to the CLT for maxima: Theorem (Fisher Tippett (1928) & Gnedenko (1943)) Suppose that X n, n = 1, 2,... are iid random variables and a n max X i + b n a n 1 i n 1 i n X i + b n d Z, as n, for some a n > 0, b n R. Then, G(x) = P{Z x} is one of three types of extreme value distributions (EVD): e x α, x > 0(α > 0) (Fréchet) G(x) = e e x, x R (Gumbel) e ( x)α, x < 0(α > 0) (negative Fréchet) The type of the limit is determined by the tails of the X i s. Max stable processes have extreme value marginals.

7 α Fréchet max stable processes Z is said to be α Fréchet, if P(Z x) = exp{ σ α /x α }, x > 0, with some scale parameter σ > 0.

8 α Fréchet max stable processes Z is said to be α Fréchet, if P(Z x) = exp{ σ α /x α }, x > 0, with some scale parameter σ > 0. Heavy tails: P(Z > x) = 1 e σα /x α σ α /x α, as x.

9 α Fréchet max stable processes Z is said to be α Fréchet, if P(Z x) = exp{ σ α /x α }, x > 0, with some scale parameter σ > 0. Heavy tails: P(Z > x) = 1 e σα /x α σ α /x α, as x. The process X = {X t } t T is called α Fréchet, if the max linear combination n a i X ti is α Fréchet i=1 for all a i > 0, t i T, 1 i n. Note: Recall the definition of Gaussian processes.

10 α Fréchet max stable processes Z is said to be α Fréchet, if P(Z x) = exp{ σ α /x α }, x > 0, with some scale parameter σ > 0. Heavy tails: P(Z > x) = 1 e σα /x α σ α /x α, as x. The process X = {X t } t T is called α Fréchet, if the max linear combination n a i X ti is α Fréchet i=1 for all a i > 0, t i T, 1 i n. Note: Recall the definition of Gaussian processes. Fact: A process X with α Fréchet marginals is max stable if and only if it is an α Fréchet process. No generality is lost if one focuses on the class of α Fréchet process max stable processes.

11 Asymptotics in a Picture: The Chocolate Hills Where?

12 Asymptotics in a Picture: The Chocolate Hills Where? Bohol Island, Philippines.

13 Representations

14 The de Haan s spectral represenation Theorem (de Haan (1984)) Let X = {X t } t R be a stochastically continuous α Fréchet max stable process. Then, there exist functions f t (u) L α +([0, 1], du), such that f.d.d. {X t } t R = { f t (U i ) } i 1 Γ 1/α i where 0 Γ 1 Γ 2 is a standard Poisson point process on (0, ) and the U i s are independent Uniform(0, 1) random variables, independent of the Γ i s. t R,

15 Extremal integral representations Theorem (S. & Taqqu (2006); S. (2008)) Let X = {X t } t T be an α Fréchet max stable process (separable in probability). Then, {X t } t X fdd = { e S } f t (u)m α (du), t T for some deterministic f t (u) L α +(S, µ). Here M α is an α Fréchet random sup measure.

16 Extremal integral representations Theorem (S. & Taqqu (2006); S. (2008)) Let X = {X t } t T be an α Fréchet max stable process (separable in probability). Then, {X t } t X fdd = { e S } f t (u)m α (du), t T for some deterministic f t (u) L α +(S, µ). Here M α is an α Fréchet random sup measure. The sup measure: Given a measure space (S, S, µ), M α is said to be an α Fréchet sup measure with control measure µ, if: independently scattered: M α (A i ) s are independent, for disjoint A i s. α Fréchet: P(M α (A) x) = exp{ µ(a)x α }, x > 0. σ sup-additive: For disjoint measurable A i s: M α ( i=1a i ) = sup M α (A i ), i N almost surely.

17 Extremal Integrals For a simple and non negative function f (u) := n i=1 a i1 Ai (u), (A i S), with disjoint A i s, we set e S f (u)m α (du) def = n a i M α (A i ), i=1

18 Extremal Integrals For a simple and non negative function f (u) := n i=1 a i1 Ai (u), (A i S), with disjoint A i s, we set Properties: S e S f (u)m α (du) def = i=1 n a i M α (A i ), i=1 α Fréchet: For all x > 0, we have ( e ) { n P fdm α x = exp ai α µ(a i )x α} { = exp f α L α (µ) /x α}.

19 Extremal Integrals For a simple and non negative function f (u) := n i=1 a i1 Ai (u), (A i S), with disjoint A i s, we set Properties: S e S f (u)m α (du) def = i=1 n a i M α (A i ), i=1 α Fréchet: For all x > 0, we have ( e ) { n P fdm α x = exp ai α µ(a i )x α} { = exp f α L α (µ) /x α}. max linearity: For all a, b > 0 and f, g L α +(µ), e S (af bg)dm α = a e S fdm α b e S gdm α.

20 Extremal Integrals For a simple and non negative function f (u) := n i=1 a i1 Ai (u), (A i S), with disjoint A i s, we set Properties: S e S f (u)m α (du) def = i=1 n a i M α (A i ), i=1 α Fréchet: For all x > 0, we have ( e ) { n P fdm α x = exp ai α µ(a i )x α} { = exp f α L α (µ) /x α}. max linearity: For all a, b > 0 and f, g L α +(µ), e S (af bg)dm α = a e S fdm α b e S gdm α. e (convergence) S f P ndm α ξ if and only if S f n α f α 0, for some f L α e +(µ).thus, extends by continuity in P to all f L α e +(µ) and ξ := S fdm α.

21 Back to max stable processes For any collection of non negative f t L α +(µ), the process X t := is α Fréchet (max stable). e S f t dm α, (t T ) { ( f ti (u) ) αµ(du) } P(X ti x i, 1 i n) = exp max, (x i > 0). S 1 i n x i Follows: from max linearity of the extremal integral.

22 Back to max stable processes For any collection of non negative f t L α +(µ), the process X t := is α Fréchet (max stable). e S f t dm α, (t T ) { ( f ti (u) ) αµ(du) } P(X ti x i, 1 i n) = exp max, (x i > 0). S 1 i n x i Examples: stationary α Fréchet processes/fields. (moving maxima) With (S, S, µ) (R, B, Leb), X t := e f (t x)m α (dx), R t R (mixed moving maxima) (S, S, µ) (R d V, B R d V, Leb ν), X t := e R V f ( t x, v)m α (d x, dv), t R d. Known as M3 process in Zhang & Smith (2004)

23 A Basic Example: The Extremal Process Take (S, S, µ) (R, B, Leb) and let X t = e dm α (dx) M α ([0, t)), t > 0. [0,t)

24 A Basic Example: The Extremal Process Take (S, S, µ) (R, B, Leb) and let X t = e dm α (dx) M α ([0, t)), t > 0. [0,t) For all t > 0, we have X t = M α ([0, t)) d = t 1/α Z, where Z is standard α Fréchet: P(Z x) = e x α, x > 0.

25 A Basic Example: The Extremal Process Take (S, S, µ) (R, B, Leb) and let X t = e dm α (dx) M α ([0, t)), t > 0. [0,t) For all t > 0, we have X t = M α ([0, t)) d = t 1/α Z, where Z is standard α Fréchet: P(Z x) = e x α, x > 0. The process {X t } t 0 has independent max increments: (X t1,x t2,,x tn ) d = (t 1/α 1 Z 1,t 1/α 1 Z 1 (t 2 t 1 ) 1/α Z 2,,t 1/α 1 Z 1 (t n t n 1 ) 1/α Z n ), where the Z i s are iid standard α Fréchet. Can be viewed as an analog of the Brownian motion.

26 α Fréchet Extremal Processes: Sample Paths 16 Five paths of the α Frechet Extremal Process: α = X(t) t

27 Further Examples

28 Smith Random Fields Let Σ be a positive definite d d symmetric matrix and set φ Σ ( x) := 1 { (2π) d/2 Σ 1/2 exp for the Normal density with covariance Σ. Define the random field X t := i 1 x T Σ 1 x }, x R d 2 φ Σ (t U i ) Γ 1/α, t R d, i where {(Γ i, U i )} is a Poisson point process on (0, ) R d with the Lebesgue intensity. Then {X t } t R d is a moving average α Fréchet random field: {X t } t R d f.d.d. = { e R d φ Σ (t x)m α (dx) } t R d.

29 Smith Random Fields: Sample Surface 2D Smith random field α = 3, ρ = Y X

30 Brown Resnick Processes Let w t (ω ) be a standard Brownian motion supported on (Ω, F, P ). Consider a random α Fréchet sup measure M α (dω ) supported on (Ω, F, P). Define the α Fréchet process: X t := e Ω e wt(ω ) α t /2 M α (dω ), t R.

31 Brown Resnick Processes Let w t (ω ) be a standard Brownian motion supported on (Ω, F, P ). Consider a random α Fréchet sup measure M α (dω ) supported on (Ω, F, P). Define the α Fréchet process: X t := e Ω e wt(ω ) α t /2 M α (dω ), t R. Surprisingly, the process {X t } t R is stationary. It is called a Brown Resnick stationary α Fréchet process.

32 Brown Resnick Processes Let w t (ω ) be a standard Brownian motion supported on (Ω, F, P ). Consider a random α Fréchet sup measure M α (dω ) supported on (Ω, F, P). Define the α Fréchet process: X t := e Ω e wt(ω ) α t /2 M α (dω ), t R. Surprisingly, the process {X t } t R is stationary. It is called a Brown Resnick stationary α Fréchet process. More generally, if {w t } t R d is a zero-mean Gaussian random field with stationary increments, then X t := e wt(ω ) ασ2 e t /2 M α (dω ), t R d, Ω is a stationary α Fréchet random field. For more details, see Kabluchko, Schlather & de Haan (2009).

33 Prediction

34 Prediction for Max stable Random Fields Given is a max stable random field: X t := Problem: We observe X t1,, X tn. e S f t (u)m α (du), t T. (1)

35 Prediction for Max stable Random Fields Given is a max stable random field: X t := Problem: We observe X t1,, X tn. Goal: Predict X s1,, X sm. e S f t (u)m α (du), t T. (1)

36 Prediction for Max stable Random Fields Given is a max stable random field: X t := e S f t (u)m α (du), t T. (1) Problem: We observe X t1,, X tn. Goal: Predict X s1,, X sm. Need: To compute functionals of the conditional distribution (X s1,, X sm ) (X t1,, X tn ).

37 Prediction for Max stable Random Fields Given is a max stable random field: X t := Problem: We observe X t1,, X tn. Goal: Predict X s1,, X sm. e S f t (u)m α (du), t T. (1) Need: To compute functionals of the conditional distribution Challenges: (X s1,, X sm ) (X t1,, X tn ). The c.d.f. s are theoretically available from (1), but the densities are not.

38 Prediction for Max stable Random Fields Given is a max stable random field: X t := Problem: We observe X t1,, X tn. Goal: Predict X s1,, X sm. e S f t (u)m α (du), t T. (1) Need: To compute functionals of the conditional distribution Challenges: (X s1,, X sm ) (X t1,, X tn ). The c.d.f. s are theoretically available from (1), but the densities are not. The current state-of-the-art: only bi variate densities are known in closed form, for just a couple of models.

39 The Max Linear Model Consider the model X = A Z,

40 The Max Linear Model Consider the model X = A Z, that is p X i = a ij Z j, (1 i n) j=1 where A = (a ij ) n p, X = (Xi ) n i=1, Z = (Zj ) p j=1.

41 The Max Linear Model Consider the model X = A Z, that is p X i = a ij Z j, (1 i n) j=1 where A = (a ij ) n p, X = (Xi ) n i=1, Z = (Zj ) p j=1. If the Z j s are i.i.d. α Fréchet, then X is max stable.

42 The Max Linear Model Consider the model X = A Z, that is p X i = a ij Z j, (1 i n) j=1 where A = (a ij ) n p, X = (Xi ) n i=1, Z = (Zj ) p j=1. If the Z j s are i.i.d. α Fréchet, then X is max stable. In fact, X i = e [0,1] p f i dm α, with f i (u) = p 1/α a ij 1 [(j 1)/p,j/p) (u) j=1

43 The Max Linear Model Consider the model X = A Z, that is p X i = a ij Z j, (1 i n) j=1 where A = (a ij ) n p, X = (Xi ) n i=1, Z = (Zj ) p j=1. If the Z j s are i.i.d. α Fréchet, then X is max stable. In fact, X i = e [0,1] p f i dm α, with f i (u) = p 1/α a ij 1 [(j 1)/p,j/p) (u) j=1 Max linear models with large p can approximate, arbitrary e max stable models: X t = [0,1] f tdm α.

44 Another Representation: Spectral Measure For all max stable (α Fréchet) random vectors X : P( X { ( x) = exp S n 1 + max 1 i n w ) i αγ(d } w), ( x 0) x i where S n 1 + = { w 0 : w = max 1 i n w i = 1}, and Γ is unique finite measure on S n 1 + the spectral measure of X.

45 Another Representation: Spectral Measure For all max stable (α Fréchet) random vectors X : P( X { ( x) = exp S n 1 + max 1 i n w ) i αγ(d } w), ( x 0) x i where S n 1 + = { w 0 : w = max 1 i n w i = 1}, and Γ is unique finite measure on S n 1 + the spectral measure of X. For the max-linear model, Γ is discrete: Γ(d w) = where a j = (a ij ) n i=1 Rn +. p a j α δ aj / a j (d w), j=1

46 Another Representation: Spectral Measure For all max stable (α Fréchet) random vectors X : P( X { ( x) = exp S n 1 + max 1 i n w ) i αγ(d } w), ( x 0) x i where S n 1 + = { w 0 : w = max 1 i n w i = 1}, and Γ is unique finite measure on S n 1 + the spectral measure of X. For the max-linear model, Γ is discrete: Γ(d w) = where a j = (a ij ) n i=1 Rn +. p a j α δ aj / a j (d w), j=1 Hence the max linear models are called spectrally discrete.

47 Examples Spectrally discrete random field: Let φ t (j) 0 be deterministic functions and p X t := φ j (t)z j t T. j=1 For any t 1,, t n, and s 1,, s m, we have X = A Z, with a ij := φ ti (j) and X = (X ti ) n i=1. and Y = B Z, with b ij := φ si (j) and Y = (X si ) m i=1. Moving maxima: Let φ(u) L α +(R d, du), then X t := e R d φ(t u)m α (du) P φ(t u j )Z j. j [ M,M] d

48 Prediction: a Computational Solution Problem: Consider the max linear model X = A Z, where A = (a ij ) n p, a ij > 0, and the Z j s are independent α Fréchet. Goal: sample from the conditional distribution of Z X. Plug in: Y = B Z and obtain samples from the conditional distribution Y X : (X s1,, X sm ) (X t1,, X tn ). }{{}}{{} = Y = X

49 Prediction: a Computational Solution Problem: Consider the max linear model X = A Z, where A = (a ij ) n p, a ij > 0, and the Z j s are independent α Fréchet. Goal: sample from the conditional distribution of Z X. Plug in: Y = B Z and obtain samples from the conditional distribution Y X : (X s1,, X sm ) (X t1,, X tn ). }{{}}{{} = Y Idea: If Y 1,, Y N are independent samples from Y X = x, then by the LLN, for P X almost all x: 1 N h( Y N ( i ) a.s. E h( Y ) σ( X ) ), as N. j=1 = X

50 Benefits & Caveats Benefits:

51 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense.

52 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals.

53 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals.

54 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals.

55 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals.

56 Benefits & Caveats Benefits: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals.

57 Benefits & Caveats Benefits: Caveats: Approximations to the optimal predictor for h( Z) given X in the mean square sense. Monte Carlo approximations to conditional medians, quantiles other optimal predictors and prediction intervals. Need to sample efficiently from the conditional distribution. Must be able to handle large p = dim(z)!

58 Basic Observations Suppose x = A Z. (2) Observations: For all 1 j n: x i 0 Z j ẑ j ( x) min ( where 1 1 i n a 0 = ) ij For some j s the upper bounds are attained, so that (2) holds! Hitting scenario: such a set J = J( x) {1,, p}, that yields (2). Z j = ẑ j ( x), j J and Z j < ẑ j ( x), j J,

59 The Simple Solution Theorem (Wang & S. (2010)) The regular conditional probability p Z X ( x) equals: p Z X (E x) = p J ( x)ν J (E x), J hitting scenario where p J ( x) = w J / K w K for some weights w j ν J (E x) = δẑj (π j (E)) P(Z j π j (E) Z j ẑ j ). j J }{{} active constraints j J } {{ } inequality constraints

60 The Simple Solution Theorem (Wang & S. (2010)) The regular conditional probability p Z X ( x) equals: p Z X (E x) = p J ( x)ν J (E x), J hitting scenario where p J ( x) = w J / K w K for some weights w j ν J (E x) = δẑj (π j (E)) P(Z j π j (E) Z j ẑ j ). j J j J The weights: w J = 0, if J > r( x) and if J = r( x): w J = j J ẑjf Zj (ẑ j ) j J F Z j (ẑ j ). Notes: Here r( x) is the minimal number of equality constraints: r( x) = min J hitting scenario for x J. The result applies to independent Z j s with p.d.f. s f Z s and c.d.f. s F Z s.

61 Example Consider the toy model X 1 X 2 = X Z 1 Z 2 Z 3.

62 Example Consider the toy model X 1 X 2 = X Z 1 Z 2 Z 3. We have X 1 = Z 1, X 2 = Z 1 Z 2, X 3 = Z 1 Z 2 Z 3.

63 Example Consider the toy model X 1 X 2 = X Z 1 Z 2 Z 3. We have X 1 = Z 1, X 2 = Z 1 Z 2, X 3 = Z 1 Z 2 Z 3. If x = (2 2 2) T, then the hitting scenarios are: J 1 = {1}, J 2 = {1, 2}, J 3 = {1, 3}, J 4 = {1, 2, 3}.

64 Example Consider the toy model X 1 X 2 = X Z 1 Z 2 Z 3. We have X 1 = Z 1, X 2 = Z 1 Z 2, X 3 = Z 1 Z 2 Z 3. If x = (2 2 2) T, then the hitting scenarios are: J 1 = {1}, J 2 = {1, 2}, J 3 = {1, 3}, J 4 = {1, 2, 3}. Thus, r = r( x) = 1, and the conditional probability p Z X (dz x) = δ 2 (dz 1 )P(Z 2 dz 2 Z 2 < 2)P(Z 3 dz 3 Z 3 < 2) has only one component.

65 The Efficient Solution. Implementation Big Problem: Finding all hitting scenarios with brute force is NP hard (as a function of n and p)! Harder than the set covering problem.

66 The Efficient Solution. Implementation Big Problem: Finding all hitting scenarios with brute force is NP hard (as a function of n and p)! Harder than the set covering problem. Big Solution: Yizao found an equivalent representation of the regular conditional probability. Theorem (Wang & S. (2010)) For P X almost all x, we have p Z X (E x) = r( x) k=1 ν(k) (E x), with ν (k) (E x) = j J (k) ( x) p (k) j ( x)ν (k) j (E x), p (k) j ( x) = where w (k) j ( x) = ẑ j f Zj (ẑ j ) l J (k) \{j} F Z l (ẑ l ), ν (k) j (E x) = δẑj (π j (E)) l J (k) \{j} ( x) i J (k) ( x) w (k) i ( x), w (k) j P(Z l π l (E) Z l ẑ l ).

67 Some ingredients in the proof Hitting matrix: Given x = A Z, construct { 0, if aij ẑ H = (h ij ) n p, where h ij = j < x i 1, if a ij ẑ j = x i Intuition: want to pick r = r( x) columns of H (i.e., j s) that cover all rows.

68 Some ingredients in the proof Hitting matrix: Given x = A Z, construct { 0, if aij ẑ H = (h ij ) n p, where h ij = j < x i 1, if a ij ẑ j = x i Intuition: want to pick r = r( x) columns of H (i.e., j s) that cover all rows. Key facts: With P X probability one, the structure of H = H( x) is nice! One can decompose {1,, p} into r = r( x) disjoint classes: J(k), k = 1,, r. One and only one j in each J (k) J (k), k = 1,, r is active. For more details, please see Wang & S. (2010).

69 An application: Max AR(q) Davis & Resnick (1989): Max AR(q) are the stationary solutions to: X t = φ 1 X t 1 φ m X t m Z t, t Z, (3) with φ i 0, 1 i q and i.i.d. 1 Fréchet Z t s.

70 An application: Max AR(q) Davis & Resnick (1989): Max AR(q) are the stationary solutions to: X t = φ 1 X t 1 φ m X t m Z t, t Z, (3) with φ i 0, 1 i q and i.i.d. 1 Fréchet Z t s. If φ = q i=1 φ i < 1, then (3) has a unique solution. X t = ψ j Z t j. j=0

71 An application: Max AR(q) Davis & Resnick (1989): Max AR(q) are the stationary solutions to: X t = φ 1 X t 1 φ m X t m Z t, t Z, (3) with φ i 0, 1 i q and i.i.d. 1 Fréchet Z t s. If φ = q i=1 φ i < 1, then (3) has a unique solution. X t = ψ j Z t j. j=0 ψ j s decay exponentially: by truncation, we get a max linear model X t P Xt = M ψ j Z t j. j=0

72 An application: Max AR(q) Davis & Resnick (1989): Max AR(q) are the stationary solutions to: X t = φ 1 X t 1 φ m X t m Z t, t Z, (3) with φ i 0, 1 i q and i.i.d. 1 Fréchet Z t s. If φ = q i=1 φ i < 1, then (3) has a unique solution. X t = ψ j Z t j. j=0 ψ j s decay exponentially: by truncation, we get a max linear model X t P Xt = M ψ j Z t j. Max AR(q): we get the projection predictors by recursively solving j=0 X t+h = φ 1 Xt+h 1 φ q Xt+h q.

73 Prediction for Max AR Using i.i.d. conditional samples, we get: the conditional median, 9% predition intervals. estimates of P{X t+h X t+h X s, s t}. X Prediction of MARMA(3,0) processes MARMA process Conditional 9% quantile Conditional median Projection predictor Lag (h) Coverage Width Proj Pred t

74 12 Moving Maxima Random Fields Smith moving maxima random field model where φ(t) = e X t = φ(t u)mα(du), R2 β 1 β 2 2π 1 ρ 2 exp { [β2 1 t2 1 2ρβ 1β 2 t 1 t 2 + β 2 2 t2 2 ] 2(1 ρ 2 ) Figure on the left: 4 conditional samples with β 1 = β 2 = 1, ρ = 0, given 7 observed values (all equal to ). }

75 The Conditional Median Conditional expected median of the Smith model Parameters:ρ=0, β 1=1, β 2=1

76 An application: Rainfall in Bourgogne, France The model: Discretization of the 2 component moving maxima (Smith models) where X t = Intuition: e φ β1 (t u)m α (1) (du) R 2 e φ β2 (t u)m α (2) (du), R 2 φ(u) φ β (u 1, u 2 ) = 1 2πβ 2 e (u2 1 +u2 2 )/2β2, β > 0. Need 2 components to represent large and small scale effects. For simplicity, use isotropic components (ρ = 0 and equal scales).

77 A Realization of the Two component Smith Model A Realization of a two component Smith model

78 Observations: X ti, for i = 1,, 146 stations maxima over 1 years of daily rainfall Lambert Longitude Lambert Latitude Conditional Median

79 Rainfall in Bourgogne, France Observations: X ti, for i = 1,, 146 stations maxima over 1 years of daily rainfall Lambert Longitude Conditional Median Model fitting via Cross Validation Do a grid search over (β 1, β 2 ): 1. Condition on largest stations. 2. Compute p values for the rest Check if uniform. If not: 4. Change (β 1, β 2 ), goto 1.

80 Future work General estimation methodology?? Hard but recent exciting work via partial likelihood (sandwich) methods available. Construction and estimation of max stable models area of interest. Spatio temporal models many environmental applications. Computer networks extremal dependence in delay/loads/congestions. Prediction for the spectrally continuous case?? Important but hard!

81 Thank you!

82 Some References Brown, B. M. & Resnick, S. I. (1977), Extreme values of independent stochastic processes, J. Appl. Probability 14(4), de Haan, L. (1984), A spectral representation for max stable processes, Annals of Probability, 12(4): Kabluchko & Schlather (2009) Ergodic properties of max infinitely divisible processes, Stoch. Process. Appl. Smith, R.L. (1990) Max-Stable Processes and Spatial Extremes. Unpubished manuscript. S. (2008) On the ergodicity and mixing of max-stable processes,stoch. Process. Appl. Wang (2010) maxlinear an R package. yizwang/software/maxlinear. Wang & S. (2010) On the structure and representations of max stable processes, Adv. Appl. Probab. 42(3): Wang & S. (2010) Conditional sampling for spectrally discrete max stable random fields. Adv. Appl. Probab. To appear. Zhang & Smith (2004) The behavior of multivariate maxima of moving maxima processes, Journal of Applied Probability, 41(4):

83 Appendix: Some Derivations

84 The idea behind de Haan s spectral representation { P{X tk x k, k = 1,, n} = P { =: P i 1 g(u i ) Γ 1/α i i 1 1 Γ 1/α i } 1 = P{N A = } = e µ(a), } max f t k (U i )/x k 1 1 k n where µ is the intensity measure of the Poisson point process N = {(U i, Γ i )} and A = {(u, x) : g(u) x 1/α } c {(u, x) : g(u) α > x}. We have (by Fubini) that µ(a) = 1 Thus, { P{X tk x k, k = 1,, n} = exp {g α (u)>x}dudx = 1 0 g α (u)du. ( ) 1 αdu } max 0 1 k n f tk (u)/x k.

85 Stationarity of the standard Brown Resnick process For simplicity α 1 and let 0 t 1 t n, τ > 0: P{X tk +τ x k, 1 k n} { } = exp max x 1 Ω 1 k n k ewt k +τ (ω ) (t k +τ)/2 P (dω ) { ( = exp E P max x 1 1 k n k e(wt k +τ wτ tk/2) e (wτ τ/2)) } { ( ) = exp E P max x 1 1 k n k e(wt k t k/2) } = P{X tk x k, 1 k n}. Since {w t } t 0 has stationary and independent increments and since E P e wt = e t /2.