Normalized kernel-weighted random measures

Transcription

1 Normalized kernel-weighted random measures Jim Griffin University of Kent 1 August 27

2 Outline 1 Introduction 2 Ornstein-Uhlenbeck DP 3 Generalisations

3 Bayesian Density Regression We observe data (x 1, y 1 ),..., (x n, y n ) and we assume that y i F xi. We want to estimate F x for x X.

4 Bayesian Density Regression We observe data (x 1, y 1 ),..., (x n, y n ) and we assume that y i F xi. We want to estimate F x for x X. We could build the hierarchical model y i υ(ψ i, φ) and θ 1, θ 2, θ 3, i.i.d. H. G xi ψ i G xi d = p i (x i )δ θi i=1

5 Bayesian Density Regression We observe data (x 1, y 1 ),..., (x n, y n ) and we assume that y i F xi. We want to estimate F x for x X. We could build the hierarchical model y i υ(ψ i, φ) and θ 1, θ 2, θ 3, i.i.d. H. G xi ψ i G xi d = p i (x i )δ θi i=1 Then F x can be estimated by E G,φ y [ k(y i ψ, φ) dg x (ψ)].

6 Bayesian Density Regression We would like G x to be stationary.

7 Bayesian Density Regression We would like G x to be stationary.... and to have a way of controlling the dependence between G x and G y.

8 Possible approaches Usually we generalize standard construction of priors for exchangeable sequences:

9 Possible approaches Usually we generalize standard construction of priors for exchangeable sequences: Dirichlet process - DDP (MacEachern 1999)

10 Possible approaches Usually we generalize standard construction of priors for exchangeable sequences: Dirichlet process - DDP (MacEachern 1999) Stick-breaking - πddp (Griffin and Steel, 26), Kernel-weighted stick-breaking (Dunson and Park, 26)

11 Possible approaches Usually we generalize standard construction of priors for exchangeable sequences: Dirichlet process - DDP (MacEachern 1999) Stick-breaking - πddp (Griffin and Steel, 26), Kernel-weighted stick-breaking (Dunson and Park, 26) Pólya urn scheme (Caron et al, 27)

12 Normalized random measures We could extend the class of normalized random measures (Regazzini et al 23, James et al 25)).

13 Normalized random measures We could extend the class of normalized random measures (Regazzini et al 23, James et al 25)). Let (J, θ) follow an homogeneous Poisson process on R + Θ with intensity κ(j)h(θ) and define i=1 G = J iδ θi i=1 J i

14 Normalized random measures We could extend the class of normalized random measures (Regazzini et al 23, James et al 25)). Let (J, θ) follow an homogeneous Poisson process on R + Θ with intensity κ(j)h(θ) and define i=1 G = J iδ θi i=1 J i then G follows a (homogeneous) NRM under suitable conditions for κ we have a random probability measure (infinite activity) and h is the density of the centring distribution.

15 Examples of NRMs Dirichlet process - Normalized gamma process κ(j) = M exp{ J} J Normalized Generalized Gamma process κ(j) = γ Γ(1 γ) J γ exp{ rj

16 Normalized kernel-weighted measures Let (τ, J, θ) follow an homogeneous Poisson process on X R + Θ with intensity κ(j)h(θ) and define i=1 G x = k(x, τ i)j i δ θi i=1 k(x, τ i)j i for some kernel function k(x, τ i ) centred at τ i

17 Normalized kernel-weighted measures Let (τ, J, θ) follow an homogeneous Poisson process on X R + Θ with intensity κ(j)h(θ) and define i=1 G x = k(x, τ i)j i δ θi i=1 k(x, τ i)j i for some kernel function k(x, τ i ) centred at τ i For modelling, we wish to control Dependence between G x and G y. In these process, for a measureable set B, we can measure correlation through Corr(G x (B), G y (B)) which usually won t depend on B. The marginal prior of G x for all x.

18 Normalized kernel-weighted measures Dependence The correlation of the unnormalized random measures is k(x, τ)k(y, τ) dτ k(x, τ) 2 dτ This correlation will typically carry over to the normalized version unless we have a marginal processs that gives distributions with a few large jumps.

19 Normalized kernel-weighted measures Dependence The correlation of the unnormalized random measures is k(x, τ)k(y, τ) dτ k(x, τ) 2 dτ This correlation will typically carry over to the normalized version unless we have a marginal processs that gives distributions with a few large jumps. Stationarity The form of κ can be derived to give particular marginal processes.

20 Ornstein-Uhlenbeck Dirichlet Process With a 1D regressor, typically time, we fix the kernel function to be k(x, τ) = exp{ λ(x τ)}i(x > τ). and assume a marginal Dirichlet process.

21 Ornstein-Uhlenbeck Dirichlet Process With a 1D regressor, typically time, we fix the kernel function to be k(x, τ) = exp{ λ(x τ)}i(x > τ). and assume a marginal Dirichlet process. The unnormalized process must be a Gamma process.

22 Ornstein-Uhlenbeck Dirichlet Process With a 1D regressor, typically time, we fix the kernel function to be k(x, τ) = exp{ λ(x τ)}i(x > τ). and assume a marginal Dirichlet process. The unnormalized process must be a Gamma process. The ideas of Barndorff-Nielsen and Shephard are useful to define this process. Let φ 1, φ 2, φ 3,... are i.i.d. exponential (1) and τ 1, τ 2, τ 3,... follow a Poisson process with intensity Mλ then γ t = I(τ i < t) exp{ λ i τ i }φ i i=1 is Ga(M, 1) distributed for all t.

23 Definition of OUDP This is a construction when the covariate x is time. Define i=1 G x = I(τ i < x) exp{ λ(x τ i )}J i δ θi i=1 I(τ i < x) exp{ λ(x τ i )}J i or τ follows a Poisson process with intensity λm. J 1, J 2, J 3, i.i.d. Ex(1) θ 1, θ 2, θ 3, i.i.d. H (τ, J, θ) follows a Poisson process with intensity.5 λm exp{ J}h x

24 The autocorrelation at lag k is approximately exp{ λk} [1 + 1M ] (1 exp{ λk} λ =.25 λ = 1 = 1 = 4

25 Dynamics of moments The dynamics of the mean are µ t = w t µ t 1 + (1 w t )µ G λ =.125 λ =.5 λ = 2 M = 1 M = 16

26 Computation The stationarity of the process makes inference possible using fairly standard methods exp{ λt}γ G t = exp{ λt}γ + m i=1 exp{ λ(t τ G i)}j i m i=1 + exp{ λ(t τ i)}j i exp{ λt}γ + m i=1 exp{ λ(t τ i)}j i where G follows a Dirichlet process and γ follows a gamma distribution with shape parameter M. Inference using: Gibbs sampling Particle filtering

27 Example - Brazilian stock index return We observe r 1, r 2,..., r T which are daily log returns and let r t σ 2 t N(, σ 2 t ) σ 2 t F t where {F t } T follows an OUDP, centred on an inverse Gaussian distribution, whose parameters are estimated from the marginal distribution of the data.

28 Example Brazilian stock index.25 Data.25 Smoothed Predictive return

29 Generalizing to other marginal processes For other marginal processes, let w(a) be the Lévy density of the unnormalized marginal process.

30 Generalizing to other marginal processes For other marginal processes, let w(a) be the Lévy density of the unnormalized marginal process. The intensity of the Poisson process of the unnormalized process with the kernel will be w (J)h(θ) where w (J) = λjw(j).

31 Generalizing to other marginal processes For other marginal processes, let w(a) be the Lévy density of the unnormalized marginal process. The intensity of the Poisson process of the unnormalized process with the kernel will be w (J)h(θ) where w (J) = λjw(j). A marginal NGG process arising from assuming the intensity κ(j) = γλ Γ(1 γ) J1 γ exp{ rj which is a finite activity Poisson process.

32 In general, if we define a kernel K (x, τ) then the two measures are linked by the integral equation a w(j) dj = where ν is Lesbesgue measure. a w (J)ν({τ K (, τ) > a/j}) dj This is a Volterra integral equation and can be solved using standard methods (in principle).

33 Generalizing to other kernels In 2D, if the kernel k(x, τ) = exp{ λ x t 2 } and we want a marginal Dirichlet process then the intensity function is λ π exp{ J}h(θ) (which is proportional to the intensity function for the OUDP) M = 1 M =

34 Discussion Normalized Kernel-Weighted Random Measures offer a way to model dependent nonparametric processes: Flexible kernels and marginal processes allow a large range of models to be defined. Computation is helped by representations through finite activity Poisson processes for some elements. and include continuous process on the space of measures.