Abstract
Two algorithms are proposed to simulate space-time Gaussian random fields with a covariance function belonging to an extended Gneiting class, the definition of which depends on a completely monotone function associated with the spatial structure and a conditionally negative definite function associated with the temporal structure. In both cases, the simulated random field is constructed as a weighted sum of cosine waves, with a Gaussian spatial frequency vector and a uniform phase. The difference lies in the way to handle the temporal component. The first algorithm relies on a spectral decomposition in order to simulate a temporal frequency conditional upon the spatial one, while in the second algorithm the temporal frequency is replaced by an intrinsic random field whose variogram is proportional to the conditionally negative definite function associated with the temporal structure. Both algorithms are scalable as their computational cost is proportional to the number of space-time locations that may be irregular in space and time. They are illustrated and validated through synthetic examples.
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Acknowledgements
Denis Allard and Christian Lantuéjoul acknowledge support of the RESSTE network funded by the Applied Mathematics and Informatics division of INRA. Xavier Emery acknowledges the support of Grant CONICYT PIA AFB180004 (AMTC) from the National Agency for Research and Development of Chile. We are grateful to three anonymous reviewers for their very careful reading and the many valuable comments that helped to improve the manuscript.
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Appendices
A Proofs for the spectral approach
1.1 A.1 Proof of Theorem 1
We use the notations defined in Sect. 3. Substituting (7) in (15), one obtains
where \(\lambda (\varvec{\omega },r)=\frac{|\varvec{\omega }|^2}{4r}\) and where the last equality stems from the symmetry of \({\mathcal {X}}\) and the integrability condition (8). Therefore, the distribution \(F_T ( d \tau \mid \varvec{\omega }, r)\) is an infinitely divisible distribution with Lévy measure \({\nu }_{\varvec{\omega },r}=\lambda (\varvec{\omega },r){\mathcal {X}}\).
Assume first that \({\mathcal {X}}({\mathbb {R}})=+\infty \). In this case, \({\nu }_{\varvec{\omega },r}({\mathbb {R}})=+\infty \) for \(\varvec{\omega }\ne 0\) and the Lévy measure \({\nu }_{\varvec{\omega },r}\) is absolutely continuous, since \({\mathcal {X}}\) is absolutely continuous. Then, by applying Lemma 1 of Sato (1982) the infinitely divisible distribution \(F_T ( d \tau \mid \varvec{\omega }, r)\) is absolutely continuous for \(\varvec{\omega }\ne 0\) and for a.e. \(\varvec{\omega }\). As a consequence, since \(F_S ( d\varvec{\omega }\mid r)\) is also absolutely continuous, it follows that
is absolutely continuous.
Let us now assume \(\vartheta ={\mathcal {X}}({\mathbb {R}})<+\infty \). In this case, (22) can be rewritten as
by symmetry of the finite measure \({\mathcal {X}}\). Since \(0< \vartheta < +\infty \), \(\Omega ^T(\varvec{\omega },r)\) has a compound Poisson distribution with \({\mathbb {P}}\bigl (\Omega ^T(\varvec{\omega },r)=0)\bigr ) =\exp \bigl (-\vartheta \lambda (\varvec{\omega },r)\bigr )\), which implies that
In conclusion, when \(\vartheta < +\infty \), the distribution of \(\Omega ^T\) has an atom at 0. Hence, F is not absolutely continuous in this case.
1.2 A.2 Proof of Theorem 2
Recall that
If \(u=0\), then
which is nothing but (14). This shows that \( F_S\) possesses the density
Plugging (24) into (23), one obtains
On the other hand, up to a multiplicative factor, the function \(\varvec{\omega }\, \mapsto \exp \left( -\, r |\varvec{h}|^2 / (\gamma (u)+1) \right) \) is the Fourier transform of a Gaussian random vector:
Comparing (25) and (26), the injectivity of the Fourier transform implies
or equivalently
which is precisely (15). \(\square \)
1.3 A.3 On the generic approach
The aim of this section is to show that \(\sum _{n \ge 1} X_{\mathrm{T}_n}\) and \(\Omega ^T (\varvec{\omega },r)\) have the same distribution. This is done by comparing their Fourier transforms. Remind that the spectral measure of the variogram is positive, symmetric, without an atom at the origin, and satisfies the integrability property
Let us start with
Because of (27), the positive function \(\theta \) defined on \({\mathbb {R}}_+\) by
is upper bounded by A. It follows that, for each \(t>0\), the measure
is a probability measure on \({\mathbb {R}}\). This measure is symmetric, and satisfies
Consider now a Poisson point process \(\bigl (\mathrm{T}_n: n \ge 1 \bigr )\) with intensity \(\lambda (t) = \lambda \, \theta (t)\) on \({\mathbb {R}}_+\) (\(\lambda \) is put here as a short notation for \(\frac{|\varvec{\omega }|^2}{4r}\)). Since \(\theta \)\(\lambda (t)\) is upper bounded by \(\lambda A\), this process has no accumulation point. Consider also a family \(\bigl ( X_t: t \in {\mathbb {R}}_+ \bigr )\) of independent random variables, with \(X_t\) being distributed as \({\mathcal {X}}_t\). Because \({\mathcal {X}}_t\) is symmetric, the Fourier transform of \(X_t\) can be written as
In what follows, we calculate the Fourier transform of \(\mathrm{T}= \sum _{n \ge 1} X_{\mathrm{T}_n}\). Denoting by \( \Lambda (t_0)\) the integral of \(\lambda (t)\) on \(]0,t_0[\), we have
This implies, owing to (29)
Permuting the integrals and replacing \(\lambda (t)\) by its expression, we obtain
Finally, the spectral representation (7) of \(\gamma \) gives
which is precisely the Fourier transform (15) of \(\Omega ^T (\varvec{\omega },r)\). \(\Box \)
1.4 A.4 Implementing the generic approach for logarithmic variograms
The construction of \(\theta \) and \({\mathcal {X}}_t\) proposed in Appendix A.3 is not necessarily unique. Starting from
it appears that a possible decomposition such as (16) can be obtained by taking
and
Consider now a Poisson point process \( \bigl ( \mathrm{T}_n : \, n \ge 1 \bigr ) \) on \({\mathbb {R}}_+\) with intensity function
A simple approach to simulate a nonhomogeneous Poisson point process is recommended by Devroye (1986, Chapter 6). It consists in calculating the inverse of the primitive of the intensity function \(\lambda (t)\) that vanishes at a, i.e. \(\Lambda (t) = \lambda \, \ln ( t / a) \), at the points of a homogeneous Poisson process with a unit intensity on the positive half-line, as shown in Fig. 3.
In this figure, the \(U_i\)’s are independent standard uniform variables and are related to the Poisson time \(\mathrm{T}_n\) by the formula \( \Lambda ( \mathrm{T}_n ) = \lambda \, \ln ( \mathrm{T}_n / a ) = - \ln \bigl ( U_1 \cdots U_n \bigr )\), which gives
Now, recall that \(\Omega ^T(\varvec{\omega },r)\) has the same distribution as \(\sum _{n=1}^{+\infty } X_{\mathrm{T}_n}\), where each \(X_t\) is distributed as \({\mathcal {X}}_t\). Because the \(X_t\)’s are independent, we have
Moreover, (30) implies
Consequently
Similarly, if the series is truncated at order \(n_0\), then the same calculation leads to the residual variance
Let \(\varepsilon > 0\) be arbitrarily small. From the previous calculations, it follows that
\(\square \)
B Proofs for the substitution approach
1.1 Proof of Theorem 3
For \((\varvec{x},t) \in {\mathbb {R}}^k \times {\mathbb {R}}\), \(Z(\varvec{x},t)\) conditional on \((R,\tilde{\varvec{\Omega }},U,W)\) (i.e. only letting \(\Phi \) vary randomly) has a zero expectation, insofar as it is proportional to the cosine of a random variable uniformly distributed on an interval of length \(2 \pi \). The prior expectation of \(Z(\varvec{x},t)\) is therefore zero:
Let us now calculate the covariance between the random variables \(Z(\varvec{x},t)\) and \(Z(\varvec{x}^{\prime },t^{\prime })\), with \((\varvec{x},t) \in {\mathbb {R}}^k \times {\mathbb {R}}\) and \((\varvec{x}^{\prime },t^{\prime }) \in {\mathbb {R}}^k \times {\mathbb {R}}\):
The last equality stems from the fact that \(-\ln (U)\) is an exponential random variable with mean 1 and is independent of \((R,\tilde{\varvec{\Omega }},W)\). Using the product-to-sum trigonometric identities, one can write the product of cosines as half the sum of two cosines, namely:
-
the cosine of the difference: \(\cos \left( \sqrt{2R} \, \langle \tilde{\varvec{\Omega }}, \varvec{x}-\varvec{x}^{\prime } \rangle + \frac{|\tilde{\varvec{\Omega }}|}{\sqrt{2}} (W(t)-W(t^{\prime })) \right) \)
-
the cosine of the sum: \(\cos \left( \sqrt{2R} \, \langle \tilde{\varvec{\Omega }}, \varvec{x}+\varvec{x}^{\prime } \rangle + \frac{|\tilde{\varvec{\Omega }}|}{\sqrt{2}} (W(t)+W(t^{\prime })) + 2 \Phi \right) \).
However, because \(\Phi \) is uniformly distributed on \((0, 2\pi )\) and independent of \((R, \tilde{\varvec{\Omega }},W)\), the expectation of the cosine of the sum is 0. It remains
The increment \(W(t)-W(t^{\prime })\) is a Gaussian random variable with zero mean and variance \(2 \gamma (t-t^{\prime })\) and is independent of \((R,\tilde{\varvec{\Omega }})\), i.e.:
with \(Y \sim {\mathcal {N}} (0,1)\) independent of \((R,\tilde{\varvec{\Omega }})\). Defining \(\varvec{h}= \varvec{x}- \varvec{x}^{\prime }\) and \(u=t - t^{\prime }\) and denoting by g the standard Gaussian probability density, one therefore obtains:
The last equality in (31) stems from the angle-sum trigonometric identity and the fact that \(\langle \tilde{\varvec{\omega }}, \varvec{h}\rangle \) is an odd function of \(\tilde{\varvec{\omega }}\) and \(|\tilde{\varvec{\omega }}|\sqrt{\gamma (u)} y\) is an even function of \(\tilde{\varvec{\omega }}\).
The simulated random field Z is therefore second-order stationary, since its expectation is identically zero and the covariance between any two variables \(Z(\varvec{x},t)\) and \(Z(\varvec{x}^{\prime },t^{\prime })\) only depends on \(\varvec{h}= \varvec{x}- \varvec{x}^{\prime }\) and \(u = t - t^{\prime }\). Up to a multiplicative factor, the last integral in (31) appears as the Fourier transform of the standard Gaussian probability density g(y) on \({\mathbb {R}}\). Specifically:
Hence:
The last equality in (32) stems from (11) and completes the proof. \(\square \)
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Allard, D., Emery, X., Lacaux, C. et al. Simulating space-time random fields with nonseparable Gneiting-type covariance functions. Stat Comput 30, 1479–1495 (2020). https://doi.org/10.1007/s11222-020-09956-4
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DOI: https://doi.org/10.1007/s11222-020-09956-4