Working through some generalisations of the GaltonWatson process as an INAR process.
Consider

van Harn & Steutel’s work on “Fstable branching processes.” Also bounded influence kernel?

Lee, Hopcraft, Jakeman and Williams on discrete stable processes. Discrete state, continuous time  How do these differ from the usual Hawkes processes, if at all?
Long Memory GaltonWatson
For my own edification and amusement I would like to walk through the construction of a particular analogue of the continuous time Hawkes point process on a discrete index set.
Specifically, a nonMarkovian generalisation of the GaltonWatson process which still operates in quantised time, but has interesting, possiblyunbounded influence kernels, like the Hawkes process.
I denote a realisation of the process \(\{N_t\}_{t\in\mathbb{N}}\). and the associated nonnegative increment process \(\{X_t\}\equiv\{N_tN_{t1}\}\) and a conditional nonnegative pseudointensity process \(\lambda_t\equiv g(\{N_s\}_{s \lt t})\), adapted to the whole history \(\{N_s\}_{s \lt t}\). By “pseudointensity” I mean that the innovation law \(X_t\sim\mathcal{L}_t\) is parameterised (solely, for now) by some scalarvalued process \(\lambda_t(\mathcal{F}(X_t))\). That is, \(\{X_t\}\{N_s\}_{s \lt t}\sim \mathcal{L}(\lambda_t)\). For the moment I will take this be be Poisson. To complete the analogy with the Hawkes process I choose the dependence on the past values of the process linear with influence kernel \(\phi\): This is also close to clustering, and indeed there are lots of papers noticing the connection.
Then a linear conditional intensity process \(\lambda_t\) would be
The \(1\) in \(\phi(st1)\) is to make sure our influence kernel is defined on \(\mathbb{N}_0\), which is convenient for typical count distribution functions.
If the kernel has bounded support such that
then we have an autoregressive count process of order p. More on that in a moment.
What influence kernel shape will we use?
Geometric distributions are natural, although it doesn’t have to be strictly monotonic, or even unimodal. Poisson or negative binomial would also work. We could in general give any arbitrary probability mass function as influence kernel, or use a nonparametric form.
for some \(\{a_k, b_k\}\).
If we expect to be using sparsifying lasso penalties for such a kernel we probably want to decompose the kernel in a way that minimises correlation between mixture components to improve our odds of correctly identifying dependency at different scales. If we constrain our distributions to be positive the only way to do this is for them to be completely orthogonal is to have disjoint support.
Intermediately, we could choose a Poisson mixture
There is a subtlety here with regard to the filtration  do we set up the kernel strictly to regard triggering events at previous timesteps? If so, no problem. If we want to allow sameday triggering, we might allow the exogenous events to also contribute to the kernel, in which case we might have to estimate an extra influence parameter, or find some principled way to include it in the kernel weights.
TODO: unconditional distribution using, e.g. generator fns.
Autoregressive characterisation
Turns out Steutel and van Harn saw me coming here, and characterised this process in 1979  see StHa79. (Wait  is this strictly true, that we can make this go with a thinning operator? Many related definitions here, muddying the waters)
We need their binomial thinning operator \(\odot\), which is defined for some count RV \(X\) by
for \(N_i\) independent \(\text{Bernoulli}(\alpha)\) RVs.
In terms of generating functions,
\(G_{\alpha\odot X}(s)=G_{X}(1\alpha+\alpha s)\)
There are many generalisation of this operator  see Weiß08 for an overview.
Anyway, you can use this thinning operator to construct an autoregressive time series model driven by thinned versions of its history.
(Maybe it would be simpler to use Fokkianos’ GLM characterisation? I think they are equivalent or nearly equivalent in ths case  certainly with stable distributions they are.)
Estimation of parameters
Well studied for finiteorder GINAR(p) processes.
Influence kernels
Hardiman et al propose multiplescale exponential kernels to simultaneously estimate decays and branching ratios Bacry et al 2012 have a related nonparametric method based on estimating the kernel in the spectral domain. Convergence properties are unclear.
We are also free to use a sumofexponentials kernel, possibly calculating the branching ratio from that alone, and some measure of tailheaviness from that.
Possibly Smoothlasso (penalises component CHANGE)
Endoexo models
Note that we can still recover the endoexo model with this by simply calculating the projected ratio between exogenous and endogenous events. It would be interesting to derive the properties of this as a single parameter of interest.
Short timescale process
We want the distribution within a bin to be plausibly a cluster process.
The distribution of subcritical processes are generally tedious to calculate, although we can get a nice form for the generating funciton for a geometric offspring distribution from HaJV05, p115.
Set \(\frac{1}{\lambda+1}=p\) and \(q=1p\). We write \(G^n\equiv G\cdot G\cdot \dots \codt G\cdot G\) for the \(n\)fold composition of \(G\). Then the (noncritical) geometric offspring distribution branching process obeys the identity
This can get us a formula for the first two factorial moments, and hence the mean and variance, which is all we will bother with here.
Although, reading HaOa74 I see that the actual offspring distribution is Poisson. Maybe I should use Dwas69 to get the moments? Dominic Yeo has a great explanation as always.
Ideas
Consider the contagion process with immigration, where the immigration rate must have the same distribution as this, where the immigration rate is proportional to a contagion proces with a law from the same family (possibly different parameters). Possibly many such, on a graph. e.g. a model for multiple “cities” or other discrete population with some contagion between them. (I’m sure there is some evolutionary biology on this point, not just epidemiology.)
Can this be linked to general theory of coarse graining?
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