\(\renewcommand{\var}{\operatorname{Var}} \renewcommand{\dd}{\mathrm{d}} \renewcommand{\pd}{\partial} \renewcommand{\bb}[1]{\mathbb{#1}} \renewcommand{\bf}[1]{\mathbf{#1}} \renewcommand{\vv}[1]{\boldsymbol{#1}} \renewcommand{\mm}[1]{\mathrm{#1}} \renewcommand{\cc}[1]{\mathcal{#1}} \renewcommand{\oo}[1]{\operatorname{#1}} \renewcommand{\gvn}{\mid} \renewcommand{\II}{\mathbb{I}}\)

Gamma processes are the classic model of subordinators, i.e.Â non-decreasing LĂ©vy processes.

Tutorial introductions to Gamma processes can be found in in (Applebaum 2009; Asmussen and Glynn 2007; Rubinstein and Kroese 2016; Kyprianou 2014). Existence proofs etc are deferred to those sources. You could also see Wikipedia.

## As a LĂ©vy process

Wikipedia tells us that a Gamma process is a *pure jump process* with jump *intensity* given

\[\nu (x)=\alpha x^{-1}\exp(-\lambda x).\]

That is, the Poisson rate, with respect to â€śtimeâ€ť \(t\), of jumps whose size is in the range \([x, x+dx)\), is \(\nu(x)dx.\) We think of this as an infinite superposition of Poisson processes driving different sized jumps, where the jumps are mostly tiny.

This is how we think about these processes in terms of LĂ©vy process theory, at least; we tend to set them up differently for statistical inference, in terms of the distribution of the process and its increments.

The marginal distribution of the an increment of duration \(t\) is given by the Gamma distribution, which we had better cover first.

## Gamma distributions

Let us take a brief divergence into the vanilla Gamma distribution which induces Gamma process.

The density \(g(x;t,\alpha, \lambda )\) of the univariate gamma is

\[
g(x; \alpha, \lambda)=
\frac{ \lambda^{\alpha} }{ \Gamma (\alpha) } x^{\alpha\,-\,1}e^{-\lambda x},
x\geq 0.
\] This is once again the *shape-rate* parameterisation, with rate \(\lambda\) and shape \(\alpha,\). We can think of the Gamma distribution as the distribution at time 1 of a Gamma process.

If \(G\sim \operatorname{Gamma}(\alpha, \lambda)\) then \(\bb E(G)=\alpha/\lambda\) and \(\var(G)=\alpha/\lambda^2.\)

We use various facts about the Gamma distribution which quantify its divisibility properties.

- If \(G_1\sim \operatorname{Gamma}(\alpha_1, \lambda),\,G_2\sim \operatorname{Gamma}(\alpha_2, \lambda),\) and \(G_1\perp G_2,\) then \(G_1+G_2\sim \operatorname{Gamma}(\alpha_1+\alpha_2, \lambda)\) (additive rule)
- If \(G\sim \operatorname{Gamma}(\alpha, \lambda)\) then \(cG\sim \operatorname{Gamma}(\alpha, \lambda/c)\) (multiplicative rule)
- If \(G_1\sim \operatorname{Gamma}(\alpha_1, \lambda)\perp G_1\sim \operatorname{Gamma}(\alpha_2, \lambda)\) then \(\frac{G_1}{G_1+G_2}\sim \operatorname{Beta}(\alpha_1, \alpha_2)\) independent of \(G_1+G_2\) (stick-breaking rule)

## The Gamma process

The univariate Gamma *process* \(\{\Lambda(t;\alpha,\lambda\}\) is an independent-increment process, with time index \(t\) and parameters by \(\alpha, \lambda.\)

The marginal density \(g(x;t,\alpha, \lambda )\) of the process at time \(t\) is a Gamma RV, specifically,

\[ g(x;t, \alpha, \lambda) =\frac{ \lambda^{\alpha t} } { \Gamma (\alpha t) } x^{\alpha t\,-\,1}e^{-\lambda x}, x\geq 0. \] That is, \(\Lambda(t) \sim \operatorname{Gamma}(\alpha(t_{i+1}-t_{i}), \lambda)\).

which corresponds to increments per unit time in terms of \(\bb E(\Lambda(1))=\alpha/\lambda\) and \(\var(\Lambda(1))=\alpha/\lambda^2.\)

Note that if \(\alpha t=1,\) then \(X(t;\alpha ,\lambda )\sim \operatorname{Exp}(\lambda).\)

This leads to a method for simulating a path of a gamma process at a sequence of increasing times, \(\{t_1, t_2, t_3, t_L\}.\) Given \(\Lambda(t_1;\alpha, \lambda),\) we know that the increments are distributed as independent variates \(G_i:=\Lambda(t_{i+1})-\Lambda(t_{i})\sim \operatorname{Gamma}(\alpha(t_{i+1}-t_{i}), \lambda)\). Presuming we may simulate from the Gamma distribution, it follows that

\[\Lambda(t_i)=\sum_{j \lt i}\left( \Lambda(t_{i+1})-\Lambda(t_{i})\right)=\sum_{j \lt i} G_j.\]

A standard \(d\)-dimensional gamma process is the concatenation of \(d\) independent univariate Gamma processes.

## Gamma bridge

Consider a univariate gamma process, \(\Lambda(t)\) with \(\Lambda(0)=0.\) The Gamma bridge, analogous to the Brownian bridge, is the Gamma process conditional upon attaining a fixed the value \(S=\Lambda(1)\) at terminal time \(1.\) We write \(\Lambda_{S}:=\{\Lambda(t)\mid \Lambda(1)=S\}_{0\lt t \lt 1}\) for the paths of this process.

We can simulate from the Gamma bridge easily. Given the increments of the process are independent, if we have a gamma process \(G\) on the index set \([0,1]\) such that \(\Lambda(1)=S\), then we can simulate from the bridge paths which connect these points at intermediate time \(t,\, 0<t<1\) by recalling that we have known distributions for the increments; in particular \(\Lambda(t)\sim\operatorname{Gamma}(\alpha, \lambda)\) and \(\Lambda(1)-\Lambda(t)\sim\operatorname{Gamma}(\alpha (1-t), \lambda)\) and these increments, as increments over disjoints sets, are themselves independent. Then, by the stick breaking rule,

\[\frac{\Lambda(t)}{\Lambda(1)}\sim\operatorname{Beta}(\alpha t, \alpha(1-t))\] independent of \(\Lambda(1).\) We can therefore sample from a path of the bridge \(\Lambda_{S}(t)\) for some \(t\lt 1\) by simulating \(\Lambda_{S}(t)=B S,\) where \(B\sim \operatorname{Beta}(\alpha (t),\alpha (1-t)).\)

## Gamma processes with dependent components

I am not sure what general correlations are possible here, but one obvious one is to choose a transform matrix \(M\) with non-negative entries. Then the process \(\{M\Lambda(t)\}\) is still marginally a Gamma process, but the components of the vector are no longer independent. Is this the most general possible Gamma process? What is the covariance structure of that process? đźš§

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