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Simple counting-process intensity and compensator contract
Definition
Assume the Axiom of Choice The Axiom of Choice. Fix a finite horizon , a probability space and a continuous-time filtration in the all-pairs convention of Continuous-time filtrations and all-pairs martingales. A simple unit-jump counting process is an adapted, càdlàg (right-continuous with left limits), nondecreasing, integer-valued process with , almost surely, and for every outside one null event; here .
An at-risk process is a predictable process , and an intensity density is a finite, nonnegative predictable process . Form the Lebesgue integral For this model and every nonnegative predictable process , define the pathwise energy integral by The continuous intensity compensator contract requires the defining integral for to be finite pathwise, and requires to be continuous, adapted and predictable with , and that . Set . The model contract is the additional hypothesis that is a square-integrable martingale relative to the stated filtration. Under this contract is a continuous predictable compensator of .
The displayed intensity density alone does not assert that is a martingale, that such a process exists for a given , or that the contract follows from an empirical observation scheme. No general compensator-existence theorem is included here. The Axiom of Choice is inherited from the continuous-time martingale and conditional-expectation interfaces; no further choice is used to state the model contract.
Source notes
Tian's Unit 8 sets up the one-event process, at-risk indicator and integrated hazard; Andersen–Borgan–Gill–Keiding define the counting-process compensator as a predictable finite-variation process for which is a local martingale. This library contract strengthens that model clause to a square-integrable martingale and imposes for the isometry developed below. It makes no existence claim from an intensity formula.
Depends on
Used by
- Elementary predictable integral against a counting-process martingale Definition
- L² counting-process integral when the limit exists Definition
- Optional and predictable brackets for counting-process martingales Definition
- Temporal point-process models and conditional intensity Definition
- A finite-window Poisson process that terminates at time one Example
- A tied jump produces a nonzero cross bracket Example
- Delayed entry with a predictable risk process Example
- Homogeneous Poisson likelihood and a Hawkes intensity specification Example
- One exponential event: compensator and isometry Example
- Consistency of the weighted logrank variance estimate Lemma
- Cox score martingale representation and random-risk-mean replacement Lemma
- Isometry for elementary predictable counting-process integrals Lemma
- Pathwise identity for bounded predictable counting-process integrals Lemma
- Predictable step processes are dense for the compensator L² norm Lemma
- Uniform Cox partial-likelihood Hessian convergence Lemma
- Weighted logrank score expansion under a common-hazard model Lemma
- Brackets of a compensated simple counting process Theorem
- Compensators, brackets, and jump-integral isometry for reaction counts Theorem
- Cox partial-likelihood score expansion and estimator linearization Theorem
- Cross brackets vanish without simultaneous jumps Theorem
- L² extension of the predictable counting-process integral Theorem
- Pointwise influence limit and Greenwood variance consistency Theorem
- Terminal normal limit for weighted logrank scores Theorem
Dependency tree · two levels
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Sources
- Lu Tian, STAT331 Unit 8: Lebesgue-Stieltjes Integrals, Martingales, Counting Processes, PDF pp. 1–5, 12–14 (standard reference, not scraped)
- P. K. Andersen, Ø. Borgan, R. D. Gill, N. Keiding, Censoring, Truncation and Filtering in Statistical Models Based on Counting Processes, §2, PDF pp. 3–8 (standard reference, not scraped)