How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
A common factor can reverse an unreduced root classification
Example
Let be centered white noise with variance and put . Its canonical ARMA representation is the reduced pair , which is causal. Multiplying both sides of this equation by gives the same process the unreduced equation with pair . That pair has a common root at ; applying the root criterion before cancelling it would falsely classify the process as noncausal.
Facts & Assumptions
Given: A centered real white-noise sequence with variance and the process .
Linear processes, white noise, and ARMA models: an ARMA equation is with normalized polynomials; the canonical representation requires common factors to be cancelled.
ARMA causality and invertibility root criteria: for a normalized coprime pair, causality is equivalent to absence of zeros of in .
Verification
Both polynomials have constant term one and the common factor has root , so the unreduced pair is not coprime. Cancelling it leaves , whose AR polynomial has no root.
With , the reduced equation holds and has the causal representation , for . By [F2] the reduced pair is causal. Multiplying both sides by the same finite filter gives , so the unreduced pair also displays the same process.
The unreduced has a zero in , but [F2] applies only after common-factor cancellation. Thus this raw root is not a valid classification of the process and explains the normalization clause.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
6 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Dewei Wang, STAT 720 Time Series Analysis, Theorems 3.1–3.2 (standard reference, not scraped)
- Nicolai Meinshausen, Time Series Analysis: Notes for the course HS 2022, Section 2.5 (standard reference, not scraped)