Result · FI-RANK-CEILING
A bin budget limits information rank
Derived from the literaturepublication: derived from published
Meaning
A regular score model with K categories has retained-information rank at most K minus one.
Scientific context
With K categories and a d-dimensional parameter, the binned information has rank at most min(d, K-1), so a nonsingular answer needs at least d + 1 categories. Asking three bins to measure four parameters is not a hard optimization problem to solve poorly; it is an impossible one, and the library refuses such a configuration rather than returning a degenerate answer.
Statement
rank(I_q)<=min(d,K-1); positive-definite full-D information requires K>=d+1.
Closest prior work
Relationships
Rests on
Enables
- Full in-bin Ds profiling is singular when K<=dDS-FULL-PROFILE-K-LE-D-SINGULAR
- Projected efficient-score problem only requires K>=d_ψ+1DS-PROJECTED-K-REQUIREMENT
- Frozen-rule vector retention: plug-in CLT for the geometric-mean D-retention with a consistent influence-function varianceRETENTION-PLUGIN-CLT-FROZEN-VECTOR
- Singular binned information: the plug-in geometric-mean retention is biased upward at rate n^{-(d-r)/d}RETENTION-PLUGIN-SINGULAR-ENDPOINT-RATE
In the library
Carried by InformationReport.effective_rank. Reference (opens in a new tab).
Enforced by the refusal CE-DS-MARGINS-RANK-VACUITY-001: the library declines the operation and names the fixture.
Local graph
Proof
U4. Rank ceiling
Since ,
Therefore is necessary for nonsingular full-D information.