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.
Continuous Neyman allocation may require constrained rounding
Example
Take two strata of sizes and . In the first, let the values be ; in the second, take five copies each of and . With total sample size six, the continuous Neyman allocation rounds to zero units in the first stratum under nearest- integer rounding, which violates the stratified design's positive sample-size requirement.
Facts & Assumptions
Given: The fixed strata above, independent within-stratum SRSWOR, total sample size six, and componentwise rounding to the nearest integer.
Stratified sampling designs and the stratified total estimator: A stratified design requires integer sample sizes ; for a noncertainty stratum, .
Continuous fixed-size Neyman allocation minimizes stratified variance: With positive noncertainty stratum variances and a fixed positive total sample size, the uncapped continuous optimum is proportional to ; this relaxation imposes no integer, capacity, lower-bound, or minimum-sample constraints.
Unbiasedness and variance of the stratified total estimator: For an admissible allocation, the stratified total's variance is the sum of the stratum terms over noncertainty strata.
Verification
The first stratum has mean zero and . The second has mean zero and , so .
The Neyman weights are and . By [F2] and step 1.1, and . Nearest-integer rounding gives , which violates [F1]'s condition.
Allocation is admissible. By [F3] and step 1.1 its variance is ; this feasible correction is not claimed to solve every constrained integer problem.
Depends on
Used by
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Dependency tree · two levels
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Sources
- Shalabh, Sampling Theory, MTH 417 lecture notes, Chapter 4: Stratified Sampling, PDF pp. 8–9 and 16 (standard reference, not scraped)
- Robin Arnold, STAT392 Sample Surveys, Chapter 8: Stratified Sampling, §8.8 and Notes 1–2 (standard reference, not scraped)
- Tommy Wright, A Simple Method of Exact Optimal Sample Allocation under Stratification with Any Mixed Constraint Patterns, U.S. Census Bureau, §§2.1–2.3 (standard reference, not scraped)