Forest-structured Dvoretzky-Kiefer-Wolfowitz post hoc confidence bounds
Mathematical Methods AIMech:ForestStructuredDKWPostHocConfidenceBounds DRAFT
Aliases: Forest-structured reference family post hoc false positive bounds, Bilinear bottom-up tree interpolation for post hoc false discovery bounds, Local DKW-Massart quadratic-inversion reference-family calibration, Simes-DKW hybrid post hoc confidence envelope
A multiple-testing post hoc inference methodology that constructs simultaneous (1 - alpha)-confidence upper bounds V(S) on the number of false positives |S cap H_0(P)| across all 2^m subsets S subset N_m = {1, ..., m} by combining a deterministic forest-structured reference family R_ref = (R_k, zeta_k(X))_{k in K} (where any two regions R_k, R_{k'} are either disjoint or nested: R_k cap R_{k'} in {R_k, R_{k'}, empty}) with random local Dvoretzky-Kiefer-Wolfowitz-Massart (DKW) upper bounds zeta_k(X). Unlike nested data-dependent p-value level-set reference families with fixed capacities zeta_k = k - 1, multi-subset interpolation V_tilde^q_{R_ref}(S) = min_{Q subset K, |Q| <= q} (sum_{k in Q} (zeta_k wedge |S cap R_k|) + |S \ cup_{k in Q} R_k|) is proven to equal the exact combinatorial optimal bound V^*_{R_ref}(S) = V_tilde_{R_ref}(S) = V_tilde^d_{R_ref}(S) whenever R_ref has a forest structure (where d is the maximum number of pairwise disjoint regions in R_ref, Theorem 3.6) and is computable in O(Hm) (or bilinear O(|K| |S|)) time via a bottom-up tree dynamic program over the completed atomic forest R_ref^oplus (Algorithm 1). Local capacities calibrated via quadratic inversion of the tight Massart-DKW inequality control the Joint Error Rate at level alpha under null super-uniformity and independence (Proposition 4.1), uniformly dominate the Genovese-Wasserman (2004) DKW bound (Remark 4.3, Lemma A.1), improve Simes and Bonferroni post hoc bounds by an asymptotic factor 1 - r on localized signal regions of signal density r (Proposition 4.5, Corollary 4.6), and combine with the Simes bound into a hybrid envelope V^gamma_{hybrid}(alpha, S) = min(V_{Simes}((1-gamma)alpha, S), V_{tree}(gamma*alpha, S)) (Section 5.3).
Identity Notes & Concept Scope
Distinct from AIMech:ReferenceFamilyJERPostHocConfidenceBounds (Blanchard, Neuvial, & Roquain 2020, The Annals of Statistics 48(3):1281-1303, arXiv:1703.02307), which studies random data-dependent p-value level-set reference regions R_k(X) = {i : p_i(X) <= t_k(lambda)} with fixed capacities zeta_k = k - 1 calibrated by single-step or step-down lambda-calibration and uses single-region interpolation V_bar_{R_ref}(S) = |S| wedge min_k (zeta_k + |S \ R_k|) (exact only for nested families R_1 subset ... subset R_K). Durand, Blanchard, Neuvial, & Roquain (2020, Scandinavian Journal of Statistics 47(4):1114-1148, arXiv:1807.01470v2, DOI:10.1111/sjos.12453) instead fix deterministic multiscale spatial/hierarchical regions R_k satisfying the (Forest) condition (two regions are either disjoint or nested) with random local error capacities zeta_k(X), introduce the q-subset interpolation hierarchy V_tilde^q_{R_ref}(S), prove exact optimality V^*_{R_ref}(S) = V_tilde_{R_ref}(S) = V_tilde^d_{R_ref}(S) on any forest family together with an O(Hm) bottom-up tree dynamic program (Theorem 3.6, Algorithm 1, and Algorithm 2 atomic decomposition), derive the quadratic-inversion local DKW-Massart bound zeta_k(X) (Proposition 4.1) and its asymptotic 1 - r localized-signal improvement over Simes/Bonferroni (Proposition 4.5, Corollary 4.6), and introduce the Simes-DKW hybrid bound V^gamma_{hybrid}(alpha, S). Also distinct from AIMech:SimultaneousPathwiseFalseDiscoveryProportionBounds (Katsevich & Ramdas 2020), which bounds ordered pathwise sets S_t along a single filtration.
Claims & Grounded Evidence
4 Claims
Mechanism
#multi-subset-interpolation-hierarchy-and-forest-exactness-theorem
For any reference family R_ref = (R_k(X), zeta_k(X))_{k in K} of cardinality K = |K|, Section 2.3 (Equations 8-9, Lemma 2.1, page 4) defines the q-subset interpolation hierarchy V_tilde^q_{R_ref}(S) = min_{Q subset K, |Q| <= q} (sum_{k in Q} (zeta_k wedge |S cap R_k|) + |S \ cup_{k in Q} R_k|) for 1 <= q <= K and V_tilde_{R_ref}(S) = V_tilde^K_{R_ref}(S), satisfying V^*_{R_ref}(S) <= V_tilde_{R_ref}(S) <= V_tilde^q_{R_ref}(S) <= V_tilde^1_{R_ref}(S) = V_bar_{R_ref}(S). On non-forest families, V^*_{R_ref}(S) < V_tilde_{R_ref}(S) can be strict (Example 2.2, page 4: m = 4, K = 3, R_1 = {1,2,4}, R_2 = {2,3,4}, R_3 = {1,3,4}, zeta_1 = zeta_2 = zeta_3 = 1, S = N_4 gives V^*_{R_ref}(S) = 1 < V_tilde_{R_ref}(S) = 2). When R_ref satisfies the (Forest) condition (forall k, k' in K, R_k cap R_{k'} in {R_k, R_{k'}, empty}, Definition 3.1, page 5), Lemma 3.3 and Algorithm 2 (Appendix B, pages 22-24) construct an atomic partition (P_n)_{1 <= n <= N} of N_m such that every R_k = P_{i:j} = cup_{i <= n <= j} P_n; completing R_ref to R_ref^oplus by adjoining missing atoms with trivial bounds zeta_{i,i} = |P_i| preserves V^*_{R_ref^oplus}(S) = V^*_{R_ref}(S) and V_tilde_{R_ref^oplus}(S) = V_tilde_{R_ref}(S) (Definition 3.5, Lemma A.4, pages 6, 22), and Theorem 3.6 (pages 6, 15-17) proves exact equality V^*_{R_ref}(S) = V_tilde_{R_ref}(S) = V_tilde^d_{R_ref}(S) where d is the maximum number of pairwise disjoint sets in R_ref, recovering V^*_{R_ref} = V_bar_{R_ref} under (Nested) (d = 1) and V^*_{R_ref}(S) = sum_{k=1}^K (zeta_k wedge |S cap R_k|) + |S \ cup_{k=1}^K R_k| under (Disjoint) (Corollary 3.7, page 7).
Mechanism
#bottom-up-dynamic-programming-algorithm-on-completed-forest
Algorithm 1 (Section 3.2, pages 7-8) computes the exact optimal bound V^*_{R_ref}(S) on any forest-structured reference family by first completing R_ref to R_ref^oplus so (All-atoms) holds, computing node depths phi(k) = 1 + |{k' in K : R_{k'} supsetneq R_k}| (Equation 11) and maximal forest depth H = max_{k in K} phi(k), forming the level partitions K^h = {(i, j) in K^oplus : phi(i, j) = h or (i = j and phi(i, i) <= h)} (which partition N_m for every 1 <= h <= H by Lemma A.2, page 21), initializing V_k = zeta_k wedge |S cap R_k| for k in K^H, and iterating bottom-up for h = H - 1, ..., 1 via newV_k = min(zeta_k wedge |S cap R_k|, sum_{k' in Succ_k} V_{k'}) where Succ_k = {k' in K^{h+1} : R_{k'} subset R_k}, returning V^*_{R_ref}(S) = sum_{k in K^1} V_k in O(Hm) time (or bilinear complexity in the sizes of the reference hierarchy and selected subset S).
Mechanism
#local-dkw-massart-quadratic-inversion-calibration-and-gw-dominance
For any K = |K| deterministic reference regions R_k subset N_m with alpha / K < 1/2, under (Superunif) and (Indep), Proposition 4.1 and Section 7.4 (pages 9, 18, Equations 16-17 and 32) prove that R_ref = (R_k, zeta_k(X))_{k in K} controls the Joint Error Rate at level alpha using local bounds obtained by quadratic inversion of the Dvoretzky-Kiefer-Wolfowitz inequality with Massart's (1990) tight constant: |R_k cap H_0| <= |R_k| wedge min_{t in [0, 1)} (C / (2(1 - t)) + sqrt(C^2 / (4(1 - t)^2) + sum_{i in R_k} 1{p_i(X) > t} / (1 - t)))^2 where C = sqrt((1/2) log(K / alpha)), computable by evaluating the finite minimum over the s + 1 grid points 0 = p_{(0)} <= p_{(1)} <= ... <= p_{(s)} of ordered p-values in R_k (Remark 4.2, page 9). Remark 4.3 and Lemma A.1 (Equation 33, page 21: d wedge (b/(2a) + sqrt(b^2/(4a^2) + c/a))^2 <= d wedge ((c + d^{1/2}*b)/a)) prove that this quadratic-inversion bound is a uniform improvement over the classical Genovese & Wasserman (2004, Equation 16) DKW bound zeta^{GW}_k(X) = |R_k| wedge min_{t in [0, 1)} floor((sum_{i in R_k} 1{p_i(X) > t} + |R_k|^{1/2} * C) / (1 - t)).
Mechanism
#localized-signal-asymptotic-improvement-and-simes-dkw-hybrid-envelope
Under the regular partition R_k = {1 + (k - 1)s, ..., ks} (1 <= k <= K = m/s) where region R_1 contains false nulls in proportion r = |R_1 cap H_1| / |R_1| in (0, 1) with independent one-sided Gaussian test statistics X_i ~ N(mu, 1) (mu > 0) and R_2, ..., R_K are purely null, Proposition 4.5 (pages 10, 18-19, Equations 20-23) establishes that E(V_{DKW}(R_1))/|R_1| <= 1 wedge (1 - r + 2*r*Phi_bar(mu) + (4*C/sqrt(s))*(1 + C/sqrt(s))), whereas E(V_{Simes}(R_1))/|R_1| >= (1 - r)*(1 - alpha*s/m) + r*Phi_bar(mu - Phi_bar^{-1}(alpha*s/m)) and E(V_{Bonf}(R_1))/|R_1| = (1 - r)*(1 - alpha/m) + r*Phi_bar(mu - Phi_bar^{-1}(alpha/m)). Corollary 4.6 (page 10) proves that as m -> infty with s >> log K, s << m, mu -> infty, and mu - Phi_bar^{-1}(alpha*s/m) -> -infty, limsup_m E(V_{DKW}(R_1))/|R_1| <= 1 - r while limsup_m E(V_{Simes}(R_1))/|R_1| = limsup_m E(V_{Bonf}(R_1))/|R_1| = 1, improving Simes and Bonferroni by an asymptotic factor 1 - r. Because V_{tree} depends on alpha only through 2*C^2 = log(K / alpha) (Remark 4.4) while paying a minimum floor cost zeta_k(X) >= floor(log(K/alpha)/2) >= 1 on small sets, Section 5.3 (pages 12-14, Figure 10) defines the hybrid post hoc bound V^gamma_{hybrid}(alpha, S) = min(V_{Simes}((1 - gamma)*alpha, S), V_{tree}(gamma*alpha, S)) for gamma in [0, 1], which is a valid (1 - alpha) post hoc bound by a two-term union bound that matches Simes pinpointing on small p-value level sets S_k and multiscale DKW-tree proportion estimation on larger sets.
⚠️ Curated Limitations & Negative Results
- Exact equality V^*_{R_ref}(S) = V_tilde_{R_ref}(S) = V_tilde^d_{R_ref}(S) and the O(Hm) bottom-up dynamic program (Theorem 3.6, Algorithm 1) require the reference family R_ref to satisfy the (Forest) condition (any two regions are disjoint or nested); when reference regions overlap without nesting, V^*_{R_ref}(S) < V_tilde_{R_ref}(S) can be strict (Example 2.2, page 4, verified in research/discovery/forest-structured-dkw-post-hoc-confidence-bounds-boundaries.py where V^*_{R_ref}(S) = 1 < V_tilde_{R_ref}(S) = 2).
- The local DKW-Massart calibration (Proposition 4.1) requires within-region independence of null p-values (Indep) and incurs an unavoidable non-zero floor cost zeta_k(X) >= floor(log(K/alpha)/2) >= 1 whenever alpha <= e^{-2}*K (page 9), making pure DKW bounds trivial on small selection sets S of size |S| <= floor(log(K/alpha)/2) unless combined with Simes via the hybrid bound V^gamma_{hybrid} (Section 5.3, pages 12-14).
- Four minor typographical slips in arXiv:1807.01470v2 were identified and verified during full-text inspection: (1) in Section 2.2 (page 4, line 1), 'for all S subset N_m, V^*_{R_ref}(R) <= V(R)' writes R instead of S inside V^*_{R_ref} and V, (2) in Equation (16) and Remark 4.2 (page 9), the integer floor brackets floor(...)^2 are printed inside the exponent 2 rather than enclosing the squared expression floor((...)^2) derived in Equation (32) of the proof of Proposition 4.1 (page 18; note also page 9 line 13 writes zeta_k(X) >= floor(log(K/alpha)/2) = floor(C^2), which is the floor of the square rather than the square of the floor), (3) in Section 5.2 (page 12, line 1), 'choice of the model parameters d (in rows)' writes d instead of r (the signal proportion parameter shown in the row labels of Figure 8), and (4) in Section 6.2 Equation (24) (page 15), P_{X~P}(|R_k cap H_0(P)| <= L_k(lambda)) <= lambda writes '<= L_k(lambda)' inside the probability instead of '> L_k(lambda)' (or '>= 1 - lambda' on the right-hand side).
- All 24 pages of the full-text PDF of Durand, Blanchard, Neuvial, & Roquain (2020, Scandinavian Journal of Statistics 47(4):1114-1148, arXiv:1807.01470v2, DOI:10.1111/sjos.12453) were read. Constructed numerical checks in research/discovery/forest-structured-dkw-post-hoc-confidence-bounds-boundaries.py verify (1) Example 2.2 non-forest gap V^* = 1 < V_tilde = 2, (2) Example 3.2/3.4 node depths, Algorithm 2 atomic partition, and Theorem 3.6 / Algorithm 1 exactness across all 256 subsets of an 8-element forest, (3) Proposition 4.1 empirical JER control and Remark 4.3 / Lemma A.1 strict dominance over the Genovese-Wasserman (2004) DKW bound across 3,000 region-trial checks, and (4) Proposition 4.5 / Figure 6 localized-signal ratio upper bounds and Section 5.3 Simes-DKW hybrid envelope complementarity.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:DataDredgingBias
Data Dredging Bias
|
related_match | Forest-structured DKW post hoc confidence bounds V(S) provide simultaneous (1 - alpha)-confidence guarantees on false positives over all 2^m data-dependently selected subsets S subset N_m, directly preventing post-selection data-snooping invalidation (AIO:DataDredgingBias). |
Evidence Sources & Primary Literature
3 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
durand2020-pdf
|
Guillermo Durand, Gilles Blanchard, Pierre Neuvial, Etienne Roquain
|
primary paper
full_text
|
ID: https://doi.org/10.1111/sjos.12453
sha256:c8e024a9c369…
|
durand2020-crossref
|
Guillermo Durand, Gilles Blanchard, Pierre Neuvial, Etienne Roquain
|
primary paper
abstract_only
|
ID: https://doi.org/10.1111/sjos.12453
sha256:ab88eb06001f…
|
durand2020-abs
|
Guillermo Durand, Gilles Blanchard, Pierre Neuvial, Etienne Roquain
|
primary paper
abstract_only
|
ID: https://doi.org/10.48550/arXiv.1807.01470
sha256:7bcfe7da684a…
|
Ontology & Grounding Topology
Open in interactive network graph →Curation Provenance
Curator: Antigravity
Method: ai_assisted
Model: Gemini 3
Created: 2026-10-17T17:05:00Z
Updated: 2026-10-17T17:05:00Z
Source File:
data/curated/ForestStructuredDKWPostHocConfidenceBounds.yaml
· Schema:
curated_concept.yaml
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