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BY arbitrary dependence and BH PRDS
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Approximation Algorithms and Gap Reductions
- BH independent FDR
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Conditional Distributions and Regular Conditional Probability
- Conditional Expectation
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Estimators and Randomized Procedures
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Infinite Product Measures and Kolmogorov Extension
- Lebesgue-Stieltjes Measures and Distribution Functions
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Loss, Risk and Decision Theory
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Polynomial Rings, the Division Algorithm and Roots
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Standard-Borel Real Codings and Determining Classes
- Statistical Experiments, Models and Identifiability
- Subspaces, Products, and Quotients
- Suprema and Infima
- Tests, Neyman–Pearson and Valid P-values
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Lebesgue Integral and the Convergence Theorems
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Valid p-values, Bonferroni, simultaneous coverage
2 · Summary
The Benjamini–Yekutieli (BY) step-up rule uses critical values , where . For any finite family of valid p-values, its FDR is at most under arbitrary dependence. The proof sums each true-null rejection contribution over the possible rejection counts and applies only marginal superuniformity.
Ordinary Benjamini–Hochberg (BH) uses critical values ; these equal the BY cutoffs when and are larger when . Its separate guarantee here assumes that the p-value vector is positive regression dependent on the true-null coordinates (PRDS): conditional probabilities of all increasing Borel sets must have nondecreasing versions as each true-null p-value increases. Under that exact condition, BH also has FDR at most . PRDS is a distributional condition; pairwise positive covariance alone does not verify it, and two-sided Gaussian tests have no blanket PRDS guarantee. See the companion examples for a discrete independent threshold comparison and a counterexample to the pairwise-correlation shortcut.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Benjamini–Yekutieli step-up procedure
Definition
Input an integer , observed values of p-value statistics (Valid p-values), and a target level . Let be the harmonic number from Harmonic numbers for set-cover analysis. For , set
Order the pairs lexicographically, so equal values are ordered by their original indices, and write the ordered values . Set
Reject exactly the hypotheses at ranks ; when , reject none. Because , each critical value satisfies . This definition specifies the procedure; no FDR guarantee is asserted here.
The unit interval with its Borel structure is standard Borel
Statement
Let with the subspace topology from the usual real topology and with its Borel sigma-algebra. Then is a standard-Borel space (Standard Borel spaces).
Facts & Assumptions
Given: The usual real metric and the subspace .
The absolute value makes a metric space: is a metric, its open balls are the intervals , and it is unbounded: the usual metric is and its open balls are .
The reals are complete: every Cauchy sequence in the usual real metric converges in .
is countably infinite: is countably infinite, so there is a bijection from to .
The rationals embed densely in the reals: between any two distinct real numbers lies a rational.
Every subset of an at most countable set is at most countable: every subset of an at most countable set is at most countable; countable means finite or bijective with (Finite, countably infinite, countable, uncountable, Equinumerous sets, and ).
The reals form a totally ordered field: the real order is total and translation preserves strict inequalities (Order on the reals, Order is preserved by adding a constant and by adding inequalities).
The Borel sigma-algebra of a topological space: the Borel sigma-algebra is generated by open sets and is closed under complements.
The Borel sigma-algebra of a subspace is the trace of the ambient Borel sigma-algebra: for a topological subspace, its Borel sigma-algebra is the trace of the ambient Borel sigma-algebra (Subspace topology: the traces of the open sets, its closed sets and its bases, the continuity of the inclusion, and the characteristic property of a map into a subspace).
Polish spaces are separable completely metrizable spaces: a space is Polish if it is separable and has a compatible complete metric; Standard Borel spaces identifies a measurable space with the Borel structure of a Polish presentation.
Open subset of (every point has a neighbourhood inside it), closed subset (complement open), and clopen: a subset of is closed when its complement is open.
The closed interval is (Intervals of : the nine order-convex forms, nondegeneracy, and length).
The topology on is the subspace topology inherited from (Subspace topology: the traces of the open sets, its closed sets and its bases, the continuity of the inclusion, and the characteristic property of a map into a subspace).
The metric on is the restriction (Isometry, isometric embedding, and the subspace metric on a subset).
Proof
Given: with the usual subspace topology and Borel sigma-algebra.
The interval is closed in : for , [F6] and [A1] give either or . If , set ; then every satisfies . If , set ; then every satisfies . By [F1] these are open neighborhoods disjoint from , so its complement is open by [F10]. Thus is Borel by [F7].
The set is at most countable: restrict the bijection from [F3] to , then apply [F5]. It is dense in . Indeed, for and , if choose . If , then ; take when , and otherwise. In either case and , with strict inequality in the second case. By [F4] choose a rational with ; then and . The order comparisons use [F6] and the interval endpoints use [A1].
Let be a Cauchy sequence in under . By [A2]–[A3], Cauchy distances are the same in and , so [F2] gives a real limit . If , step 1.1 gives an open ball about disjoint from , while convergence puts all sufficiently late in that ball, a contradiction. Hence and the sequence converges in the restricted metric; is complete.
By steps 1.2 and 2.1, is separable and completely metrizable, hence Polish by [F9]. Its Borel sigma-algebra is the trace of by [F8] and [A2]. The identity map from to the Polish subspace is therefore a bimeasurable bijection; [F9] makes standard Borel. This proves the claim without any choice axiom.
Leave-one-coordinate rejection partition for a step-up rule
Statement
Let , , and critical values . Give its finite product of the trace Borel sigma-algebras on . Define the step-up rule by
rejecting exactly ranks . Fix . Give the remaining-coordinate space its finite product of the trace Borel sigma-algebras, with the empty product on the singleton empty tuple when . Write the remaining values in increasing order as , and set . For , let
with an empty intersection interpreted as the whole remaining-coordinate space. Put . For , let if the rule rejects coordinate , and otherwise. Then:
- For every , the following identity of subsets of holds: In particular, every input in the right-hand side has exactly rejections and rejects .
- The sets are a disjoint Borel partition of .
- For , and is the whole space. Each is a coordinatewise increasing Borel set.
For , the remaining-coordinate space is the singleton empty tuple with its full sigma-algebra, and is the whole space.
Facts & Assumptions
Given: , a p-value vector, nondecreasing critical values , and the displayed step-up rule.
The Borel sigma-algebra of a topological space: closed half-lines and finite unions and intersections of their inverse images are Borel.
A measurable function between measurable spaces: a map is measurable when the preimage of each Borel set is measurable.
The product sigma-algebra and its finite iterates: the finite product sigma-algebra is generated by measurable rectangles, so coordinate cylinders with Borel bases are measurable.
Seven generating families for the Borel sigma-algebra on the real line: the closed left rays generate , since they are complements of the open right rays in the theorem's list.
Proof
For and , is the finite union over -element index sets of , since at least remaining values are at most exactly when the th ordered value is at most . Each coordinate cylinder is measurable in the stated finite product structure by [F1] and [F3], so these sublevel sets are Borel. Since the closed left rays generate by [F4], each order statistic is Borel by [F2]. The coordinate-deletion projection from to is measurable: preimages of measurable rectangles are finite intersections of coordinate cylinders, and the rectangles generate the product sigma-algebra by [F3]. Thus lifting a Borel set in the remaining-coordinate space gives a Borel set in the full product. If coordinatewise, at least coordinates of are at most ; their corresponding coordinates of are also at most , so . Every remaining-coordinate order statistic is therefore coordinatewise nondecreasing.
Fix and suppose . Rank passes exactly when at least other coordinates are at most , which is ; for this holds by . For each , since , rank passes exactly when at least other coordinates are at most , namely . Hence exactly when the first condition of holds and every higher rank fails, which is . If , is automatically among the first ranks. If and is not among the first ranks, then would make rank pass. Conversely, if and is rejected, then , rank passes and every higher rank fails, so the conditions defining hold.
For any remaining vector , replace coordinate by . Since , rank one passes. Every rank occupied by a zero-valued coordinate also passes because all critical values are positive, so includes all zero-valued coordinates and rejects the inserted coordinate . Thus the resulting rejection count is some . By step 2.1, ; hence the sets cover the space. If and , its condition at gives , while membership in would give . Thus the sets are disjoint. They are Borel by step 1.1 and their finite defining inequalities.
If for some , the defining inequalities include for every , so is contained in the displayed intersection. Conversely, suppose those inequalities hold and set . Rank one passes, and by the zero-coordinate argument in step 3.1 the inserted coordinate is rejected. No rank can pass: that would require , contrary to the assumed condition at . Thus the rejection count is some , and step 2.1 puts in . This proves the formula; when , step 3.1 gives equal to the whole space. The formula is a finite intersection of Borel order-statistic superlevel sets and is coordinatewise increasing by step 1.1.
Remarks
The result is choice-free. It applies to BH and BY after substituting their respective nondecreasing critical-value sequences.
Positive regression dependence on true-null coordinates (PRDS)
Definition
Let be a probability space, let , and let be a random vector whose coordinates are p-value statistics (Valid p-values). Fix a designated true-null index set . For , write for the law of under and define its support relative to by
Give each coordinate its trace Borel sigma-algebra (The Borel sigma-algebra of a topological space) and give the finite product sigma-algebra (The product sigma-algebra and its finite iterates). In this definition, “Borel set” means a member of that product sigma-algebra; thus “increasing measurable set” means coordinatewise increasing in this stated structure. The unit-interval lemma proves that with this trace Borel structure is standard Borel (The unit interval with its Borel structure is standard Borel). The finite-product result then makes with the specified product sigma-algebra a standard-Borel target (Standard Borel spaces, Finite products of standard Borel spaces are standard Borel); its finite-product proof uses only finite choice in ZF. Under the Axiom of Choice (The Axiom of Choice), this target has a regular conditional distribution given , and the conditional-law factorization result supplies a kernel representing the conditional law of given (Existence of regular conditional distributions for standard borel targets, Regular conditional kernels factor through a standard borel conditioning variable, Conditional law given a random element).
The law of is positive regression dependent on the true-null coordinates (PRDS on ) if, for each , such a conditional-law version can be chosen so that for every coordinatewise increasing Borel set , the function
is nondecreasing on . If , this condition is vacuous. The monotonicity condition is a full conditional-law requirement; pairwise correlations alone do not verify it.
Remarks
The regular conditional law is a chosen version. PRDS requires the monotonicity on the stated support, including support points where a conditional version is not determined by an atom of positive mass. The companion examples include a finite counterexample showing that valid p-values can have positive pairwise covariance without satisfying PRDS. The BY source's one-sided Gaussian sufficient conditions do not imply a general guarantee for two-sided Gaussian tests; the PRDS theorem below applies only after its exact conditional monotonicity premise is established.
BY controls FDR under arbitrary dependence
Statement
Let be a statistical experiment (Statistical experiments), let , and fix a finite family of null hypotheses . For each , let be a p-value statistic valid for in the sense of Null-valid p-values and familywise and false-discovery error rates. For , put
For every , the BY procedure in Benjamini–Yekutieli step-up procedure satisfies
No dependence condition on the joint law of the p-values is required.
Facts & Assumptions
Given: The experiment and fixed finite family in the Statement, , and p-value validity under each corresponding null law.
Null-valid p-values and familywise and false-discovery error rates: is the set of true-null indices, counts rejected true nulls, and .
Benjamini–Yekutieli step-up procedure: BY uses the critical values and rejects exactly the first ranks, where is the largest passing rank.
Leave-one-coordinate rejection partition for a step-up rule: for any nondecreasing step-up critical values, , and the sets partition the remaining-coordinate space.
Valid p-values yield level tests for nonempty disjoint alternatives: for a valid p-value and a nonempty null hypothesis, for every parameter in that null and every . Here means , so the null is nonempty and the supplier applies.
The expectation of an indicator is the probability of the event: for every event .
Linearity, monotonicity, and the modulus bound for expectation: expectation is linear on finite sums of integrable random variables.
Expectation of a nonnegative or integrable random variable: expectation is integration with respect to the specified probability law.
Each p-value is superuniform under every parameter in its corresponding null hypothesis.
Proof
Given: A fixed family of null hypotheses, p-values valid for their respective nulls, and .
Fix and let indicate that BY rejects hypothesis . Pointwise, : if both sides are zero, and if only contributes, with weight for each rejected true null. This finite sum is bounded and integrable by [F7]; [F5] and [F6] therefore give .
Fix and put and for . Since , that null is nonempty and [F4] at gives . Since BY has , [F3] splits each event into the disjoint events for , apart from that null zero event. Using for and that the partition the remaining coordinates, .
Set for . Then , by [F4], and . Finite summation by parts gives . Here , and .
Combining steps 1.2 and 2.1 bounds each true-null contribution in step 1.1 by . Summing over the true nulls gives ; with no true nulls the outer sum is empty and FDR is zero. No dependence condition is used, and the CDF summation-by-parts argument covers discrete superuniform p-values.
Remarks
For general superuniform p-values, a single interval can have probability greater than its width . The proof therefore bounds the weighted sum of interval probabilities by summation by parts of the superuniform CDF; it does not assume each interval has mass at most .
A PRDS bound for each BH true-null contribution
Statement
Let be p-value random variables in a probability space, let , , and fix an index in a designated true-null set . Suppose is valid under this law and the law of is PRDS on as defined in Positive regression dependence on true-null coordinates (PRDS). Apply BH at level , and let indicate that is rejected and be the total number rejected. Then
Facts & Assumptions
Given: A finite vector of p-values, a fixed true-null set containing , , validity of , and PRDS on that set. The PRDS conditional-law existence convention carries the Axiom of Choice as stated in Positive regression dependence on true-null coordinates (PRDS).
Positive regression dependence on true-null coordinates (PRDS): for each and each increasing Borel , there is a regular conditional-law version that is nondecreasing on the support of .
Leave-one-coordinate rejection partition for a step-up rule: for , the events are ; the sets partition the remaining-coordinate space, and are increasing Borel sets with the whole space.
Null-valid p-values and familywise and false-discovery error rates: validity means for every under the corresponding true-null law.
Conditional law given a random element: if is a conditional law of given , then for every Borel event , for Borel .
The expectation of an indicator is the probability of the event: an event indicator has expectation equal to its probability.
Linearity, monotonicity, and the modulus bound for expectation: expectation is linear on finite sums of integrable terms.
Expectation of a nonnegative or integrable random variable: expectation of a bounded measurable random variable is its integral under the law.
The usual topology of has interval neighborhoods, relatively open subsets of are traces of open subsets of , the rationals are dense and countable, products of two countable sets are countable, and measures are countably subadditive (The absolute value makes a metric space: is a metric, its open balls are the intervals , and it is unbounded, Subspace topology: the traces of the open sets, its closed sets and its bases, the continuity of the inclusion, and the characteristic property of a map into a subspace, The rationals embed densely in the reals, is countably infinite, A product of two at most countable sets is at most countable, Finite and countable subadditivity of measures).
Every nonempty at-most-countable set has a surjection from (A nonempty set is at most countable iff it is a surjective image of ).
The conditional-law version in [F1] has the monotonicity required by PRDS on the support of the marginal law of .
The standard-Borel conditional-law existence interface is used under the Axiom of Choice (The Axiom of Choice) as recorded in Positive regression dependence on true-null coordinates (PRDS).
Proof
Given: as in the Statement.
Put , , and . If , set ; otherwise set . By [F2], . The terms are bounded and the sum is finite, so [F5]–[F7] give ; a zero-probability contributes zero by the definition of .
For each , lift to . The coordinate-deletion map from to the remaining-coordinate product is measurable: the preimage of each measurable rectangle is a finite intersection of coordinate cylinders, and such rectangles generate the product sigma-algebra. Hence is Borel, and it is coordinatewise increasing. Let , nondecreasing on the support by [F1] and [A1]; the kernel exists on this standard-Borel interface under [A2].
Write . The family is a basis for the relative topology on . If is relative open and , write with open in by [F8]; choose with using [F8], then use rational density from [F8] to choose with . Thus . Since is nonempty and at most countable by [F8], [F9] gives an enumeration of it, whose interval images enumerate ; hence the basis is countable. Let if and , and otherwise. By the definition of support in Positive regression dependence on true-null coordinates (PRDS), Indeed, a point outside the support has a relative open null neighborhood and hence a basis neighborhood contained in it, which occurs in the enumeration; conversely, membership in any such null basis neighborhood excludes support membership. The right-hand side is a countable union of null Borel sets, so [F8] gives . Thus is Borel and .
By [F4], whenever , For fixed , this average is at most the corresponding average over . By step 1.3, both averages may be restricted to without changing them. Every supported is below every supported , so by [F1] and [A1]. The larger-cutoff average is a mixture of the first average and the average over the added interval; the latter is at least the first whenever it has positive mass, and if it has zero mass the two averages agree.
By [F3], for every . Substituting into step 1.1 yields .
Let be the first index with , if one exists; then for , and gives by step 2.1. For , suppose . Since and , step 2.1 applied to the nondecreasing makes this at most . Also , so [F4] gives . The disjoint union from [F2] then yields , completing the induction.
If exists, step 3.1 at and equal to the whole space give . If no such exists, all and every , so the same bound holds. Substituting into step 2.2 proves the contribution inequality.
Remarks
The algebraic contribution bound uses only the PRDS monotonicity for the finite family of sets . The inherited Axiom of Choice is used to formulate the standard-Borel conditional-law interface; no choice is used in the finite event decomposition or induction.
BH controls FDR under PRDS
Statement
Let be a statistical experiment, let , and fix a finite family of null hypotheses with p-value statistics valid for their respective nulls. For each , write
Assume the Axiom of Choice as required for the regular conditional-law interface in Positive regression dependence on true-null coordinates (PRDS). Suppose that, under every , the p-value vector is PRDS on . Then for every , the ordinary BH procedure in Benjamini–Hochberg step-up procedure satisfies
The PRDS assumption is imposed only on the true-null coordinates; no independence assumption is made.
Facts & Assumptions
Given: A finite family of valid p-values, , and under each the PRDS condition on the true-null set . The PRDS regular conditional-law convention uses the Axiom of Choice as recorded in Positive regression dependence on true-null coordinates (PRDS).
Null-valid p-values and familywise and false-discovery error rates: is the true-null index set, counts rejected true nulls, and .
Benjamini–Hochberg step-up procedure: BH takes the largest with and rejects exactly ranks .
Positive regression dependence on true-null coordinates (PRDS): for each , every increasing Borel event has a conditional-probability version nondecreasing in on its support.
A PRDS bound for each BH true-null contribution: under validity and PRDS on , each true-null contribution satisfies .
Linearity, monotonicity, and the modulus bound for expectation: expectation is linear on finite sums of integrable random variables.
Expectation of a nonnegative or integrable random variable: expectation is integration with respect to the specified law.
Every p-value is valid under its own null, so each meets the superuniformity hypothesis of [F4].
Under every , the vector of all p-values is PRDS on the full set , not merely pairwise positively correlated.
The standard-Borel regular conditional-law existence interface assumes the Axiom of Choice (The Axiom of Choice) as stated in Positive regression dependence on true-null coordinates (PRDS).
Proof
Given: The experiment, the fixed family and validity assumptions in the Statement, , and the PRDS condition for every true-null set.
Fix . Let be the indicator that the BH rule [F2] rejects hypothesis . By [F1], and . The ratio is bounded by one, so it is integrable by [F6]; finite linearity [F5] gives .
If , step 1.1 is an empty sum and FDR is zero. Otherwise, for each , [A1] gives validity and [A2] gives PRDS on a set containing under the AC-based conditional-law interface [A3], so [F4] bounds its contribution by . Summing step 1.1 gives . Only the designated true-null coordinates are needed; independence is not assumed.
Remarks
The Axiom of Choice enters through the standard-Borel regular conditional-law interface that gives the stated PRDS condition its kernel formulation. The finite sum of null contributions uses no additional choice. Pairwise covariance information alone does not supply the hypothesis; the companion examples include a valid-p-value witness.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Yoav Benjamini and Daniel Yekutieli, The Control of the False Discovery Rate in Multiple Testing under Dependency, The Annals of Statistics 29(4) (2001), Theorem 1.3 and Section 5
- Alexandru Katsevich, STAT 9610 Lecture Notes, Section 6.3.2, Proposition 6.6, printed p. 138 / PDF p. 142
- David Marker, Descriptive Set Theory, §1 Definition 1.1 and examples of Polish spaces, §2 Definition 2.2, PDF pp. 2 and 14
- Yoav Benjamini and Daniel Yekutieli, The Control of the False Discovery Rate in Multiple Testing under Dependency, The Annals of Statistics 29(4) (2001), Section 4, PDF pp. 13–16
- Yoav Benjamini, Discovering the False Discovery Rate, full 75-slide lecture, slides 31–35
- Yoav Benjamini and Daniel Yekutieli, The Control of the False Discovery Rate in Multiple Testing under Dependency, The Annals of Statistics 29(4) (2001), Section 2.2 and Theorem 1.2
- Yoav Benjamini, Discovering the False Discovery Rate, full 75-slide lecture, slides 33–35
- Yoav Benjamini and Daniel Yekutieli, The Control of the False Discovery Rate in Multiple Testing under Dependency, The Annals of Statistics 29(4) (2001), Theorem 1.3 and Section 5, PDF pp. 5 and 18–19
- Yoav Benjamini and Daniel Yekutieli, The Control of the False Discovery Rate in Multiple Testing under Dependency, The Annals of Statistics 29(4) (2001), Theorem 1.2 and Section 4, PDF pp. 4 and 13–16