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Bounded influence can coexist with vanishing replacement breakdown
Statement refuted
At a probability law , finite gross-error sensitivity forces the empirical plug-in statistic to have replacement breakdown bounded below by a positive constant as .
Facts & Assumptions
Given: Countable Choice, the Borel probability law on , and the functional and statistics defined in the Counterexample below. Here is one-dimensional Lebesgue measure.
The Borel sigma-algebra of a topological space: is the sigma-algebra generated by the open subsets of .
Probability measures and probability spaces: a probability measure is a countably additive measure of total mass one.
Lebesgue measurable sets, the family , and the restricted set function : is the Lebesgue measure set function on the Lebesgue measurable subsets of .
Assuming countable choice, is a sigma-algebra containing every elementary set and is a complete measure extending elementary volume: under Countable Choice, Lebesgue measurable sets form a sigma-algebra and is a complete measure on it.
A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included: under Countable Choice, every interval is Borel/Lebesgue measurable with , including degenerate intervals.
The Axiom of Countable Choice (): Countable Choice is the hypothesis of [F4], [F5], and [F14], supplying the measure, interval-volume, and Borel-measurability properties of used to construct .
The lower median of a real probability law: is the lower median of a law in its domain.
A finite empirical measure is a probability law: is a probability measure for every finite sample.
Statistical functionals and gross-error influence functions: the gross-error influence function is the right derivative along a point-contamination path.
Gross-error contamination path: is the point-contamination path.
Gross-error sensitivity of a functional: sensitivity is the supremum of absolute influence values over the directions where they exist.
Finite-sample replacement breakdown: the breakdown fraction is the first replacement count at which the maximum displacement is unbounded, divided by .
Influence function and sensitivity of the median: at a continuous positive density at the median, the median's right influence is below , above , and at .
The lower median of an odd empirical sample is its middle order statistic: for an odd empirical sample, its lower median is the middle order statistic.
Under Countable Choice, every Borel subset of is Lebesgue measurable (Assuming countable choice, every Borel subset of is Lebesgue measurable).
Counterexample
Let be the Borel probability laws on for which for some finite . Then for and for , so [F6] defines . For , call an atom location when . Define
The supremum is finite in the first case because every atom location lies in an interval carrying . For a sample , let be its empirical law from [F7] and set .
By [F14], every Borel set is Lebesgue measurable; since [F5] makes Borel and Lebesgue measurable with measure , each is measurable for Borel . For pairwise disjoint Borel sets , these intersections are disjoint, so countable additivity of gives countable additivity of . Also because , and by [F5]. Thus [F1] and [F2] make a Borel probability law. For , [F5] gives ; for the intersection is empty. It follows that , the density near zero is , and has no atoms, the latter by the degenerate-interval case of [F5].
Fix any point . By [F9], is carried by a bounded interval containing and , and its only possible atom location is , because [F5] gives . The trigger in requires two distinct atom locations, so it is false and for all sufficiently small . Therefore [F8] and [F12] give , equal to for , for , and for .
Fix odd and a clean sample . Its empirical median is the middle order statistic by [F13] and lies in , so every atom location of is within distance of that median. The trigger is false and is the sample median.
After at most one replacement, at least observations remain in . At least resulting observations are at most and at least are at least , so the middle order statistic, hence the lower empirical median by [F13], remains in . All unchanged atom locations are within distance of it, and only the single replacement can add a distinct outlying atom. Thus the trigger is still false and remains the sample median in for every one-coordinate replacement; its maximum displacement is bounded.
The absolute values in step 1.2 have supremum over , so [F10] gives .
Replace two coordinates by distinct values and with . The remaining observations lie in , so the middle order statistic (position ) is still one of them and lies in ; by [F13] this is the lower empirical median. Both new atom locations are more than above that median, so the trigger is true and . Letting makes the maximum displacement unbounded. Since step 1.4 ruled out one replacement and [F11] defines the first unbounded count, the breakdown fraction is exactly .
Along odd , , while step 2.1 gives finite gross-error sensitivity at the fixed law . Hence this functional has bounded influence and an empirical plug-in statistic whose replacement breakdown is not bounded away from zero, refuting the Statement.
Depends on
- The Borel sigma-algebra of a topological space
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Finite-sample replacement breakdown
- Gross-error sensitivity of a functional
- Gross-error contamination path
- The lower median of a real probability law
- Lebesgue measurable sets, the family $\mathcal{L}(\mathbb{R}^n)$, and the restricted set function $\lambda_n$
- Probability measures and probability spaces
- Statistical functionals and gross-error influence functions
- The lower median of an odd empirical sample is its middle order statistic
- A finite empirical measure is a probability law
- Assuming countable choice, $\mathcal{L}(\mathbb{R}^n)$ is a sigma-algebra containing every elementary set and $\lambda_n$ is a complete measure extending elementary volume
- Assuming countable choice, every Borel subset of $\mathbb{R}^n$ is Lebesgue measurable
- A box in $\mathbb{R}^n$ with parameters $a_i\le b_i$ is Lebesgue measurable of measure $\prod_{i<n}(b_i-a_i)$, whichever of its faces are included
- Influence function and sensitivity of the median
Used by
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Sources
- P. B. Stark, Statistics 240 Lecture Notes, Part 9: Robustness and Related Topics, §§1.2–1.3, PDF pp. 2–3, 13–18 (standard reference, not scraped)
- Elvezio Ronchetti, Introduction to Robust Statistics, slides 15–23 (standard reference, not scraped)