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.
Products of variables with Gaussian tails are sub-exponential
Statement
Let be real random variables on one probability space, not necessarily independent, and suppose for some that Then is well-defined, centered, and sub-exponential with parameters in the convention of Sub-Gaussian variables and vectors, and sub-exponential variables.
Facts & Assumptions
Given: Real random variables with the displayed directional tail bounds and .
Layer cake gives for nonnegative (Layer-cake formulas for random variables).
Sub-exponentiality with parameters means for (Sub-Gaussian variables and vectors, and sub-exponential variables).
Increasing nonnegative measurable partial sums pass to their pointwise limit under expectation. (Monotone convergence for the integral)
Measurable partial sums dominated in absolute value by one integrable random variable converge in and in expectation to their pointwise limit. (Dominated convergence)
Expectation is linear on finite sums of integrable random variables. (Linearity, monotonicity, and the modulus bound for expectation)
Proof
Applying [F1] to gives . Since , is integrable and . Also, for , implies or , so .
Put . If , then implies , so step 1.1 gives . If , then , so the same final bound follows from . Thus for all , .
By [F1] and step 2.1, for every integer , The identity follows from substitution and .
Fix and put . The variable is integrable and centered by step 1.1. The nonnegative measurable partial sums increase pointwise to . Finite linearity [F5] and monotone convergence [F3], applied only to these nonnegative partial sums, give Thus is an integrable dominating variable.
The signed partial sums converge pointwise to and satisfy . Dominated convergence [F4] and finite linearity [F5] justify taking expectations termwise; the scalar series of expectations is absolutely convergent by step 4.1. Since , step 3.1 gives By [F2] the parameters remain and .
Depends on
Used by
Dependency tree · two levels
33 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- T. Tony Cai and Harrison H. Zhou, Optimal Rates of Convergence for Sparse Covariance Matrix Estimation, Section 4, equation (26) (standard reference, not scraped)
- Philippe Rigollet and Jan-Christian Hütter, High-Dimensional Statistics: Lecture Notes, Chapter 1, Lemma 1.12 (standard reference, not scraped)