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A Gaussian tail bound implies a sub-Gaussian MGF bound
Statement
Let be a centered real random variable and let . If then is sub-Gaussian with proxy in the MGF convention of Sub-Gaussian variables and vectors, and sub-exponential variables:
Facts & Assumptions
Given: A centered real random variable with the displayed two-sided tail bound and a constant .
Layer cake gives for nonnegative (Layer-cake formulas for random variables).
A product of two probability laws exists and has the rectangle formula (For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique).
Expectations of products of independent real random variables factor (Expectations factor over finite products of independent random variables).
Jensen's inequality gives (Jensen's inequality for expectation).
Increasing nonnegative functions pass to the limit under expectation (Monotone convergence for the integral).
A centered variable is sub-Gaussian with proxy when for all real (Sub-Gaussian variables and vectors, and sub-exponential variables).
Proof
The tail bound and [F1] imply for every , since completing the square gives . Thus all MGFs below are finite.
Let be the coordinate variables on the product of two copies of the law of ; [F2] gives this probability space, and its rectangle formula makes them independent with law equal to that of . Set . Independence [F3] gives . By [F4], the second factor is at least , so . The product is unchanged when is replaced by , hence .
A union bound gives because implies or . For , [F1] and the substitution yield The integral is evaluated using .
By [F5], the nonnegative power series for may be integrated term by term. With , step 2.1 and give For the last inequality, satisfies and for . Therefore for every , which is the proxy- assertion by [F6].
Depends on
- Layer-cake formulas for random variables
- Sub-Gaussian variables and vectors, and sub-exponential variables
- Expectations factor over finite products of independent random variables
- Jensen's inequality for expectation
- Monotone convergence for the integral
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
Used by
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Sources
- T. Tony Cai and Harrison H. Zhou, Optimal Rates of Convergence for Sparse Covariance Matrix Estimation, equation (2) (standard reference, not scraped)
- Philippe Rigollet and Jan-Christian Hütter, High-Dimensional Statistics: Lecture Notes, Chapter 1, §1.2 (standard reference, not scraped)