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Bounded stationary CLT under summable strong mixing
Statement
Assume AC. Let be a centered strictly stationary real sequence, with almost surely for a finite , and suppose Put . Then its covariance series converges absolutely and Moreover including . Writing , every finite subset satisfies The strong-mixing coefficient is that of Strong, uniform, and maximal-correlation mixing coefficients.
Facts & Assumptions
Given: AC, the centered strictly stationary bounded sequence, and summability of its strong-mixing coefficients.
AC is assumed for the characteristic-function convergence criterion and the normal-law supplier. (The Axiom of Choice)
Strict stationarity preserves finite-dimensional laws under nonnegative shifts, and the mixing coefficient is the supremum over separated past and future events. (Strong, uniform, and maximal-correlation mixing coefficients)
Bounded real variables from fields with event coefficient have covariance at most in absolute value; complex variables obey the analogous bound . (Strong-mixing covariance bound in terms of tail quantiles)
Variance and covariance are centered product expectations. (Moments, variance, and covariance on a probability space)
Dominated convergence applies on an arbitrary measure space, including counting measure with a summable majorant. (Dominated convergence)
Cauchy–Schwarz bounds absolute product expectations of square-integrable real variables. (Cauchy-Schwarz for random variables)
For , one has for all real . (Second-order characteristic-function expansion)
Under AC the characteristic function of is , including . (Characteristic function of a normal law)
Under AC, convergence of characteristic functions to that of a specified probability law is equivalent to weak convergence to that law. (Characteristic function criterion for weak convergence)
Proof
Put . By [F1]–[F2], for , so . Expanding the square of a sum indexed by a finite subset gives its diagonal terms and off-diagonal terms for pairs in . For each fixed positive lag there are at most such pairs. Thus the asserted bound holds, also for . For a consecutive block, the same expansion gives . The summable majorant and [F4] imply convergence to the stated , which is nonnegative as a limit of variances.
Extend the coefficient convention by . It is nonincreasing: increasing the separation restricts the future sigma-algebra in [F1]. For ordered indices , write and . If , centering of and [F2] applied to and give ; if , the trivial bound suffices. Splitting off the centered last factor gives the same bound with . Therefore . There are at most orderings, at most choices each for the first index and the middle gap, and pairs with maximum . Hence To justify the last limit, for each fixed , , whereas as ; the unweighted part divided by also tends to zero. Repeated indices are included by the convention at zero.
Set and . By step 1.2, . For sufficiently large , define , interpreting the second entry as if , and ignore the finitely many earlier . Then , , and . Put , , and . Thus , , , and . Retain consecutive blocks of length , separated by discarded blocks of length , and discard the final remainder. Their discarded index set has cardinality at most . Step 1.1 gives , controlling all gaps together without asserting their independence.
Summability and monotonicity imply , since . For large , , so . Let denote the sum in retained block . At a fixed real frequency , put . Both each factor and every partial product have modulus at most one. The partial product through block is measurable before the gap preceding block , so [F2] bounds the discrepancy in factoring its expectation from by . Telescoping these factorizations gives This compares characteristic functions only and requires no independent copies or coupling construction.
Write . These variables are centered and each has the law of by stationarity. Steps 1.1 and 2.1 imply . Also because and . For every , the Lindeberg sum of these block marginals is at most . Applying [F5] first to and then the finite-sum Cauchy–Schwarz inequality yields . Finally . No uniform-integrability hypothesis or deterministic block bound tending to zero has been assumed.
For fixed , [F6] gives , where and . Step 3.2 gives and . The elementary real Taylor bound for follows by integrating the second derivative twice. Since both and are at most one, the product telescoping inequality now gives Thus the product characteristic function tends to .
The discarded normalized sum in step 2.1 tends to zero in and hence in by [F5]. The elementary bound , obtained by integrating the derivative of along the real interval, makes its effect on each characteristic function tend to zero. Combine steps 3.1 and 4.1: for every real . Under [A1], [F7] identifies this with the characteristic function of , and [F8] gives the asserted weak convergence. This remains valid for ; all finite-moment and discarded-set assertions were already proved in step 1.1.
Depends on
- The Axiom of Choice
- Strong, uniform, and maximal-correlation mixing coefficients
- Moments, variance, and covariance on a probability space
- Strong-mixing covariance bound in terms of tail quantiles
- Cauchy-Schwarz for random variables
- Dominated convergence
- Second-order characteristic-function expansion
- Characteristic function of a normal law
- Characteristic function criterion for weak convergence
Used by
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Sources
- Galin L. Jones, On the Markov Chain Central Limit Theorem, Theorem 5(1) (standard reference, not scraped)
- P. Doukhan, P. Massart and E. Rio, The functional central limit theorem for strongly mixing processes, bounded-variable application (standard reference, not scraped)