Tightness of measures

From Wikipedia, the free encyclopedia

In mathematics, tightness is a concept in measure theory, the intuitive idea being that a given collection of measures does not "escape to infinity."

Contents

[edit] Definition

Let (X, T) be a topological space, and let Σ be a σ-algebra on X that contains the topology T. (Thus, every open subset of X is a measurable set and Σ is at least as fine as the Borel σ-algebra on X.) Let M be a collection of measures defined on Σ. The collection M is called tight if, for any η > 0, there is a compact subset Kη of X such that, for all measures μ in M,

\mu (X \setminus K_{\eta}) < \eta.

Very often, the measures in question are probability measures, so the last part can be written as

\mu (K_{\eta}) > 1 - \eta. \,

If a tight collection M consists of a single measure μ, then (depending upon the author) μ may either be said to be a tight measure or to be an inner regular measure.

[edit] Examples

[edit] Compact spaces

If X is a compact space, then every collection of probability measures on X is tight.

[edit] A collection of point masses

Consider the real line R with its usual Borel topology. Let δx denote the Dirac measure, a unit mass at the point x in R. The collection

M_{1} := \{ \delta_{n} | n \in \mathbb{N} \}

is not tight, since the compact subsets of R are precisely the closed and bounded subsets, and any such set, since it is bounded, has δn-measure zero for large enough n. On the other hand, the collection

M_{2} := \{ \delta_{1 / n} | n \in \mathbb{N} \}

is tight: the compact interval [0, 1] will work as Kη for any η > 0. In general, a collection of Dirac delta measures on Rn is tight if, and only if, the collection of their supports is bounded.

[edit] A collection of Gaussian measures

Consider n-dimensional Euclidean space Rn with its usual Borel topology and σ-algebra. Consider a collection of Gaussian measures

\Gamma = \{ \gamma_{i} | i \in I \},

where the measure γi has expected value (mean) μi in Rn and variance σi2 > 0. Then the collection Γ is tight if, and only if, the collections \{ \mu_{i} | i \in I \} \subseteq \mathbb{R}^{n} and \{ \sigma_{i}^{2} | i \in I \} \subseteq \mathbb{R} are both bounded.

[edit] Tightness and convergence

Tightness is often a necessary criterion for proving the weak convergence of a sequence of probability measures, especially when the measure space has infinite dimension. See

[edit] Exponential tightness

A generalization of tightness is the concept of exponential tightness, which has applications in large deviations theory. A family of probability measures (μδ)δ>0 on a Hausdorff topological space X is said to be exponentially tight if, for any η > 0, there is a compact subset Kη of X such that

\limsup_{\delta \downarrow 0} \delta \log \mu_{\delta} (X \setminus K_{\eta}) < - \eta.

[edit] References

  • Billingsley, Patrick (1995). Probability and Measure. New York, NY: John Wiley & Sons, Inc.. ISBN 0-471-00710-2. 
  • Billingsley, Patrick (1999). Convergence of Probability Measures. New York, NY: John Wiley & Sons, Inc.. ISBN 0-471-19745-9.