Convergence in measure

Convergence in measure is either of two distinct mathematical concepts both of which generalize the concept of convergence in probability.

DefinitionsEdit

Let   be measurable functions on a measure space  . The sequence   is said to converge globally in measure to   if for every  ,

 ,

and to converge locally in measure to   if for every   and every   with  ,

 .

On a finite measure space, both notions are equivalent. Otherwise, convergence in measure can refer to either global convergence in measure or local convergence in measure, depending on the author.

PropertiesEdit

Throughout, f and fn (n   N) are measurable functions XR.

  • Global convergence in measure implies local convergence in measure. The converse, however, is false; i.e., local convergence in measure is strictly weaker than global convergence in measure, in general.
  • If, however,   or, more generally, if f and all the fn vanish outside some set of finite measure, then the distinction between local and global convergence in measure disappears.
  • If μ is σ-finite and (fn) converges (locally or globally) to f in measure, there is a subsequence converging to f almost everywhere. The assumption of σ-finiteness is not necessary in the case of global convergence in measure.
  • If μ is σ-finite, (fn) converges to f locally in measure if and only if every subsequence has in turn a subsequence that converges to f almost everywhere.
  • In particular, if (fn) converges to f almost everywhere, then (fn) converges to f locally in measure. The converse is false.
  • Fatou's lemma and the monotone convergence theorem hold if almost everywhere convergence is replaced by (local or global) convergence in measure.
  • If μ is σ-finite, Lebesgue's dominated convergence theorem also holds if almost everywhere convergence is replaced by (local or global) convergence in measure.
  • If X = [a,b] ⊆ R and μ is Lebesgue measure, there are sequences (gn) of step functions and (hn) of continuous functions converging globally in measure to f.
  • If f and fn (nN) are in Lp(μ) for some p > 0 and (fn) converges to f in the p-norm, then (fn) converges to f globally in measure. The converse is false.
  • If fn converges to f in measure and gn converges to g in measure then fn + gn converges to f + g in measure. Additionally, if the measure space is finite, fngn also converges to fg.

CounterexamplesEdit

Let  , μ be Lebesgue measure, and f the constant function with value zero.

  • The sequence   converges to f locally in measure, but does not converge to f globally in measure.
  • The sequence   where   and   (The first five terms of which are  ) converges to 0 globally in measure; but for no x does fn(x) converge to zero. Hence (fn) fails to converge to f almost everywhere.
  • The sequence   converges to f almost everywhere and globally in measure, but not in the p-norm for any  .

TopologyEdit

There is a topology, called the topology of (local) convergence in measure, on the collection of measurable functions from X such that local convergence in measure corresponds to convergence on that topology. This topology is defined by the family of pseudometrics

 

where

 .

In general, one may restrict oneself to some subfamily of sets F (instead of all possible subsets of finite measure). It suffices that for each   of finite measure and   there exists F in the family such that   When  , we may consider only one metric  , so the topology of convergence in finite measure is metrizable. If   is an arbitrary measure finite or not, then

 

still defines a metric that generates the global convergence in measure.[1]

Because this topology is generated by a family of pseudometrics, it is uniformizable. Working with uniform structures instead of topologies allows us to formulate uniform properties such as Cauchyness.

See alsoEdit

ReferencesEdit

  1. ^ Vladimir I. Bogachev, Measure Theory Vol. I, Springer Science & Business Media, 2007
  • D.H. Fremlin, 2000. Measure Theory. Torres Fremlin.
  • H.L. Royden, 1988. Real Analysis. Prentice Hall.
  • G. B. Folland 1999, Section 2.4. Real Analysis. John Wiley & Sons.