Continuous mapping theorem: Difference between revisions

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{{Short description|Probability theorem}}

{{Distinguish|text=the [[contraction mapping theorem]]}}

In [[probability theory]], the '''continuous mapping theorem''' states that continuous functions [[Continuous function#Heine definition of continuity|preserve limits]] even if their arguments are sequences of random variables. A continuous function, in [[Continuous function#Heine definition of continuity|Heine’sHeine's definition]], is such a function that maps convergent sequences into convergent sequences: if ''x_n'' → ''x'' then ''g''(''x_n'') → ''g''(''x''). The ''continuous mapping theorem'' states that this will also be true if we replace the deterministic sequence {''x_n''} with a sequence of random variables {''X_n''}, and replace the standard notion of convergence of real numbers “→” with one of the types of [[convergence of random variables]].

This theorem was first proved by [[Henry Mann]] and [[Abraham Wald]] in 1943,<ref>{{cite journal | doi = 10.1214/aoms/1177731415 | last1 = Mann |first1=H. B. | last2=Wald |first2=A. | year = 1943 | title = On Stochastic Limit and Order Relationships | journal = [[Annals of Mathematical Statistics]] | volume = 14 | issue = 3 | pages = 217–226 | jstor = 2235800 | doi-access = free }}</ref> and it is therefore sometimes called the '''Mann–Wald theorem'''.<ref>{{cite book | last = Amemiya | first = Takeshi | author-link = Takeshi Amemiya | year = 1985 | title = Advanced Econometrics | publisher = Harvard University Press | ___location = Cambridge, MA | isbn = 0-674-00560-0 | url = https://books.google.com/books?id=0bzGQE14CwEC&pg=pA88 |page=88 }}</ref> Meanwhile, [[Denis Sargan]] refers to it as the '''general transformation theorem'''.<ref>{{cite book |first=Denis |last=Sargan |title=Lectures on Advanced Econometric Theory |___location=Oxford |publisher=Basil Blackwell |year=1988 |isbn=0-631-14956-2 |pages=4–8 }}</ref>

This theorem was first proved by {{harvtxt|Mann|Wald|1943}}, and it is therefore sometimes called the '''Mann–Wald theorem'''.<ref>{{harvnb|Amemiya|1985|page=88}}</ref>

==Statement==

Let {''X_n''}, ''X'' be [[random element]]s defined on a [[metric space]] ''S''. Suppose a function {{nowrap|''g'': ''S''→''S′''}} (where ''S′'' is another metric space) has the set of [[Discontinuity (mathematics)|discontinuity points]] ''D_g'' such that {{nowrap|1=Pr[''X'' ∈ ''D_g''] = 0}}. Then<ref>{{harvnbcite book |Van derlast = Billingsley Vaart|1998 first = Patrick |loc author-link =Theorem 2.3,Patrick pageBillingsley 7}}</ref><ref>{{harvnb|Billingsley title = Convergence of Probability Measures | year = 1969 | publisher = John Wiley & Sons | isbn = 0-471-07242-7|page=31, (Corollary 1) }}</ref><ref>{{harvnbcite book |Billingsley last = van der Vaart |1999 first = A. W. | title = Asymptotic Statistics | year = 1998 | publisher = Cambridge University Press | ___location = New York | isbn = 0-521-49603-9 | url =https://books.google.com/books?id=UEuQEM5RjWgC&pg=PA7 |page=21,7 (Theorem 2.73) }}</ref>

: <math>

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===Convergence in distribution===

We will need a particular statement from the [[portmanteau theorem]]: that convergence in distribution <math>X_n\xrightarrow{d}X</math> is equivalent to

: <math> \mathbb E f(X_n) \to \mathbb E f(X)</math> for every bounded continuous functional ''f''.

So it suffices to prove that <math> \mathbb E f(g(X_n)) \to \mathbb E f(g(X))</math> for every bounded continuous functional ''f''. Note that <math> F = f \circ g</math> is itself a bounded continuous functional. And so the claim follows from the statement above.

So it suffices to prove that <math> \mathbb E f(g(X_n)) \to \mathbb E f(g(X))</math> for every bounded continuous functional ''f''. For simplicity we assume ''g'' continuous. Note that <math> F = f \circ g</math> is itself a bounded continuous functional. And so the claim follows from the statement above. The general case is slightly more technical.

===Convergence in probability===

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==See also==

* [[Slutsky’sSlutsky's theorem]]

* [[Portmanteau theorem]]

* [[Pushforward measure]]

==References==

{{reflist}}

[[Category:Theorems in probability theory]]

==Further reading==

[[Category:Theorems in statistics]]

* {{cite book

| last = Amemiya

| first = Takeshi

| authorlink = Takeshi Amemiya

| year = 1985

| title = Advanced Econometrics

| publisher = Harvard University Press

| ___location = Cambridge, MA

| isbn = 0-674-00560-0

| url = https://books.google.com/books?id=0bzGQE14CwEC

| ref = harv

}}

* {{cite book

| last = Billingsley

| first = Patrick

| authorlink = Patrick Billingsley

| title = Convergence of Probability Measures

| year = 1969

| publisher = John Wiley & Sons

| isbn = 0-471-07242-7| ref = harv

}}

* {{cite book

| last = Billingsley

| first = Patrick

| title = Convergence of Probability Measures

| year = 1999

| publisher = John Wiley & Sons

| edition = 2nd

| isbn = 0-471-19745-9

| url = https://books.google.com/books?id=QY06uAAACAAJ

| ref = harv

}}

* {{cite journal

| doi = 10.1214/aoms/1177731415

| last = Mann |first=H. B.

| authorlink = Henry Mann

| last2=Wald |first2=A.

| authorlink2 = Abraham Wald

| year = 1943

| title = On Stochastic Limit and Order Relationships

| journal = [[Annals of Mathematical Statistics]]

| volume = 14

| issue = 3

| pages = 217–226

| jstor = 2235800

| ref = CITEREFMannWald1943

}}

* {{cite book

| last = Van der Vaart

| first = A. W.

| title = Asymptotic statistics

| year = 1998

| publisher = Cambridge University Press

| ___location = New York

| isbn = 0-521-49603-9

| url = https://books.google.com/books?id=UEuQEM5RjWgC

| ref = CITEREFVan_der_Vaart1998

}}

[[Category:Probability theorems]]

[[Category:Statistical theorems]]

[[Category:Articles containing proofs]]