Cramér–Wold theorem

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Short description: Mathematical theorem in measure theory

In mathematics, the Cramér–Wold theorem[1][2] or the Cramér–Wold device[3][4] is a theorem in measure theory and which states that a Borel probability measure on k is uniquely determined by the totality of its one-dimensional projections.[5][6][7] It is used as a method for proving joint convergence results. The theorem is named after Harald Cramér and Herman Ole Andreas Wold, who published the result in 1936.[8]

Let

Xn=(Xn1,,Xnk)

and

X=(X1,,Xk)

be random vectors of dimension k. Then Xn converges in distribution to X if and only if:

i=1ktiXninDi=1ktiXi.

for each (t1,,tk)k, that is, if every fixed linear combination of the coordinates of Xn converges in distribution to the correspondent linear combination of coordinates of X.[9]

If Xn takes values in +k, then the statement is also true with (t1,,tk)+k.[10]

References

  1. Samanta, M. (1989-04-01). "Non-parametric estimation of conditional quantiles". Statistics & Probability Letters 7 (5): 407–412. doi:10.1016/0167-7152(89)90095-3. ISSN 0167-7152. https://linkinghub.elsevier.com/retrieve/pii/0167715289900953. 
  2. Cuesta-Albertos, Juan Antonio; Fraiman, Ricardo; Ransford, Thomas (2007). "A Sharp Form of the Cramér–Wold Theorem" (in en). Journal of Theoretical Probability 20 (2): 201–209. doi:10.1007/s10959-007-0060-7. ISSN 0894-9840. https://link.springer.com/10.1007/s10959-007-0060-7. 
  3. Mueller, Jonas W; Jaakkola, Tommi (2015). "Principal Differences Analysis: Interpretable Characterization of Differences between Distributions". Advances in Neural Information Processing Systems (Curran Associates, Inc.) 28. https://proceedings.neurips.cc/paper/2015/hash/83fa5a432ae55c253d0e60dbfa716723-Abstract.html. 
  4. Berger, David; Lindner, Alexander (2022-05-01). "A Cramér–Wold device for infinite divisibility of Zd-valued distributions". Bernoulli 28 (2). doi:10.3150/21-BEJ1386. ISSN 1350-7265. https://projecteuclid.org/journals/bernoulli/volume-28/issue-2/A-Cram%c3%a9rWold-device-for-infinite-divisibility-of-Zd-valued-distributions/10.3150/21-BEJ1386.full. 
  5. "Cramér-Wold theorem". https://planetmath.org/cramerwoldtheorem. 
  6. Billingsley, Patrick (1995). Probability and Measure (3 ed.). John Wiley & Sons. ISBN 978-0-471-00710-4. 
  7. Bélisle, Claude; Massé, Jean-Claude; Ransford, Thomas (1997). "When is a probability measure determined by infinitely many projections?". The Annals of Probability 25 (2). doi:10.1214/aop/1024404418. ISSN 0091-1798. https://projecteuclid.org/journals/annals-of-probability/volume-25/issue-2/When-is-a-probability-measure-determined-by-infinitely-many-projections/10.1214/aop/1024404418.full. 
  8. Cramér, H.; Wold, H. (1936). "Some Theorems on Distribution Functions" (in en). Journal of the London Mathematical Society s1-11 (4): 290–294. doi:10.1112/jlms/s1-11.4.290. http://doi.wiley.com/10.1112/jlms/s1-11.4.290. 
  9. Billingsley 1995, p. 383
  10. Kallenberg, Olav (2002). Foundations of modern probability (2nd ed.). New York: Springer. ISBN 0-387-94957-7. OCLC 46937587.