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Scandinavian Actuarial Journal, 1944
If X and Y are mutually independent random variables whith the d. f. 1 F 1(χ) and F 2(χ), it is known 2 that the sum X + Y has the d. f. F 2(χ), defined as the convolution where the integrals are Lebesgue-Stiltjes integrals. One uses the abbreviation More generally the sum X 1 + X 2 + … + X n of n mutually independent random variables with the d. f.
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If X and Y are mutually independent random variables whith the d. f. 1 F 1(χ) and F 2(χ), it is known 2 that the sum X + Y has the d. f. F 2(χ), defined as the convolution where the integrals are Lebesgue-Stiltjes integrals. One uses the abbreviation More generally the sum X 1 + X 2 + … + X n of n mutually independent random variables with the d. f.
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2012
The law of large numbers states that the arithmetic mean of independent, identically distributed random variables converges to the expected value. One interpretation of the central limit theorem is as a (distributional) rate result.
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The law of large numbers states that the arithmetic mean of independent, identically distributed random variables converges to the expected value. One interpretation of the central limit theorem is as a (distributional) rate result.
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On the central limit theorems.
1971This item was digitized as part of a project to share McGill's intellectual legacy with the public. If you are the copyright holder or a relative of the copyright holder who is deceased, you may request withdrawal by emailing escholarship.library@mcgill.ca.
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2003
The Central Limit Theorem is one of the classical results in statistics with many applications in actuarial mathematics, finance, and risk management and a host of other not necessarily economic disciplines. In Chapter 14 we will apply it to the derivation of the Black—Scholes formula from the binomial Cox—Ross—Rubinstein model.
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The Central Limit Theorem is one of the classical results in statistics with many applications in actuarial mathematics, finance, and risk management and a host of other not necessarily economic disciplines. In Chapter 14 we will apply it to the derivation of the Black—Scholes formula from the binomial Cox—Ross—Rubinstein model.
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On the Multidimensional Central Limit Theorem
Theory of Probability & Its Applications, 1968openaire +1 more source
A general central limit theorem under sublinear expectations
Science China Mathematics, 2010Yufeng Shi
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