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3.8 The Cauchy Distribution: 𝖢𝖺𝗎𝖼𝗁𝗒

  1. fX⁢(x)=1π⁢(1+x2) for -∞<x<∞,

  2. FX⁢(x)=1π⁢arctan⁡(x)+12,

  3. 𝖤⁡[X] not defined.

We write X∼𝖢𝖺𝗎𝖼𝗁𝗒.

Unnumbered Figure: Link

Now, for b>0,

∫0btπ⁢(1+t2)⁢dx=12⁢π⁢[log⁡(1+t2)]0b=12⁢π⁢log⁡(1+b2)

and similarly, for a<0, ∫a0xπ⁢(1+x2)⁢𝑑x=-log⁡(1+a2). Thus

∫-∞∞tπ⁢(1+t2)⁢dt=lima→-∞,b→∞⁡∫abtπ⁢(1+t2)⁢dt=lima→-∞,b→∞⁡(log⁡(1+b2)-log⁡(1+a2)),

which is not defined since it can take any desired value depending on the relative speeds with which a→-∞ and b→∞.

Convolution: If X1,…,Xn are independent Cauchy rvs, then (X1+…+Xn)/n∼𝖢𝖺𝗎𝖼𝗁𝗒.

Transformations: If U∼𝖴𝗇𝗂𝖿⁡(-π/2,π/2), then tan⁡(U)∼𝖢𝖺𝗎𝖼𝗁𝗒. If X1∼𝖭⁡(0,1) and X2∼𝖭⁡(0,1) are independent, then X1/X2∼𝖢𝖺𝗎𝖼𝗁𝗒.

Reciprocal: if X∼𝖢𝖺𝗎𝖼𝗁𝗒 then 1/X∼𝖢𝖺𝗎𝖼𝗁𝗒. Quiz: How do we know this, given the above? When X1 and X2 are both 𝖭⁡(0,1), X1/X2 and X2/X1 must have the same distribution, by symmetry.