
<p>DISTRIBUTIONS DERIVED FROM THE NORMAL DISTRIBUTION </p><p>Definition: A random variable X with pdf </p><p>λ<sup style="top: -0.3615em;">α </sup></p><p>g(x) = </p><p>x<sup style="top: -0.4113em;">α−1</sup>e<sup style="top: -0.4113em;">−λx </sup></p><p>x ≥ 0 </p><p>Γ(α) has gamma distribution with parameters α > 0 and λ > 0. The gamma function Γ(x), is defined as </p><p>Z</p><p>∞</p><p>Γ(x) = </p><p>u<sup style="top: -0.4113em;">x−1</sup>e<sup style="top: -0.4113em;">−u</sup>du. </p><p>0</p><p>Properties of the Gamma Function: </p><p>(i) Γ(x + 1) = xΓ(x) (ii) Γ(n + 1) = n! </p><p>√</p><p>(iii) Γ(1/2) = π. </p><p>Remarks: </p><p>1. Notice that an exponential rv with parameter 1/θ = λ is a special case of a gamma rv. with parameters α = 1 and λ. <br>2. The sum of n independent identically distributed (iid) exponential rv, with parameter λ has a gamma distribution, with parameters n and λ. <br>3. The sum of n iid gamma rv with parameters α and λ has gamma distribution with parameters nα and λ. </p><p>Definition: If Z is a standard normal rv, the distribution of U = Z<sup style="top: -0.3616em;">2 </sup>called the chi-square </p><p>distribution with 1 degree of freedom. </p><p>The density function of U ∼ χ<sup style="top: -0.3616em;">2</sup><sub style="top: 0.2463em;">1 </sub>is </p><p>x<sup style="top: -0.3615em;">−1/2 </sup></p><p>e<sup style="top: -0.4113em;">−x/2 </sup></p><p></p><ul style="display: flex;"><li style="flex:1">,</li><li style="flex:1">x > 0. </li></ul><p></p><p>√</p><p>f<sub style="top: 0.1494em;">U </sub>(x) = </p><p>2π </p><p>Remark: A χ<sup style="top: -0.3616em;">2</sup><sub style="top: 0.2463em;">1 </sub>random variable has the same density as a random variable with gamma distribution, with parameters α = 1/2 and λ = 1/2. </p><p>Definition: If U<sub style="top: 0.1494em;">1</sub>, U<sub style="top: 0.1494em;">2</sub>, . . . , U<sub style="top: 0.1494em;">k </sub>are independent chi-square rv-s with 1 degree of freedom, the distribution of V = U<sub style="top: 0.1494em;">1 </sub>+ U<sub style="top: 0.1494em;">2 </sub>+ . . . + U<sub style="top: 0.1494em;">k </sub>is called the chi-square distribution with k degrees </p><p>of freedom. </p><p>Using Remark 3. and the above remark, a χ<sup style="top: -0.3615em;">2</sup><sub style="top: 0.2463em;">k </sub>rv. follows gamma distribution with parameters α = k/2 and λ = 1/2. Thus the density function of V ∼ χ<sup style="top: -0.3616em;">2</sup><sub style="top: 0.2463em;">k </sub>is: </p><p>1f<sub style="top: 0.1495em;">V </sub>(x) = </p><p>x<sup style="top: -0.4113em;">k/2−1</sup>e<sup style="top: -0.4113em;">−x/2 </sup></p><p></p><ul style="display: flex;"><li style="flex:1">,</li><li style="flex:1">x > 0 </li></ul><p></p><p>2<sup style="top: -0.2878em;">k/2</sup>Γ(k/2) <br>1<br>Proposition: If V has a chi-square distribution with k degree of freedom, then <br>E(V ) = k, Var(V ) = 2k. </p><p>Definition: If Z ∼ N(0, 1) and U ∼ χ<sup style="top: -0.3615em;">2</sup><sub style="top: 0.2463em;">n </sub>and Z and U are independent, then the distribution of </p><p>Z</p><p>q</p><p>U/n </p><p>is called the t distribution with n degrees of freedom. </p><p>Proposition: The density function of the t distribution with n degrees of freedom is </p><p></p><ul style="display: flex;"><li style="flex:1"> </li><li style="flex:1">!</li></ul><p></p><p>−(n+1)/2 </p><p>Γ[(n + 1)/2] </p><p>√</p><p>t<sup style="top: -0.3616em;">2 </sup></p><p>n</p><p></p><ul style="display: flex;"><li style="flex:1">f(t) = </li><li style="flex:1">1 + </li></ul><p></p><p>.nπ Γ(n/2) </p><p>Remarks: For the above density f(t) = f(−t), so the t density is symmetric about zero. As the number of degrees of freedom approaches infinity, the t distribution tends to be standard normal. </p><p>Definition: Let U and V be independent chi-square variables with m and n degrees of freedom, respectively. The distribution of </p><p>U/m <br>W = <br>V/n </p><p>is called F distribution with m and n degrees of freedom and is denoted by F<sub style="top: 0.1494em;">m,n </sub></p><p>.</p><p>Remarks: </p><p>(i) If T ∼ t<sub style="top: 0.1494em;">n</sub>, then T<sup style="top: -0.3615em;">2 </sup>∼ F<sub style="top: 0.1494em;">1,n</sub>. (ii) If X ∼ F<sub style="top: 0.1494em;">n,m</sub>, then X<sup style="top: -0.3616em;">−1 </sup>∼ F<sub style="top: 0.1494em;">m,n</sub>. </p><p>2</p>
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