Answer to Question #338298 in Statistics and Probability for peace boy

Question #338298
  1. Let X1, X2 , and X3 be independent standard normal random variable. If we defined Y1=X2, Y2=X1+X2/2 and Y3=X1+ X2 +X3/3.Then find then joint pdf of Y1, Y2, and Y3 using the Jacobian method?
1
Expert's answer
2022-05-12T14:43:19-0400

Suppose that random variables "X_1", "X_2", "X_3" have normal distributions with parameters "\\mu_1,\\sigma^2_1", "\\mu_2,\\sigma^2_2" and "\\mu_3,\\sigma^2_3" respectively. We use a known fact that in case a random variable "Z" has a normal distribution with parameters "\\mu_Z,\\sigma^2_Z", then, a random variable "aZ", where "a\\in{\\mathbb{R}}," has a normal distribution with parameters "a\\mu_Z,a^2\\sigma^2_Z". We receive, that "\\frac{X_2}{2}" has a normal distribution with parameters "\\frac{\\mu_2}{2},\\frac{\\sigma^2_2}{4}" and "\\frac{X_3}{3}" has a normal distribution with parameters: "\\frac{\\mu_3}{3},\\frac{\\sigma^2_3}{9}". We use also a known fact, that a sum of normally distributed variables is again a normally distributed variable. We get, that "Y_2" has a normal distribution with parameters: "\\mu_1+\\frac{\\mu_2}2,\\sigma_1^2+\\frac{\\sigma_2^2}{4}" and "Y_3" has a normal distribution with parameters: "\\mu_1+\\mu_2+\\frac{\\mu_3}3,\\sigma_1^2+\\sigma_2^2+\\frac{\\sigma_3^2}{9}". Denote: "\\alpha_1=\\mu_1+\\frac{\\mu_2}2", "\\beta_1=\\sqrt{\\sigma_1^2+\\frac{\\sigma_2^2}{4}}", "\\alpha_2=\\mu_1+\\mu_2+\\frac{\\mu_3}3", "\\beta_2=\\sqrt{\\sigma_1^2+\\sigma_2^2+\\frac{\\sigma_3^2}{9}}". We use a known fact that the joint probability density function is equal to the product of density functions for independent variables. We get: "p_{Y_1,Y_2,Y_3}(y_1,y_2,y_3)=p_{Y_1}(y_1)p_{Y_2}(y_2)p_{Y_3}(y_3)=\\frac{1}{\\sigma_2\\beta_1\\beta_2(\\sqrt{2\\pi})^3}e^{-\\frac12(\\frac{y_1-\\mu_2}{\\sigma_2})^2-\\frac12(\\frac{y_2-\\alpha_1}{\\beta_1})^2-\\frac12(\\frac{y_3-\\alpha_2}{\\beta_2})^2}"

Answer: "p_{Y_1,Y_2,Y_3}(y_1,y_2,y_3)=\\frac{1}{\\sigma_2\\beta_1\\beta_2(\\sqrt{2\\pi})^3}e^{-\\frac12(\\frac{y_1-\\mu_2}{\\sigma_2})^2-\\frac12(\\frac{y_2-\\alpha_1}{\\beta_1})^2-\\frac12(\\frac{y_3-\\alpha_2}{\\beta_2})^2}."


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