Recent content by showzen

  1. S

    Exponential Order Statistics and Independence

    I am using this definition, with ##\beta=1## and ##\mu=\theta##. Also, I am using the indicator function to specify the domain.
  2. S

    Exponential Order Statistics and Independence

    Homework Statement Consider the exponential probability density function with location parameter ##\theta## : $$f(x|\theta) = e^{-(x-\theta)}I_{(\theta,\infty)}(x)$$ Let ##X_{(1)}, X_{(2)},...,X_{(n)}## denote the order statistics. Let ##Y_i = X_{(n)} - X_{(i)}##. Show that ##X_{(1)}## is...
  3. S

    A Calculating Bivariate Normal Probabilities

    Hello good people of PF, I came across this problem today. Problem Statement Given bivariate normal distribution ##X,Y \sim N(\mu_x=\mu_y=0, \sigma_x=\sigma_y=1, \rho=0.5)##, determine ##P(0 < X+Y < 6)##. My Approach I reason that $$ P(0 < X+Y < 6) = P(-X < Y < 6-X)$$ $$ =...
  4. S

    I Probability that X is less than a set

    Is there any supplementary resource that you would recommend?
  5. S

    I Probability that X is less than a set

    If there is more than one ##x## for which ##g(x)=y## then ##g^{-1}(y)## is a set.
  6. S

    I Probability that X is less than a set

    Hi everyone, I am currently working through the textbook Statistical Inference by Casella and Berger. My question has to do with transformations. Let ##X## be a random variable with cdf ##F_X(x)##. We want to find the cdf of ##Y=g(X)##. So we define the inverse mapping, ##g^{-1}(\{y\})=\{x\in...
  7. S

    Covariance of partitioned linear combination

    Thanks everyone for your help. I see now that the answer is a matrix with elements that are the covariance between ##AX^{(1)}## and the elements of ##BX^{(2)}##.
  8. S

    Covariance of partitioned linear combination

    Homework Statement Given random vector ##X'=[X_1,X_2,X_3,X_4]## with mean vector ##\mu '_X=[4,3,2,1]## and covariance matrix $$\Sigma_X=\begin{bmatrix} 3&0&2&2\\ 0&1&1&0\\ 2&1&9&-2\\ 2&0&-2&4 \end{bmatrix}.$$ Partition ##X## as $$X=\begin{bmatrix} X_1\\X_2\\\hline X_3\\X_4\end{bmatrix}...
  9. S

    Independence of Random Variables

    Yes, the answers are different. Can ##X## and ##Y## only be independent if they are defined over a rectangle?
  10. S

    Independence of Random Variables

    ##f_X(x) = 2\int_x^\infty e^{-x}e^{-y}dy = 2e^{-2x}## ##f_Y(y) = 2\int_0^y e^{-x}e^{-y}dx = 2e^{-y}(1-e^{-y})## ##f_X(x)f_Y(y) = 4e^{-2x}e^{-y}(1-e^{-y}) \neq f_{X,Y}(x,y)## So we can show explicitly that they are not independent... But we can separate ##f_{X,Y}(x,y)## into a product of...
  11. S

    Independence of Random Variables

    Homework Statement Given ##f_{X,Y}(x,y)=2e^{-x}e^{-y}\ ;\ 0<x<y\ ;\ y>0##, The following theorem given in my book (Larsen and Marx) doesn't appear to hold. Homework Equations Definition ##X## and ##Y## are independent if for every interval ##A## and ##B##, ##P(X\in A \land Y\in B) = P(X\in...
  12. S

    Random Walk - 1 dimension

    This problem had 4 steps with equal probability. So in general with ##n## steps and ##p## probability, we have ## P(ending \ at \ w) = {{n}\choose{k}} p^k (1-p)^{n-k} \ where \ k = \frac {(n+w)} {2} ##?
  13. S

    Random Walk - 1 dimension

    Outcomes are [-4,4]. The variable k is the number of successes. Let's define success as +1 on the x axis. The relation between k and the outcomes... k=0 gives the outcome -4, k=1 gives the outcome -2, k=2 gives the outcome 0, k=3 gives the outcome 2, k=4 gives the outcome 4. So ##P(w) =...
  14. S

    Random Walk - 1 dimension

    Homework Statement Suppose a particle moves along the x-axis beginning at 0. It moves one integer step to the left or right with equal probability. What is the pdf of its position after four steps? 2. Homework Equations Binomial distribution ##P(k) = {{n}\choose{k}} p^k (1-p)^{n-k}## The...
  15. S

    Can Conservation of Energy Explain Pulley and Inclined Plane Mechanics?

    I see my mistake now! ##\delta y## moves ##w## by ##2\delta y##. So ##W=\frac{4w}{sin\theta}##, which is what I get from force analysis as well.
Back
Top