Let \( \{ X_n \}_{n \geq 1} \) be a sequence of independent random variables with \[ \Pr(X_n = -\frac{1}{2^n}) = \Pr(X_n = \frac{1}{2^n}) = \frac{1}{2}, \quad \forall n \in \mathbb{N}. \] Suppose that \( \sum_{i=1}^{n} X_i \) converges to \( U \) as \( n \to \infty \). Then \( 6 \Pr(U \leq \frac{2}{3}) \) equals ___________ (answer in integer).
Step 1: Understanding the distribution of \( X_n \).
Each \( X_n \) is a random variable with two possible outcomes, \( -\frac{1}{2^n} \) and \( \frac{1}{2^n} \), each with probability \( \frac{1}{2} \).
Step 2: Analyze the sum \( S_n = \sum_{i=1}^{n} X_i \).
The random variables \( X_n \) are independent and symmetric, meaning that each step contributes positively or negatively by a decreasing amount, \( \pm \frac{1}{2^n} \). As \( n \to \infty \), the sum \( S_n = \sum_{i=1}^{n} X_i \) converges to the limit \( U \), which is a random variable. The limit \( U \) is the sum of the infinite series: \[ U = \sum_{n=1}^{\infty} X_n. \] This sum converges because the magnitude of each term decreases exponentially.
Step 3: Distribution of \( U \).
The limiting distribution of \( U \) is a uniform distribution on the interval \( [-1, 1] \), since the steps \( X_n \) contribute to the value of \( U \) in a balanced way and the sum converges. Therefore, \( U \) is uniformly distributed on \( [-1, 1] \).
Step 4: Calculate \( \Pr(U \leq \frac{2}{3}) \).
For a uniform random variable \( U \) on \( [-1, 1] \), the probability that \( U \leq \frac{2}{3} \) is simply the proportion of the interval \( [-1, 1] \) that is less than or equal to \( \frac{2}{3} \). The length of the interval from \( -1 \) to \( \frac{2}{3} \) is: \[ \frac{2}{3} - (-1) = \frac{5}{3}. \] Since the total length of the interval \( [-1, 1] \) is 2, the probability is: \[ \Pr(U \leq \frac{2}{3}) = \frac{\frac{5}{3}}{2} = \frac{5}{6}. \]
Step 5: Multiply by 6.
We are asked to find \( 6 \Pr(U \leq \frac{2}{3}) \), so: \[ 6 \times \frac{5}{6} = 5. \]
Thus, the correct answer is \( \boxed{5} \).
Let \( (X, Y)^T \) follow a bivariate normal distribution with \[ E(X) = 2, \, E(Y) = 3, \, {Var}(X) = 16, \, {Var}(Y) = 25, \, {Cov}(X, Y) = 14. \] Then \[ 2\pi \left( \Pr(X>2, Y>3) - \frac{1}{4} \right) \] equals _________ (rounded off to two decimal places).
For \( Y \in \mathbb{R}^n \), \( X \in \mathbb{R}^{n \times p} \), and \( \beta \in \mathbb{R}^p \), consider a regression model \[ Y = X \beta + \epsilon, \] where \( \epsilon \) has an \( n \)-dimensional multivariate normal distribution with zero mean vector and identity covariance matrix. Let \( I_p \) denote the identity matrix of order \( p \). For \( \lambda>0 \), let \[ \hat{\beta}_n = (X^T X + \lambda I_p)^{-1} X^T Y, \] be an estimator of \( \beta \). Then which of the following options is/are correct?
Let \( X = (X_1, X_2, X_3)^T \) be a 3-dimensional random vector having multivariate normal distribution with mean vector \( (0, 0, 0)^T \) and covariance matrix
\[ \Sigma = \begin{pmatrix} 4 & 0 & 0 <br> 0 & 9 & 0 <br> 0 & 0 & 4 \end{pmatrix}. \]
{Let } \( \alpha^T = (2, 0, -1) \) { and } \( \beta^T = (1, 1, 1) \).
Then which of the following statements is/are correct?
Let \( X_1, X_2 \) be a random sample from a population having probability density function
\[ f_{\theta}(x) = \begin{cases} e^{(x-\theta)} & \text{if } -\infty < x \leq \theta, \\ 0 & \text{otherwise}, \end{cases} \] where \( \theta \in \mathbb{R} \) is an unknown parameter. Consider testing \( H_0: \theta \geq 0 \) against \( H_1: \theta < 0 \) at level \( \alpha = 0.09 \). Let \( \beta(\theta) \) denote the power function of a uniformly most powerful test. Then \( \beta(\log_e 0.36) \) equals ________ (rounded off to two decimal places).
Let \( X_1, X_2, \dots, X_7 \) be a random sample from a population having the probability density function \[ f(x) = \frac{1}{2} \lambda^3 x^2 e^{-\lambda x}, \quad x>0, \] where \( \lambda>0 \) is an unknown parameter. Let \( \hat{\lambda} \) be the maximum likelihood estimator of \( \lambda \), and \( E(\hat{\lambda} - \lambda) = \alpha \lambda \) be the corresponding bias, where \( \alpha \) is a real constant. Then the value of \( \frac{1}{\alpha} \) equals __________ (answer in integer).
The service times (in minutes) at two petrol pumps \( P_1 \) and \( P_2 \) follow distributions with probability density functions \[ f_1(x) = \lambda e^{-\lambda x}, \quad x>0 \quad {and} \quad f_2(x) = \lambda^2 x e^{-\lambda x}, \quad x>0, \] respectively, where \( \lambda>0 \). For service, a customer chooses \( P_1 \) or \( P_2 \) randomly with equal probability. Suppose, the probability that the service time for the customer is more than one minute, is \( 2e^{-2} \). Then the value of \( \lambda \) equals _________ (answer in integer).
The moment generating functions of three independent random variables \( X, Y, Z \) are respectively given as: \[ M_X(t) = \frac{1}{9}(2 + e^t)^2, \quad t \in \mathbb{R}, \] \[ M_Y(t) = e^{e^t - 1}, \quad t \in \mathbb{R}, \] \[ M_Z(t) = e^{2(e^t - 1)}, \quad t \in \mathbb{R}. \] Then \( 10 \cdot \Pr(X>Y + Z) \) equals __________ (rounded off to two decimal places).