Step 1: Properties of orthogonal matrices.
A matrix \( P \) is orthogonal if \( P^T P = I_3 \), meaning that \( P \) preserves the length of vectors. This implies that all eigenvalues of \( P \) lie on the unit circle in the complex plane, i.e., they have an absolute value of 1. Since \( \det(P) = 1 \), the product of the eigenvalues is 1.
Step 2: Analyzing the options.
Option (A): A matrix \( P \in \mathcal{O} \) with \( \lambda = \frac{1}{2} \) as an eigenvalue is impossible because the eigenvalues must have an absolute value of 1.
Option (B): A matrix \( P \in \mathcal{O} \) with \( \lambda = 2 \) as an eigenvalue is also impossible for the same reason.
Option (C): If \( \lambda \) is the only real eigenvalue of \( P \in \mathcal{O} \), the other two eigenvalues must be complex conjugates, and the product of all three eigenvalues must be 1. Hence, \( \lambda = 1 \).
Option (D): Since \( \det(P) = 1 \) and \( P \) is orthogonal, it is possible for \( \lambda = -1 \) to be an eigenvalue (this would correspond to a reflection matrix). Thus, the correct answer is \( \boxed{(C)} \) and \( \boxed{(D)} \).
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).
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).