Joint Density and Covariance of Two Uniform Order Statistics
Let $X_1, \ldots, X_n$ be iid $\operatorname{Uniform}(0,1)$. Consider the order statistics $X_{(i)}$ and $X_{(j)}$ with $1 \le i < j \le n$.
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中文题目Let $X_1, \ldots, X_n$ be iid $\operatorname{Uniform}(0,1)$. Consider the order statistics $X_{(i)}$ and $X_{(j)}$ with $1 \le i < j \le n$.
打开 →Let $X_1, \ldots, X_n$ be iid $\operatorname{Uniform}(0,1)$. Let $X_{(1)} = \min_i X_i$ and $X_{(n)} = \max_i X_i$.
打开 →If you join the current best quote, fill probability is 0.26, size is 120, raw edge per share is 0.011, and rebate is 0.0005. If you improve the quote, fill probability becomes 0.18, size is 120, raw edge per share is 0.017, and rebate is 0.0005. Which action has higher expected
打开 →The cost couples size and trading time through a perspective form. Show that P(x,t)=x^2/t + 3 t is convex on the domain t>0.
打开 →A Poisson process at rate $\lambda=30$ per hour is split by independent fair-coin-style labeling into a 'lit' stream (prob $0.2$) and a 'dark' stream (prob $0.8$). Over the next 30 minutes, what is the probability of observing exactly $4$ lit prints and exactly $9$ dark prints?
打开 →For standard Brownian motion, what is P(max_{0<=s<=1} W_s >= 1, W_1 <= 0)?
打开 →Suppose $X_1,\dots,X_9$ are modeled as i.i.d. $N(\mu,\sigma^2)$. From the sample you know that $$\bar X = 5, \qquad \sum_{i=1}^9 (X_i-\bar X)^2 = 18.$$ Find the MLEs of $\mu$ and $\sigma^2$.
打开 →For standard Brownian motion over t = 1, what barrier a gives P(max_{0<=s<=1} W_s >= a, W_1 <= 0) = 0.05?
打开 →For standard Brownian motion over t = 1 with barrier a = 1, what endpoint cap b gives P(max_{0<=s<=1} W_s >= 1, W_1 <= b) = 0.10?
打开 →Suppose \[ M_{X,Y}(s,t)=\exp\!\left(s+2t+\frac{s^2}{2}+2t^2\right). \] Identify the marginal laws of $X$ and $Y$, and determine whether they are independent.
打开 →Suppose \[ M_{X,Y}(s,t)=\exp\!\bigl(2s-t+2s^2+3st+\tfrac52 t^2\bigr). \] Compute $E[X]$, $E[Y]$, $\mathrm{Var}(X)$, $\mathrm{Var}(Y)$, and $\mathrm{Cov}(X,Y)$.
打开 →A regime label takes values A, B, C with probabilities 0.2, 0.5, 0.3. Conditional on A the source is deterministic, conditional on B it is uniform over 4 symbols, and conditional on C it is uniform over 2 symbols. Assuming the symbol sets are disjoint across regimes, what is the
打开 →Suppose $P(A) = 1/3$ and $P(B) = 1/2$. Can $A$ and $B$ be simultaneously independent and mutually exclusive? Prove your answer.
打开 →What does the softmax construction add that one-vs-rest logistic models do not provide automatically?
打开 →Let $X$ and $Y$ be independent $\text{Bernoulli}(1/2)$ random variables. Define $W = \max(X, Y)$. (a) Compute the distribution of $W$. (b) Determine whether $X$ and $W$ are independent by checking all four joint probabilities $P(X = x, W = w)$ for $x, w \in \{0, 1\}$.
打开 →Let $X \sim \text{Gamma}(\alpha, 1)$ and $Y \sim \text{Gamma}(\beta, 1)$ be independent. (a) Define $U = \frac{X}{X+Y}$ and $V = X + Y$. Compute the Jacobian of the transformation $(X, Y) \mapsto (U, V)$. (b) Derive the joint PDF of $(U, V)$ and show that $U$ and $V$ are indepe
打开 →Let $X \sim \chi^2(m)$ and $Y \sim \chi^2(n)$ be independent. Define $$F = \frac{X/m}{Y/n}.$$ (a) Using the transformation $(F, W) = \bigl(\frac{nX}{mY},\; Y\bigr)$, compute the Jacobian and derive the joint density $f_{F,W}$. (b) Integrate out $W$ to obtain the marginal PDF of
打开 →Let $X_1, \ldots, X_n$ be iid $\operatorname{Uniform}(0,1)$ with $n \ge 2$. The mid-range is defined as $M = \frac{X_{(1)} + X_{(n)}}{2}$. Using the joint density of $(X_{(1)}, X_{(n)})$, derive the PDF of $M$.
打开 →Let $X$ and $Y$ be independent $\operatorname{Exp}(1)$ random variables. Define $R = X/Y$. (a) Using the transformation $(R, S) = (X/Y,\, Y)$, compute the joint density $f_{R,S}$ via the Jacobian and then marginalize over $S$ to find the PDF of $R$. (b) Identify $f_R$ as a name
打开 →A biased coin lands heads with probability $1/2$. If heads, set $(X, Y) = (1, 1)$. If tails, draw $X$ and $Y$ independently, each $\text{Bernoulli}(1/2)$. (a) Compute the full joint distribution of $(X, Y)$. (b) Show that $X$ and $Y$ have the same marginal distribution. (c) Are $
打开 →Let $X \sim \operatorname{Gamma}(\alpha, 1)$ and $Y \sim \operatorname{Gamma}(\beta, 1)$ be independent. Using the transformation $(W, S) = \bigl(X/(X+Y),\; X+Y\bigr)$: (a) Compute the Jacobian of the inverse map. (b) Derive the joint density $f_{W,S}$ and marginalize to show $
打开 →Let $Z_1, Z_2$ be independent $N(0,1)$ random variables. Define $R = Z_1 / Z_2$. (a) Write the joint PDF of $(Z_1, Z_2)$ and use the transformation $(Z_1, Z_2) \mapsto (R, Z_2) = (Z_1/Z_2,\, Z_2)$ to derive the joint PDF of $(R, Z_2)$. (b) Integrate out $Z_2$ to obtain the marg
打开 →Let $X_1, X_2 \sim \text{iid } N(0,1)$. Using the transformation $(Y, V) = (X_1/X_2,\, X_2)$: (a) Derive the joint density $f_{Y,V}(y,v)$. (b) Integrate out $V$ to obtain the marginal PDF of $Y = X_1/X_2$ and identify the distribution.
打开 →Let $X_1, X_2 \sim \text{iid } N(0,1)$. Define $R = X_1^2 + X_2^2$. (a) Switching to polar coordinates $(X_1, X_2) = (r\cos\theta, r\sin\theta)$, derive the joint density of $(R, \Theta)$ where $R = X_1^2 + X_2^2$ and $\Theta = \arctan(X_2/X_1)$. (b) Marginalize over $\Theta$ t
打开 →Let $X$ and $Y$ be independent $\text{Bernoulli}(1/2)$ random variables. Define $Z = X \oplus Y$ (where $\oplus$ denotes addition modulo 2). (a) Show that $Z \sim \text{Bernoulli}(1/2)$. (b) Show that any two of $\{X, Y, Z\}$ are independent. (c) Are $X$, $Y$, $Z$ mutually indepe
打开 →Let $\Omega = \{0, 1, 2, \ldots, 7\}$ with uniform probability $P(\{\omega\}) = 1/8$. Write each $\omega$ in binary as $(b_2, b_1, b_0)$. Define events: $$A = \{\omega : b_0 = 1\}, \quad B = \{\omega : b_1 = 1\}, \quad C = \{\omega : b_2 = 1\}, \quad D = \{\omega : b_0 \oplus b_1
打开 →For standard Brownian motion, what is P(max_{0<=s<=1} W_s < 1)?
打开 →Let $U_1, U_2$ be independent $\operatorname{Uniform}(0,1)$ random variables. Define $$Z_1 = \sqrt{-2\ln U_1}\,\cos(2\pi U_2), \qquad Z_2 = \sqrt{-2\ln U_1}\,\sin(2\pi U_2).$$ (a) Compute the Jacobian of the inverse transformation from $(Z_1, Z_2)$ back to $(U_1, U_2)$. (b) Sho
打开 →Let $X_1, \ldots, X_n$ be iid $\operatorname{Uniform}(0,1)$ with $n \ge 3$. Let $X_{(1)}$ and $X_{(n)}$ denote the minimum and maximum.
打开 →Let $X \sim \operatorname{Uniform}(0,1)$. Using the CDF method, derive the PDF of $Y = X^3$.
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