概率论与数理统计 (Spring 2023)/Problem Set 4

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Assumption throughout Problem Set 4

Without further notice, we are working on probability space [math]\displaystyle{ (\Omega,\mathcal{F},\mathbf{Pr}) }[/math].

Without further notice, we assume that the expectation of random variables are well-defined.

The term [math]\displaystyle{ \log }[/math] used in this context refers to the natural logarithm.

Problem 1 (Continuous Random Variables, 30 points)

• [Density function] Determine the value of [math]\displaystyle{ C }[/math] such that [math]\displaystyle{ f(x) = C\exp(-x-e^{-x}), x\in \mathbb{R} }[/math] is a probability density function (PDF) for a continuous random variable.
• [Independence] Let [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] be independent and identically distributed continuous random variables with cumulative distribution function (CDF) [math]\displaystyle{ F }[/math] and probability density function (PDF) [math]\displaystyle{ f }[/math]. Find out the density functions of [math]\displaystyle{ V = \max\{X,Y\} }[/math] and [math]\displaystyle{ U = \min\{X,Y\} }[/math].
• [Correlation] Let [math]\displaystyle{ X }[/math] be uniformly distributed on [math]\displaystyle{ (-1,1) }[/math] and [math]\displaystyle{ Y_i = \cos(n \pi X) }[/math] for [math]\displaystyle{ i=1,2,\ldots,n }[/math]. Are the random variables [math]\displaystyle{ Y_1, Y_2, \ldots, Y_n }[/math] correlated? independent? You should prove your claim rigorously.
• [Expectation of random variables (I)] Let [math]\displaystyle{ X }[/math] be a continuous random variable with mean [math]\displaystyle{ \mu }[/math] and cumulative distribution function (CDF) [math]\displaystyle{ F }[/math].
  • Suppose [math]\displaystyle{ X \ge 0 }[/math]. Show that [math]\displaystyle{ \int_{0}^a F(x) dx = \int_{a}^{\infty} [1-F(x)] dx }[/math] if and only if [math]\displaystyle{ a = \mu }[/math].
  • Suppose [math]\displaystyle{ X }[/math] further has finite variance. Show that [math]\displaystyle{ g(a) = \mathbb{E}((X-a)^2) }[/math] is a minimum when [math]\displaystyle{ a = \mu }[/math].
• [Expectation of random variables (II)] Let [math]\displaystyle{ X, Y }[/math] be two independent and identically distributed continuous random variables with cumulative distribution function (CDF) [math]\displaystyle{ F }[/math]. Furthermore, [math]\displaystyle{ X,Y \ge 0 }[/math]. Show that [math]\displaystyle{ \mathbb{E}[|X-Y|] = 2 \left(\mathbb{E}[X] - \int_{0}^{\infty} (1-F(x))^2 dx\right) }[/math]
• [Conditional distribution] Let [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] be two random variables. The joint density of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is given by [math]\displaystyle{ f(x,y) = c(x^2 - y^2)e^{-x} }[/math], where [math]\displaystyle{ 0\leq x \lt \infty }[/math] and [math]\displaystyle{ -x\leq y \leq x }[/math]. Here, [math]\displaystyle{ c\in \mathbb{R}_+ }[/math] is a constant. Find out the conditional distribution of [math]\displaystyle{ Y }[/math], given [math]\displaystyle{ X = x }[/math].
• [Uniform Distribution (I)] Let [math]\displaystyle{ P_i = (X_i,Y_i), 1\leq i\leq n }[/math], be independent, uniformly distributed points in the unit square [math]\displaystyle{ [0,1]^2 }[/math]. A point [math]\displaystyle{ P_i }[/math] is called "peripheral" if, for all [math]\displaystyle{ r = 1,2,\cdots,n }[/math], either [math]\displaystyle{ X_r \leq X_i }[/math] or [math]\displaystyle{ Y_r \leq Y_i }[/math], or both. Find out the expected number of peripheral points.
• [Uniform Distribution (II)] Derive the moment generating function of the standard uniform distribution, i.e., uniform distribution on [math]\displaystyle{ (0,1) }[/math].
• [Exponential distribution] Let [math]\displaystyle{ X }[/math] have an exponential distribution. Show that [math]\displaystyle{ \textbf{Pr}[X\gt s+x|X\gt s] = \textbf{Pr}[X\gt x] }[/math], for [math]\displaystyle{ x,s\geq 0 }[/math]. This is the "lack of memory" property. Show that the exponential distribution is the only continuous distribution with this property.
• [Normal distribution(I)] Let [math]\displaystyle{ X,Y\sim N(0,1) }[/math] be two independent and identically distributed normal random variables. Let [math]\displaystyle{ Z = X-Y }[/math]. Find the density function of [math]\displaystyle{ Z }[/math] and [math]\displaystyle{ |Z| }[/math] respectively.
• [Normal distribution(II)] Let [math]\displaystyle{ X }[/math] have the [math]\displaystyle{ N(0,1) }[/math] distribution and let [math]\displaystyle{ a\gt 0 }[/math]. Show that the random variable [math]\displaystyle{ Y }[/math] given by [math]\displaystyle{ \begin{equation*} Y = \begin{cases} X, & |X|\lt a \\ -X, & |X|\geq a \end{cases} \end{equation*} }[/math] has the [math]\displaystyle{ N(0,1) }[/math] distribution, and find an expression for [math]\displaystyle{ \rho(a) = \textbf{Cov}(X,Y) }[/math] in terms of the density function [math]\displaystyle{ \phi }[/math] of [math]\displaystyle{ X }[/math].
• [Normal distribution(III)] Let [math]\displaystyle{ (X,Y) }[/math] be bivariate normal random variables with correlation [math]\displaystyle{ \rho }[/math], and marginally [math]\displaystyle{ X\sim N(\mu_X,\sigma_X^2) }[/math] and [math]\displaystyle{ Y\sim N(\mu_Y,\sigma_Y^2) }[/math]. If [math]\displaystyle{ \sigma_X^2 = \sigma_Y^2 }[/math], show that [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y - \rho X }[/math] are independent.
• [Random process (I)] Given a real number [math]\displaystyle{ U\lt 1 }[/math] as input of the following process, find out the expected returning value.

  Process 1

  Input: real numbers [math]\displaystyle{ U \lt 1 }[/math]; initialize [math]\displaystyle{ x = 1 }[/math] and [math]\displaystyle{ count = 0 }[/math]; while [math]\displaystyle{ x \gt U }[/math] do choose [math]\displaystyle{ y \in (0,1) }[/math] uniformly at random; update [math]\displaystyle{ x = x * y }[/math] and [math]\displaystyle{ count = count + 1 }[/math]; return [math]\displaystyle{ count }[/math];

• [Random process (II)] Given a real number [math]\displaystyle{ U\lt 1 }[/math] as input of the following process, find out the expected returning value.

  Process 2

  Input: real numbers [math]\displaystyle{ U \lt 1 }[/math]; initialize [math]\displaystyle{ x = 0 }[/math] and [math]\displaystyle{ count = 0 }[/math]; while [math]\displaystyle{ x \lt U }[/math] do choose [math]\displaystyle{ y \in (0,1) }[/math] uniformly at random; update [math]\displaystyle{ x = x + y }[/math] and [math]\displaystyle{ count = count + 1 }[/math]; return [math]\displaystyle{ count }[/math];

• [Random semicircle] We sample [math]\displaystyle{ n }[/math] points within a circle [math]\displaystyle{ C=\{(x,y) \in \mathbb{R}^2 \mid x^2+y^2 \le 1\} }[/math] independently and uniformly at random (i.e., the density function [math]\displaystyle{ f(x,y) \propto 1_{(x,y) \in C} }[/math]). Find out the probability that they all lie within some semicircle with radius [math]\displaystyle{ 1 }[/math]. (Hint: you may apply the technique of change of variables, see function of random variables or Chapter 4.7 in [GS])
• [Stochastic domination] Let [math]\displaystyle{ X, Y }[/math] be continuous random variables. Show that [math]\displaystyle{ X }[/math] dominates [math]\displaystyle{ Y }[/math] stochastically if and only if [math]\displaystyle{ \mathbb{E}[f(X)]\geq \mathbb{E}[f(Y)] }[/math] for any non-decreasing function [math]\displaystyle{ f }[/math] for which the expectations exist.

Problem 2 (Modes of Convergence)

• [Convergence in mean] Let [math]\displaystyle{ (X_n)_{n \ge 1}, X }[/math] be random variables. If [math]\displaystyle{ X_n \overset{1}{\to} X }[/math], then [math]\displaystyle{ \mathbb{E}[X_n] \to \mathbb{E}[X] }[/math] as [math]\displaystyle{ n \to \infty }[/math].
• [Independent random variables] Let [math]\displaystyle{ X,Y }[/math] be independent random variables with finite means. Show that [math]\displaystyle{ \mathbb{E}[XY]=\mathbb{E}[X] \mathbb{E}[Y] }[/math] by using the statement in previous problem. (Hint: Consider [math]\displaystyle{ X_n = \lfloor nX \rfloor/n }[/math] and [math]\displaystyle{ Y_n = \lfloor nY \rfloor/n }[/math])
• [Connection of convergence (I)] Let [math]\displaystyle{ (X_n)_{n \ge 1}, (Y_n)_{n \ge 1}, X }[/math] be random variables and [math]\displaystyle{ c\in\mathbb{R} }[/math] be a real number. Suppose [math]\displaystyle{ X_n \overset{D}{\to} X }[/math] and [math]\displaystyle{ Y_n \overset{P}{\to} c }[/math]. Prove that [math]\displaystyle{ X_nY_n \overset{P}{\to} cX }[/math].
• [Connection of convergence (II)] Let [math]\displaystyle{ (X_n)_{n \ge 1}, X }[/math] be random variables. Prove that [math]\displaystyle{ X_n \overset{P}{\to} X }[/math] if and only if for every subsequence [math]\displaystyle{ X_{n(m)} }[/math], there exists a further subsequence [math]\displaystyle{ Y_k = X_{n(m_k)} }[/math] that converges almost surely to [math]\displaystyle{ X }[/math]. (Hint: you may use the first Borel-Cantelli lemma.)

Problem 3 (Borel-Cantelli Lemmas) (Bonus problem, 5 points)

Let [math]\displaystyle{ (A_n)_{n \ge 1} }[/math] be events. Suppose [math]\displaystyle{ \sum_{n \ge 1} \mathbf{Pr}(A_n)=+\infty }[/math]. Show that [math]\displaystyle{ \mathbf{Pr}(A_n \text{ i.o.}) \ge \limsup_{n \to \infty} \frac{ \left(\sum_{k=1}^n\mathbf{Pr}(A_k)\right)^2 }{\sum_{1\le j,k \le n} \mathbf{Pr}(A_j \cap A_k)} }[/math].