概率论与数理统计 (Spring 2023)/Problem Set 3: Difference between revisions
Jump to navigation
Jump to search
Line 12: | Line 12: | ||
== Problem 1 == | == Problem 1 == | ||
<ul> | <ul> | ||
<li> [<strong> | <li>[<strong>Variance (I)</strong>] | ||
For pairwise independent random variables <math>X_1,X_2,\cdots, X_n</math>, show that <math>\textbf{Var}\left[\sum_{i=1}^n X_i\right] =\sum_{i=1}^n \textbf{Var} (X_i)</math>. | For pairwise independent random variables <math>X_1,X_2,\cdots, X_n</math>, show that <math>\textbf{Var}\left[\sum_{i=1}^n X_i\right] =\sum_{i=1}^n \textbf{Var} (X_i)</math>. | ||
</li> | </li> | ||
<li>[<strong>Variance</strong>] | <li>[<strong>Variance (II)</strong>] | ||
Let <math>X = \sum_{i=1}^N X_i</math>, where <math>X_i (i\geq 1)</math> are independent, identically distributed random variables with mean <math>\mu</math> and variance <math>\sigma^2</math>, and <math>N</math> is positive, integer-valued random variable, and is independent of the <math>X_i</math> for all <math>i\geq 1</math>. Show that <math>\textbf{Var}(X) = \sigma^2\mathbb{E}[N] + \mu^2 \textbf{Var}(N)</math>. | Let <math>X = \sum_{i=1}^N X_i</math>, where <math>X_i (i\geq 1)</math> are independent, identically distributed random variables with mean <math>\mu</math> and variance <math>\sigma^2</math>, and <math>N</math> is positive, integer-valued random variable, and is independent of the <math>X_i</math> for all <math>i\geq 1</math>. Show that <math>\textbf{Var}(X) = \sigma^2\mathbb{E}[N] + \mu^2 \textbf{Var}(N)</math>. | ||
</li> | </li> | ||
<li>[<strong>Variance</strong>] | <li>[<strong>Variance (III)</strong>] | ||
Each member of a group of <math>n</math> players rolls a dice (with six faces). For any pair of players who throw the same number, the group scores 1 point. Find the mean and variance of the total score of the group. (Hint: use the property of pairwise independent.) | Each member of a group of <math>n</math> players rolls a dice (with six faces). For any pair of players who throw the same number, the group scores 1 point. Find the mean and variance of the total score of the group. (Hint: use the property of pairwise independent.) | ||
</li> | </li> | ||
<li>[<strong>Variance</strong>] | <li>[<strong>Variance (IV)</strong>] | ||
An urn contains <math>n</math> balls numbered 1, 2, ..., <math>n</math>. We remove <math>k</math> balls at random (without replacement) and add up their numbers. Find the mean and variance of the sum. | An urn contains <math>n</math> balls numbered 1, 2, ..., <math>n</math>. We remove <math>k</math> balls at random (without replacement) and add up their numbers. Find the mean and variance of the sum. | ||
</li> | </li> | ||
<li>[<strong>Covariance and | <li> [<strong>Moments (I)</strong>] | ||
Find an example of a random variable with finite <math>j</math>-th moments for <math>1 \leq j \leq k</math> but an unbounded <math>(k + 1)</math>-th moment. Give a clear argument showing that your choice has these properties. | |||
</li> | |||
<li>[<strong>Moments (II)</strong>] | |||
Let <math>X\sim \text{Geo}(p)</math> for some <math>p \in (0,1)</math>. Find <math>\mathbb{E}[X^3]</math> and <math>\mathbb{E}[X^4]</math>. (Hint: find a recursive expression from <math>\mathbb{E}[X^{n-1}]</math> to <math>\mathbb{E}[X^{n}]</math> may be useful.) | |||
</li> | |||
<li>[<strong>Covariance and correlation (I)</strong>] | |||
Let <math>X</math> and <math>Y</math> be discrete random variables with correlation <math>\rho</math>. Show that <math>|\rho|\leq 1</math>. | Let <math>X</math> and <math>Y</math> be discrete random variables with correlation <math>\rho</math>. Show that <math>|\rho|\leq 1</math>. | ||
</li> | </li> | ||
</ul> | </ul> |
Revision as of 10:09, 24 April 2023
- 每道题目的解答都要有完整的解题过程,中英文不限。
- 我们推荐大家使用LaTeX, markdown等对作业进行排版。
Assumption throughout Problem Set 3
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
- [Variance (I)] For pairwise independent random variables [math]\displaystyle{ X_1,X_2,\cdots, X_n }[/math], show that [math]\displaystyle{ \textbf{Var}\left[\sum_{i=1}^n X_i\right] =\sum_{i=1}^n \textbf{Var} (X_i) }[/math].
- [Variance (II)] Let [math]\displaystyle{ X = \sum_{i=1}^N X_i }[/math], where [math]\displaystyle{ X_i (i\geq 1) }[/math] are independent, identically distributed random variables with mean [math]\displaystyle{ \mu }[/math] and variance [math]\displaystyle{ \sigma^2 }[/math], and [math]\displaystyle{ N }[/math] is positive, integer-valued random variable, and is independent of the [math]\displaystyle{ X_i }[/math] for all [math]\displaystyle{ i\geq 1 }[/math]. Show that [math]\displaystyle{ \textbf{Var}(X) = \sigma^2\mathbb{E}[N] + \mu^2 \textbf{Var}(N) }[/math].
- [Variance (III)] Each member of a group of [math]\displaystyle{ n }[/math] players rolls a dice (with six faces). For any pair of players who throw the same number, the group scores 1 point. Find the mean and variance of the total score of the group. (Hint: use the property of pairwise independent.)
- [Variance (IV)] An urn contains [math]\displaystyle{ n }[/math] balls numbered 1, 2, ..., [math]\displaystyle{ n }[/math]. We remove [math]\displaystyle{ k }[/math] balls at random (without replacement) and add up their numbers. Find the mean and variance of the sum.
- [Moments (I)] Find an example of a random variable with finite [math]\displaystyle{ j }[/math]-th moments for [math]\displaystyle{ 1 \leq j \leq k }[/math] but an unbounded [math]\displaystyle{ (k + 1) }[/math]-th moment. Give a clear argument showing that your choice has these properties.
- [Moments (II)] Let [math]\displaystyle{ X\sim \text{Geo}(p) }[/math] for some [math]\displaystyle{ p \in (0,1) }[/math]. Find [math]\displaystyle{ \mathbb{E}[X^3] }[/math] and [math]\displaystyle{ \mathbb{E}[X^4] }[/math]. (Hint: find a recursive expression from [math]\displaystyle{ \mathbb{E}[X^{n-1}] }[/math] to [math]\displaystyle{ \mathbb{E}[X^{n}] }[/math] may be useful.)
- [Covariance and correlation (I)] Let [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] be discrete random variables with correlation [math]\displaystyle{ \rho }[/math]. Show that [math]\displaystyle{ |\rho|\leq 1 }[/math].