[SOLVED] EMATM0061 Statistical Computing and Empirical Methods TB1 2024 Assignment 6

Assignment 6

EMATM0061: Statistical Computing and Empirical Methods, TB1, 2024

Introduction

This is the sixth assignment for Statistical Computing and Empirical Methods. This assignment is mainly based on Lectures 15, 16, and 17 (see the Blackboard).

You can optionally submit this assignment by 13:00 Monday 4th November. This will help us understand your work but will not count towards your final grade. The submission point can be found under the assignment tab at Blackboards (click the   title “Assignment 06” and upload a pdf file).

Load packages

Some of the questions in this assignment require the tidyverse package. If it hasn’t been installed on your computer, please use “install.packages()” to install them first.

To load the tidyverse package:

library(tidyverse)

1. The Gaussian distribution

Gaussian random variables are an important family of continuous random variables.

In this assignment, we will first explore several properties of the Gaussian distribution.

Use the help function to look up the following four functions: “dnorm()”, “pnorm()”, “qnorm()” and “rnorm()” .

Also, the probability density function of a Gaussian random variable was introduced in Lecture 14.

(Q1) Generate a plot which displays the probability density function for three  Gaussian random variables X1 ∼ N(μ1, σ1(2)), X2 ∼ N(μ2, σ2(2)), and X3 ∼ N(μ3, σ3(2)) with μ1 = μ2 = μ3 = 1 and σ1(2) = 1, σ2(2) = 2, σ3(2) = 3.

Your plot should look like this:

 

(Q2) Generate a plot which displays the cumulative distribution function for three Gaussian random variables X1 ∼ N(μ1, σ1(2)), X2 ∼ N(μ2, σ2(2)), and X3 ∼ N(μ3, σ3(2)) with μ1 = μ2 = μ3 = 1 and σ1(2) = 1, σ2(2) = 2, σ3(2) = 3.

(Q3) Generate a plot for the quantile function for the same three Gaussian distributions as above. Describe the relationship between the quantile function and the cumulative distribution function.

(Q4)

Now use “rnorm()” to generate a random independent and identically distributed sequence z1, ⋯ ,zn ∼ N(0,1) so that each zi ∼ N(0,1) has standard Gaussian distribution. Set n = 100. Make sure your code is reproducible by using the “set.seed()” function. Store your random sample in a vector called ““standardGaussianSample”” .

(Q5)

Suppose z ∼ N(0,1) is a Gaussian random variable. Take α, β ∈ ℝ and let W: Ω → ℝ be the random variable given by W = αz + β . Then W is also a Gaussian random variable. We will use this fact to create samples of Gaussian random variables from samples of standard Gaussian random variables.

Use your existing sample stored in “standardGaussianSample” to generate a new sample of the form y1, ⋯ , yn ∼ N(1,3) with expectation μ = 1 and population variance σ 2 = 3. The i-th observation in this sample should be of the form yi = α ⋅ zi + β, for appropriately chosen α, β ∈ ℝ, where zi is the i-th observation in the sample “standardGaussianSample”. Store the generated sample of y1, ⋯ , yn in a vector called “mean1Var3GaussianSampleA” . So to answer this question you need to decide the value of α and β, such that yi has the required expectation and variance.

(Q6)

Reset the random seed to the same value as the one you used in (Q4) using the “set.seed()” function and generate an i.i.d. sample of the form y1, ⋯ , yn ∼ N(1,3)   using the “rnorm()” function (instead of using “standardGaussianSample”). Store this sample in a vector called “mean1Var3GaussianSampleB” . Are the entries of the vectors “mean1Var3GaussianSampleA” and “mean1Var3GaussianSampleB” the same?

(Q7)

Now generate a graph which includes both a  for your sample “mean1Var3GaussianSampleA” and a plot of the population density (the probability density function) generated using “dnorm()” . You can also include two vertical lines which display respectively the population mean and the sample mean.

Some guidance for creating the plot: It would be helpful to look at the example provided in Section 3.3(Q4) of Assignment 5. You may want to use the “geom_density()” and “geom_vline()” functions. In particular, both “geom_density()” and “geom_line()” have an argument called “data” that you may want to explore.

Also, you can specify your own color by using the “scale_color_manual” and your own line type by “scale_linetype_manual” .

Your plot should look similar to the following:

 

(Q8) (*)

This is an optional question (*). If you are short on time you can work on the other questions first.

Recall that for a random variable X: Ω → ℝ is said to be Gaussian with expectation μ and variance σ 2 (i.e., X ∼ N(μ, σ 2)) if for any a, b ∈ ℝ, we have

Suppose Z ~ N(0,1) is a Gaussian random variable. Take α, β ∈ R and let W: Ω → R be the random variable given by W = αZ + β. In (Q5) we have assumed that W constructed in this way is a Gaussian random variable. Now, apply a change of   variables to show that W is a Gaussian random variable with expectation β and variance α 2.

Hint: can you derive the expression of ℙ(c ≤ W ≤ d)?

2. Location estimators with Gaussian data

In this question we compare two estimators for the population mean μ0 in a

Gaussian setting in which we have independent and identically distributed data X1, ⋯ ,Xn ∼ N(μ0, σ0(2)).

The following code generates a data frame consisting of the mean squared error of the sample median as an estimator of μ0.

set.seed(0)

num_trials_per_sample_size <- 1000

min_sample_size <- 30

max_sample_size <- 500

sample_size_inc <- 5

mu_0 <- 1

sigma_0 <- 3

# create data frame. of all pairs of sample_size and trial

simulation_df<-crossing(trial=seq(num_trials_per_sample_size),

sample_size=seq(min_sample_size,

max_sample_size,sample_size_inc)) %>%

# simulate sequences of Gaussian random variables

mutate(simulation=pmap(.l=list(trial,sample_size),

.f=~rnorm(.y,mean=mu_0,sd=sigma_0))) %>%

# compute the sample medians

mutate(sample_md=map_dbl(.x=simulation,.f=median)) %>%

group_by(sample_size) %>%

summarise(msq_error_md=mean((sample_md-mu_0)^2))

(Q1) Derive the mathematical expression for the population median of a Gaussian random variable xi ~ N(μ0, σ0(2)).

(Q2)

Modify the above code to include estimates of the mean square error of the sample mean. Your data frame. “simulation_df” should have a new column called “msq_error_mn” which estimates the mean squared error of the sample mean as an estimator of μ0.

Then generate a plot which includes both the mean square error of the sample mean and the sample median as a function of the sample size.

Your plot might look like the following:

 

3. (**) The law of large numbers and Hoeffding’s inequality

This is an optional question. . You can answer the other questions first before working on this one.

(Q1) Prove the following version of the weak law of large numbers.

Theorem (A law of large numbers). Let x: Ω → ℝ be a random variable with a

well-behaved expectation μ : = E(x) and variance σ 2 : = Var(x). Let x1, ⋯ ,xn: Ω → ℝ be a sequence of independent copies of x. Then for all E > 0,

You may want to begin by looking up the Chebyshev’s inequality: For any random variable z with finite expectation E(z) and variance Var(z), we have ℙ(|z − E(z)| ≥ t) ≤ Var(z)/t2 for any given number t > 0.

(Comparing the weak law of large numbers with Hoeffding’s inequality): Below is some further information about Hoeffding’s inequality. Hoeffding’s inequality is the following important result:

Theorem (Hoeffding). Let x: Ω → [0,1] be a bounded random variable with a well- behaved expectation μ : = E(x). Let x1, ⋯ ,xn: Ω → ℝ be a sequence of independent   copies of x. Then for all E > 0,

Please note that in the Hoeffding Theorem we additionally assume x to be bounded.

We can view Hoeffding’s inequality as a variant of the law of large numbers.

However, Hoeffding’s inequality gives us information about the rate of convergence. In particular, the sample average for bounded random variables converges exponentially fast to its expectation.

Hoeffding’s inequality is a precursor to Vapnik-Chervonekis theory which serves as a foundation for the theory of Statistical Machine Learning.