LearningActivities CauchyTGaussian

From Socr

(Difference between revisions)

Jump to: navigation, search

Revision as of 20:42, 25 October 2011

Distributome Learning Activities - Distributome Activity on the relations between Cauchy, Student's T and Gaussian Distributions

Overview

The relation between Cauchy and Gaussian distributions

This activity illustrates the inter-distribution relationships between Cauchy, Student's T and Standard Normal (Gaussian) distributions.

Surveys about public opinions on controversial social issues are becoming increasingly frequent as topics such as the legalization of marijuana, abortion policy, marriage rights for homosexuals, and immigration policy are hotly debated in the media. For example, both the Opinion Research Corporation (polling for CNN) and the Pew Research Center for The People and The Press conducted surveys of American adults in spring of 2011 to estimate the percentage of the public that favors the legalization of marijuana. The sample sizes in the two polls were 824 for the Opinion Research Corporation poll and 1504 for the Pew poll.

Goals

TBD

Hands-on Activity

In this activity you may assume that both of these pollsters use similar techniques that involve telephone interviews and weighting the answers given by individuals to align the respondents demographics with population values and finally averaging to produce unbiased and essentially normally distributed estimates.

The Pew poll had almost twice the sample size of the Opinion Research Corporation poll. What is the chance that it was more accurate than that poll for estimating p = the percentage of American adults that favored the legalization of marijuana in spring, 2011? Be sure to clearly define how you are interpreting “more accurate.” Also state and justify any assumptions you make in solving for this probability.

See a Hint

Describe how you would combine the data from these two polls to form a single estimate of $p$ .

What is the correlation between your estimate above and the individual estimate produced by the Pew poll?

What is the probability that your combined estimate from part b) is more accurate than the estimate based only on the Pew poll?

Note: The questions above may be appropriate for an undergraduate course in probability. The last part would be more appropriate for masters level course (and should have a General Cauchy distribution tag and a tag for its relationship to the bivariate normal).

Solutions

Part 1

We want the probability that the Pew estimate based on a sample size of $n 1 = 1504$ , which comes closer to the true value of $p$ than the Opinion Research poll based on $n 2 = 824$ respondents.

If $\hat{p_1}$ = the estimate of $p$ from Pew, and $\hat{p_2}$ = the estimate of $p$ from Opinion Research.

See a Solution to First Problem: Step 1

See a Solution to First Problem: Step 2

See a Solution to First Problem: Step 3

See a Solution to First Problem: Step 4

See a Solution to First Problem: Step 5

Alternative approaches

Alternative 1:

See a Solution to First Problem: Alternative 1

Alternative 2:

See a Solution to First Problem: Alternative 2

Part 2

The obvious choice is to propose a linear combination of the two estimates weighting inversely proportional to the variances to get the smallest overall variance amongst such linear combinations:

$\hat{p}=\frac{1504}{2328}\hat{p_1}+\frac{824}{2328}\hat{p_2}$ .

Of course, this is just the estimate that comes from combining the samples into one large sample of $n = 2328$ and has variance $\frac{\sigma^2}{2328}$ .

Part 3

Note that $Cov(\hat{p},\hat{p_1})=Cov(\frac{1504}{2328}\hat{p_1}+\frac{824}{2328}\hat{p_2}, \hat{p_1})=\frac{1504}{2328}\sigma_1^2=\frac{\sigma^2}{2328}$ . Thus, $Corr(\hat{p},\hat{p_1})=\frac{\sigma^2/2328}{\sqrt{\sigma^2(\frac{1504}{2328^2}+\frac{824}{2328^2})}\sqrt{\frac{\sigma^2}{1504}}}=\sqrt{\frac{1504}{2328}}=0.8038.$ .

Part 4

d) We want $P[|\hat{p}-p| < |\hat{p_1}-p| ]$ . Taking $X=\hat{p}-p$ and $Y=\hat{p_1}-p$ , then $X\sim N(0, \frac{\sigma^2}{2328})$ , $Y\sim N(0, \frac{\sigma^2}{1504})$ and $C o r r (X, Y) = 0.8038$ .

Hence, $P(|X| < |Y|) = P(-1< \frac{X}{Y} < 1) = G(1) - G(-1)$ , where G is the CDF of the generalized Cauchy distribution (see below):

$G(u) = 0.5 +\frac{1}{\pi}\arctan(\frac{u-\rho\frac{\sigma_X}{\sigma_Y}}{\frac{\sigma_X}{\sigma_Y}\sqrt{1-\rho^2}})$ .

So the answer is $\frac{1}{\pi}(\arctan(\sqrt{\frac{824}{1504}}) -\arctan(\frac{-3832}{\sqrt{1504 \times 824}}))\approx 0.613$ .

Generalized Cauchy distribution CDF derivation

To derive the generalized Cauchy distribution CDF directly, we start with the bivariate normal distribution of X and Y:

$f(x,y) =\frac{1}{\pi\sigma_x\sigma_y\sqrt{1-\rho^2}} exp(-\frac{(x/\sigma_x)^2-2\rho xy/(\sigma_x\sigma_y)+(y/\sigma_y)^2}{2(1-\rho^2)})$ .

Then the pdf of $U=\frac{X}{Y}$ is:

$g(u) = \int_{-\infty}{\infty}{|y|f(uy,y)dy}= \frac{1}{\pi\sigma_x\sigma_y\sqrt{1-\rho^2}} \int_0^{\infty} {yexp(-y^2\frac{(u/\sigma_x)^2-2\rho uy/(\sigma_x\sigma_y)+(1/\sigma_y)^2}{2(1-\rho^2)})dy}$ .

Since $\int{0^{\infty}{y exp(-ay^2)dy}=\frac{1}[2a}$ , we find that

$g(u)=\frac{1}{\pi\sigma_x\sigma_y\sqrt{1-\rho^2}} (\frac{1-\rho^2}{(u/ \sigma_x)^2 - 2\rho u / (\sigma_x\sigma_y) +(1/\sigma_y)^2})=$

$=\frac{1}{\pi}\frac{\frac{\sigma_x}{\sigma_y}\sqrt{1-\rho^2}}{u^2-2\rho u \frac{\sigma_x}{\sigma_y} (\frac{\sigma_x}{\sigma_y})^2}.$

Thus, $g(u)=\frac{1}{\pi}\frac{\frac{\sigma_x}{\sigma_y}\sqrt{1-\rho^2}}{(u-\rho \sigma_x / \sigma_y)^2 +(\sigma_x / \sigma_y)^2(1-\rho^2)}.$

Finally, we know that $\int_0^y {\frac{b}{(u-m)^2+b^2}du} = \arctan(\frac{y-m}{b})$

Therefore, the generalized Cauchy distribution CDF is:

$G(u) = 0.5 +\frac{1}{\pi}\arctan(\frac{u-\rho\frac{\sigma_X}{\sigma_Y}}{\frac{\sigma_X}{\sigma_Y}\sqrt{1-\rho^2}})$ .

Cauchy, Student's T and Gaussian distribution interrelations

The Student's T-distribution represents a one-parameter homotopy path connecting Cauchy and Gaussian Distribution:

$Cauchy=T_{(df=1)} \longrightarrow T_{(df)}\longrightarrow N(0,1)=T_{(df=\infty)}$ .

See the Cauchy, Student's T and Gaussian distribution calculators.

Explore the relations between these and many other probability distributions using the interactive graphical Distributome Navigator.

Conclusions

TBD

Translate this page:

(default)	Deutsch	Español	Français	Italiano	Português	日本語	България	الامارات العربية المتحدة	Suomi	इस भाषा में	Norge
한국어	中文	繁体中文	Русский	Nederlands	Ελληνικά	Hrvatska	Česká republika	Danmark	Polska	România	Sverige

@@ Line 48: / Line 48: @@
 <math>= P(-\sqrt{0.548}< \frac{Z_1}{Z_2} < \sqrt{0.548})</math>, where <math>Z_1</math> and <math>Z_2</math> are standard normal variables, and their ratio is the standard Cauchy that can then be used to get the correct numerical answer.}}
-* Alteranative 2:
+* Alternative 2:
 {{hidden|See a Solution to First Problem: Alternative 2| A second, more cumbersome, alternative is to work directly to find the marginal distribution of <math>\frac{X}{Y}</math> from the bivariate normal – see solution to the last part below with <math>\rho=0</math>.}}
@@ Line 68: / Line 68: @@
 : <math>G(u) = 0.5 +\frac{1}{\pi}\arctan(\frac{u-\rho\frac{\sigma_X}{\sigma_Y}}{\frac{\sigma_X}{\sigma_Y}\sqrt{1-\rho^2}})</math>.
 :: So the answer is <math>\frac{1}{\pi}(\arctan(\sqrt{\frac{824}{1504}}) -\arctan(\frac{-3832}{\sqrt{1504 \times 824}}))\approx 0.613</math>.
 ===Generalized Cauchy distribution CDF derivation===