Theory of Stochastic Signals/Two-Dimensional Random Variables

1. Theory of Stochastic Signals
2. Random Variables with Statistical Dependence
3. Two-Dimensional Random Variables

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# OVERVIEW OF THE FOURTH MAIN CHAPTER #

$\Rightarrow \hspace{0.5cm}\text{We are just beginning the English translation of this chapter.}$

Now random variables with statistical bindings are treated and illustrated by typical examples. 

After the general description of two-dimensional random variables,  we turn to the auto-correlation function,  the cross-correlation function and the associated spectral functions  $($power density spectrum,  cross power density spectrum"$)$.

Specifically,  this chapter covers:

• the statistical description of  »two-dimensional random variables«  using the  »joint PDF«,
• the difference between  »statistical dependence«  and  »correlation«,
• the classification features  »stationarity«  and  »ergodicity«  of stochastic processes,
• the definitions of  »auto-correlation function«  $\rm (ACF)$  and  »power density spectrum«  $\rm (PDS)$,
• the definitions of  »cross-correlation function«  $\rm (CCF)$   and  »cross power spectral density«  $\rm (CPDS)$, 
• the numerical determination of all these variables in the two- and multi-dimensional case.

Properties and examples

As a transition to the  "correlation functions"  we now consider two random variables  $x$  and  $y$,  between which statistical dependences exist. 

Each of these two random variables can be described on its own with the introduced characteristic variables corresponding

• to the second main chapter   ⇒  Discrete Random Variables  
• and the third main chapter   ⇒   Continuous Random Variables.

$\text{Definition:}$  To describe the statistical dependences between two variables  $x$  and  $y$,  it is convenient to combine the two components into one  two-dimensional random variable  $(x, y)$.

• The individual components can be signals such as the real and imaginary parts of a phase modulated signal.
• But there are a variety of two-dimensional random variables in other domains as well,  as the following example will show.

$\text{Example 1:}$  The left diagram is from the random experiment  "Throwing two dice". 

Two examples of statistically dependent random variables

• Plotted to the right is the number of the first die  $(W_1)$, 
• plotted to the top is the sum  $S$  of both dice. 

The two components here are each discrete random variables between which there are statistical dependencies:

• If  $W_1 = 1$,  then the sum  $S$  can only take values between  $2$  and  $7$,  each with equal probability.
• In contrast,  for  $W_1 = 6$  all values between  $7$  and  $12$  are possible,  also with equal probability.

In the right diagram,  the maximum temperatures of the  $31$ days in May 2002 of Munich  (to the top)  and the mountain  "Zugspitze"  (to the right)  are contrasted.  Both random variables are continuous in value:

• Although the measurement points are about  $\text{100 km}$  apart,  and on the Zugspitze,  due to the different altitudes  $($nearly  $3000$  versus  $520$  meters$)$  is on average about  $20$  degrees colder than in Munich,  one recognizes nevertheless a certain statistical dependence between the two random variables  ${\it Θ}_{\rm M}$  and  ${\it Θ}_{\rm Z}$.
• If it is warm in Munich,  then pleasant temperatures are also more likely to be expected on the Zugspitze.  However,  the relationship is not deterministic:  The coldest day in May 2002 was a different day in Munich than the coldest day on the Zugspitze.

Joint PDF

We restrict ourselves here mostly to continuous random variables.  However, sometimes the peculiarities of two-dimensional discrete random variables are discussed in more detail.  Most of the characteristics previously defined for one-dimensional random variables can be easily extended to two-dimensional variables.

$\text{Definition:}$  The probability density function of the two-dimensional random variable at the location  $(x_\mu, y_\mu)$   ⇒   joint PDF  is an extension of the one-dimensional PDF  $(∩$  denotes logical AND operation$)$:

$$f_{xy}(x_\mu, \hspace{0.1cm}y_\mu) = \lim_{\left.{\delta x\rightarrow 0 \atop {\delta y\rightarrow 0} }\right. }\frac{ {\rm Pr}\big [ (x_\mu - {\rm \Delta} x/{\rm 2} \le x \le x_\mu + {\rm \Delta} x/{\rm 2}) \cap (y_\mu - {\rm \Delta} y/{\rm 2} \le y \le y_\mu +{\rm \Delta}y/{\rm 2}) \big] }{ {\rm \delta} \ x\cdot{\rm \Delta} y}.$$

$\rm Note$:

• If the 2D random variable is discrete, the definition must be slightly modified:
• For the lower range limits in each case, the "≤" sign must then be replaced by the "<" sign according to the page  CDF for discrete random variables 

.

Using this (joint) PDF  $f_{xy}(x, y)$  statistical dependencies within the two-dimensional random variable  $(x, y)$  are also fully captured in contrast to the two one-dimensional density functions   ⇒   marginal probability density functions:

$$f_{x}(x) = \int _{-\infty}^{+\infty} f_{xy}(x,y) \,\,{\rm d}y ,$$

$$f_{y}(y) = \int_{-\infty}^{+\infty} f_{xy}(x,y) \,\,{\rm d}x .$$

These two marginal density functions  $f_x(x)$  and  $f_y(y)$

• provide only statistical information about the individual components  $x$  and  $y$, respectively,
• but not about the bindings between them.

Two-dimensional CDF

$\text{Definition:}$  The  2D distribution function  like the 2D PDF, is merely a useful extension of the  one-dimensional distribution function  (CDF):

$$F_{xy}(r_{x},r_{y}) = {\rm Pr}\big [(x \le r_{x}) \cap (y \le r_{y}) \big ] .$$

The following similarities and differences between the 1D CDF and the 2D CDF emerge:

• The functional relationship between two-dimensional PDF and two-dimensional CDF is given by integration as in the one-dimensional case, but now in two dimensions.  For continuous random variables:

$$F_{xy}(r_{x},r_{y})=\int_{-\infty}^{r_{y}} \int_{-\infty}^{r_{x}} f_{xy}(x,y) \,\,{\rm d}x \,\, {\rm d}y .$$

• Inversely, the probability density function can be given from the distribution function by partial differentiation to  $r_{x}$  and  $r_{y}$  :

$$f_{xy}(x,y)=\frac{{\rm d}^{\rm 2} F_{xy}(r_{x},r_{y})}{{\rm d} r_{x} \,\, {\rm d} r_{y}}\Bigg|_{\left.{r_{x}=x \atop {r_{y}=y}}\right.}.$$

• Relative to the distribution function  $F_{xy}(r_{x}, r_{y})$  the following limits apply:

$$F_{xy}(-\infty,-\infty) = 0,$$

$$F_{xy}(r_{\rm x},+\infty)=F_{x}(r_{x} ),$$

$$F_{xy}(+\infty,r_{y})=F_{y}(r_{y} ) ,$$

$$F_{xy} (+\infty,+\infty) = 1.$$

• In the limiting case  $($infinitely large  $r_{x}$  and  $r_{y})$  Thus, for the 2D CDF, the value  $1$.  From this, we obtain the  normalization condition  for the 2D PDF:

$$\int_{-\infty}^{+\infty} \int_{-\infty}^{+\infty} f_{xy}(x,y) \,\,{\rm d}x \,\,{\rm d}y=1 .$$

$\text{Conclusion:}$  Note the significant difference between one-dimensional and two-dimensional random variables:

• For one-dimensional random variables, the area under the PDF always yields the value  $1$.
• For two-dimensional random variables, the PDF volume is always equal  $1$.

PDF and CDF for statistically independent components

For statistically independent components  $x$  and  $y$  the following holds for the joint probability according to the elementary laws of statistics if  $x$  and  $y$  are continuous in value:

$${\rm Pr} \big[(x_{\rm 1}\le x \le x_{\rm 2}) \cap( y_{\rm 1}\le y\le y_{\rm 2})\big] ={\rm Pr} (x_{\rm 1}\le x \le x_{\rm 2}) \cdot {\rm Pr}(y_{\rm 1}\le y\le y_{\rm 2}) .$$

For this, independent components can also be written:

$${\rm Pr} \big[(x_{\rm 1}\le x \le x_{\rm 2}) \cap(y_{\rm 1}\le y\le y_{\rm 2})\big] =\int _{x_{\rm 1}}^{x_{\rm 2}}f_{x}(x) \,{\rm d}x\cdot \int_{y_{\rm 1}}^{y_{\rm 2}} f_{y}(y) \, {\rm d}y.$$

$\text{Definition:}$  It follows that for  statistical independence  the following condition must be satisfied with respect to the 2D probability density function:

$$f_{xy}(x,y)=f_{x}(x) \cdot f_y(y) .$$

$\text{Example 2:}$  In the graph, the instantaneous values of a two-dimensional random variable are plotted as points in the  $(x, y)$–plane.

• Ranges with many points, which accordingly appear dark, indicate large values of the 2D PDF  $f_{xy}(x, y)$.
• In contrast, the random variable  $(x, y)$  has relatively few components in rather bright areas.

Statistically independent components:  $f_{xy}(x,y)$, $f_{x}(x)$  and $f_{y}(y)$

The graph can be interpreted as follows:

• The marginal probability densities  $f_{x}(x)$  and  $f_{y}(y)$  already indicate that both  $x$  and  $y$  are Gaussian and zero mean, and that the random variable  $x$  has a larger standard deviation than  $y$  .
• $f_{x}(x)$  and  $f_{y}(y)$  however, do not provide information on whether or not statistical bindings exist for the random variable  $(x, y)$ .
• However, using the 2D PDF  $f_{xy}(x,y)$  one can see that there are no statistical bindings between the two components  $x$  and  $y$  here.
• With statistical independence, any cut through  $f_{xy}(x, y)$  parallel to  $y$-axis yields a function that is equal in shape to the edge PDF  $f_{y}(y)$.  Similarly, all cuts parallel to  $x$-axis are equal in shape to  $f_{x}(x)$.

• This fact is equivalent to saying that in this example  $f_{xy}(x, y)$  can be represented as the product of the two marginal probability densities:   $f_{xy}(x,y)=f_{x}(x) \cdot f_y(y) .$

PDF and CDF for statistically dependent components

If there are statistical bindings between  $x$  and  $y$, then different cuts parallel to  $x$– and  $y$–axis, respectively, yield different, non-shape equivalent functions.  In this case, of course, the joint PDF cannot be described as a product of the two (one-dimensional) marginal probability densities either.

Statistically dependent components:  $f_{xy}(x,y)$, $f_{x}(x)$,  $f_{y}(y)$

$\text{Example 3:}$  The graph shows the instantaneous values of a two-dimensional random variable in the  $(x, y)$–plane, where now, unlike  $\text{Example 2}$  there are statistical bindings between  $x$  and  $y$  .

• The 2D random variable takes all 2D values with equal probability in the parallelogram drawn in blue.
• No values are possible outside the parallelogram.

One recognizes from this representation:

• Integration over $f_{xy}(x, y)$  parallel to  $x$–axis leads to the triangular marginal density $f_{y}(y)$, integration parallel to  $y$–axis to the trapezoidal PDF$f_{x}(x)$.
• From the 2D PDF$f_{xy}(x, y)$  it can already be guessed that for each  $x$–value on statistical average a different  $y$–value is to be expected.
• This means that here the components  $x$  and  $y$  are statistically dependent on each other.

Expected values of two-dimensional random variables

A special case of statistical dependence is correlation.

$\text{Definition:}$  Under  correlation  one understands a linear dependence  between the individual components  $x$  and  $y$.

• Correlated random variables are thus always also statistically dependent.
• But not every statistical dependence implies correlation at the same time

.

To quantitatively capture correlation, one uses various expected values of the 2D random variable  $(x, y)$.

These are defined analogously to the one-dimensional case.

• according to  Chapter 2  (for discrete value random variables).
• bzw.  Chapter 3  (for continuous value random variables):

$\text{Definition:}$  For the (non-centered)  moments  the relation holds:

$$m_{kl}={\rm E}\big[x^k\cdot y^l\big]=\int_{-\infty}^{+\infty}\hspace{0.2cm}\int_{-\infty}^{+\infty} x\hspace{0.05cm}^{k} \cdot y\hspace{0.05cm}^{l} \cdot f_{xy}(x,y) \, {\rm d}x\, {\rm d}y.$$

Thus, the two linear means are  $m_x = m_{10}$  and  $m_y = m_{01}.$

$\text{definition:}$  The  $m_x$  and  $m_y$  related  central moments  respectively are:

$$\mu_{kl} = {\rm E}\big[(x-m_{x})\hspace{0.05cm}^k \cdot (y-m_{y})\hspace{0.05cm}^l\big] .$$

In this general definition equation, the variances  $σ_x^2$  and  $σ_y^2$  of the two individual components are included by  $\mu_{20}$  and  $\mu_{02}$  respectively.

$\text{Definition:}$  Of particular importance is the  covariance  $(k = l = 1)$, which is a measure of the linear statistical dependence  between the random variables  $x$  and  $y$  :

$$\mu_{11} = {\rm E}\big[(x-m_{x})\cdot(y-m_{y})\big] = \int_{-\infty}^{+\infty} \int_{-\infty}^{+\infty} (x-m_{x}) \cdot (y-m_{y})\cdot f_{xy}(x,y) \,{\rm d}x \, {\rm d}y .$$

In the following, we also denote the covariance  $\mu_{11}$  in part by  $\mu_{xy}$, if the covariance refers to the random variables  $x$  and  $y$ 

Notes:

• The covariance  $\mu_{11}=\mu_{xy}$  is related to the non-centered moment $m_{11} = m_{xy} = {\rm E}\big[x \cdot y\big]$ as follows:

$$\mu_{xy} = m_{xy} -m_{x }\cdot m_{y}.$$

• This equation is enormously advantageous for numerical evaluations, since  $m_{xy}$,  $m_x$  and  $m_y$  can be found from the sequences  $〈x_v〉$  and  $〈y_v〉$  in a single run.
• On the other hand, if one were to calculate the covariance  $\mu_{xy}$  according to the above definition equation, one would have to find the mean values  $m_x$  and  $m_y$  in a first run and could then only calculate the expected value  ${\rm E}\big[(x - m_x) \cdot (y - m_y)\big]$  in a second run.

Example 2D expected values

$\text{Example 4:}$  In the first two rows of the table, the respective first elements of two random sequences  $〈x_ν〉$  and  $〈y_ν〉$  are entered.  In the last row, the respective products  $x_ν - y_ν$  are given.

• By averaging over the ten sequence elements in each case, one obtains 

$$m_x =0.5,\ \ m_y = 1, \ \ m_{xy} = 0.69.$$

• This directly results in the value for the covariance:

$$\mu_{xy} = 0.69 - 0.5 · 1 = 0.19.$$

Without knowledge of the equation  $\mu_{xy} = m_{xy} - m_x\cdot m_y$  one would have had to first determine the mean values  $m_x$  and  $m_y$  in the first run,
in order to then determine the covariance  $\mu_{xy}$  as the expected value of the product of the zero mean variables in a second run.

Correlation coefficient

With statistical independence of the two components  $x$  and  $y$  the covariance  $\mu_{xy} \equiv 0$.  This case has already been considered in  $\text{Example 2}$  on the  PDF and CDF for statistically independent components  page.

• But the result  $\mu_{xy} = 0$  is also possible for statistically dependent components  $x$  and  $y$  namely when they are uncorrelated, i.e.  linearly independent .
• The statistical dependence is then not of first order, but of higher order, for example corresponding to the equation  $y=x^2.$

One speaks of  complete correlation when the (deterministic) dependence between  $x$  and  $y$  is expressed by the equation  $y = K · x$  . Then the covariance is given by:

• $\mu_{xy} = σ_x · σ_y$  with positive value of  $K$,
• $\mu_{xy} = - σ_x · σ_y$  with negative  $K$–value.

Therefore, instead of covariance, one often uses the so-called correlation coefficient as a descriptive variable.

$\text{Definition:}$  The  correlation coefficient  is the quotient of the covariance  $\mu_{xy}$  and the product of the rms values  $σ_x$  and  $σ_y$  of the two components:

$$\rho_{xy}=\frac{\mu_{xy} }{\sigma_x \cdot \sigma_y}.$$

The correlation coefficient  $\rho_{xy}$  has the following properties:

• Because of normalization,   $-1 \le ρ_{xy} ≤ +1$ always holds.
• If the two random variables  $x$  and  $y$  are uncorrelated, then  $ρ_{xy} = 0$.
• For strict linear dependence between  $x$  and  $y$  is  $ρ_{xy}= ±1$   ⇒   complete correlation.
• A positive correlation coefficient means that when  $x$ is larger, on statistical average  $y$  is also larger than when  $x$ is smaller.
• In contrast, a negative correlation coefficient expresses that  $y$  becomes smaller on average as  $x$  increases.

Gaussian 2D PDF with correlation

$\text{Example 5:}$  The following conditions apply:

• The considered components  $x$  and  $y$  each have a Gaussian PDF.
• The two standard deviations are different  $(σ_y < σ_x)$.
• The correlation coefficient is  $ρ_{xy} = 0.8$.

Unlike the  Example 2  with statistically independent components   ⇒   $ρ_{xy} = 0$  $($drotz  $σ_y < σ_x)$  one recognizes that here with larger  $x$-value on statistical average also  $y$  is larger than with smaller  $x$.

Correlation line

Gaussian 2D PDF with correlation line

$\text{Definition:}$  A  correlation line  is the straight line  $y = K(x)$  in the  $(x, y)$–plane through the "midpoint"  $(m_x, m_y)$. Sometimes this straight line is also called  regression line .

The correlation line has the following properties:

• The mean square deviation(error???) from this straight line - viewed in  $y$–direction and averaged over all  $N$  points - is minimal:

$$\overline{\varepsilon_y^{\rm 2} }=\frac{\rm 1}{N} \cdot \sum_{\nu=\rm 1}^{N}\; \;\big [y_\nu - K(x_{\nu})\big ]^{\rm 2}={\rm minimum}.$$

• The correlation straight line can be interpreted as a kind of  "statistical symmetry axis" . The equation of the straight line is:

$$y=K(x)=\frac{\sigma_y}{\sigma_x}\cdot\rho_{xy}\cdot(x - m_x)+m_y.$$

The angle taken by the correlation line to the  $x$–axis is:

$$\theta_{y\hspace{0.05cm}\rightarrow \hspace{0.05cm}x}={\rm arctan}\ (\frac{\sigma_{y} }{\sigma_{x} }\cdot \rho_{xy}).$$

By this nomenclature it should be made clear that we are dealing here with the regression of  $y$  on  $x$  .

• The regression in the opposite direction - that is, from  $x$  to  $y$ - on the other hand, means the minimization of the mean square deviation in  $x$ direction.

• The interactive applet  Correlation Coefficient and Regression Line  illustrates that in general  $($if  $σ_y \ne σ_x)$  for the regression of  $x$  on  $y$  will result in a different angle and thus a different regression line:

$$\theta_{x\hspace{0.05cm}\rightarrow \hspace{0.05cm} y}={\rm arctan}\ (\frac{\sigma_{x}}{\sigma_{y}}\cdot \rho_{xy}).$$

Exercises for the chapter

Exercise 4.1: Triangular (x, y) Area

Exercise 4.1Z: Appointment to Breakfast

Exercise 4.2: Triangle Area again

Exercise 4.2Z: Correlation between "x" and "e to the Power of x"

Exercise 4.3: Algebraic and Modulo Sum

Exercise 4.3Z: Dirac-shaped 2D PDF