Top Page | Upper Page | Contents | About This Site | JAPANESE

Proportional variance model data structure

The simplest form of the mathematical model of Proportional variance is the equation :
Y = E * X

The next simplest form is the following equation:
Y = (a + E) * X

The meaning of this formula is "The product of the cause X and the error is Y". In the world, if you use a scatter plot of Y and X with a high correlation, the larger the X, the greater the variation in the Y direction.

Once you know this structure, it will be one of the Structure of data that becomes a directed graph.
proportional regression

The following story about using skewness and kurtosis to identify the structure of the Proportional variance model is the author's idea. There may be previous research in the world. If you know of such literature, I would appreciate it if you could tell me.

How to extract the structure of Y = E * X from the data (between two variables)

If E is normally or uniformly distributed, E is symmetrical. 1/E is asymmetrical. The more asymmetric it is, the greater the absolute skewness. Take advantage of this property.

Specifically, when there are two variables, S and T, S / T and T are calculated, and skewness is calculated for each. For example, if the skewness of T/S is -0.20 and the skewness of S/T is 0.94, you can estimate
T = (a + E) * S
proportional regression proportional regression proportional regression

The structure is represented by arrows as shown below.
proportional regression

How to extract the structure of Y = E * X from the data (between the three variables)

Suppose there are three variables with the relationship
T = (a1 + E1) * S
U = (a2 + E2) * T

Two steps are required to identify the structure between the three variables.

Stage 1: Determination of orientation between two variables (use of skewness)

Using the method for 2 variables, calculating the skewness will look like the graph below.
proportional regression

For S and T, S and U, and T and U, you can see the direction of the arrows in each, and if you express them as they are with arrows, they will be down. Judging by the skewness alone, this is what happens.
proportional regression

Here, too, between S and U, there is an arrow.

Stage 2: Selection of arrows (utilization of kurtosis)

First, make sure it's happening between S and U.
U = (a2 + E2) * (a1 + E1) * S
The relationship between you and S is as shown above.
The part
(a2 + E2) * (a1 + E1)
multiplied by S is the product of normal distributions, but this is a symmetric distribution to determine the direction of the arrows between S and U.

This is where kurtosis is used. The graph below shows the kurtosis of the normal distribution. We calculate using 10,0 samples. Regardless of the magnitude of the standard deviation, we can see that it is almost 0.
proportional regression

Also, it is the product of normal distributions, As shown below, if there are two normal distributions, Z1 and Z2, the graph of the variable Z1*Z2 is more pointed than Z1 and Z2. You can see the difference in this sharpness by the kurtosis.
proportional regression

Use the kurtosis properties described above. The kurtosis is calculated as shown in the graph below. From this graph, we can see that you and S seem to contain the product relationship between normal distributions, and the arrows between you and S can be removed.
proportional regression proportional regression

If the structure of Y = E * X cannot be extracted from the data

When E is not symmetrical

There is an assumption that E is symmetrical. Even if the larger X is, the greater the variation in the Y direction, this is not possible if E is not symmetrical.

Therefore, it is better to make a histogram for both E and 1/E to check it.

For models with Y = X + E or close to it

The structure of the data that becomes a regression model, that is, if Y = X + E, it cannot be determined by skewness because Y/X is asymmetric.

In real data, the structure of the data that results in the regression model and the data structure of the Proportional variance model can be difficult to distinguish.

When two variables affect one variable

In the case of 3 variables, the above method can be examined at such times.
proportional regression

If two variables affect one variable, that is, when it is as shown below, it cannot be examined.
proportional regression

NEXT Time Difference of Cause-Effect