A Quick Multiple choice for marginal density function

In summary, the marginal density f(y), defined as ∫f(x,y)dx, cannot be classified according to its increasing or decreasing character without more information. This is because the support for the joint continuous probability density is a triangle, and we do not know how the joint density behaves on that support. Therefore, it is neither an increasing nor a decreasing function.
  • #1
Askhwhelp
86
0
Given a plane with three points, (0,−1), (2,0), and (0,1) with x-axis and y-axis connecting three points to make a triangle. Suppose this triangle represents the support for a joint continuous probability density Pick one of the following:

The marginal density f(y), defined as ∫f(x,y)dx.

i) is an increasing function

ii) is a decreasing function

iii) is neither an increasing or decreasing function

iv) cannot be classified according to its increasing or decreasing character without more
information.

Explain you choice

I pick (iii) when the marginal density function begins at y=-1, it must increase at some point before y=1 since the PDF has to be able to integrate to 1 ..on the other than when it got to y=1 ...it must decrease back to 0...so the marginal density function f(y) could not be increasing or decreasing function right...therefore, the answer would be neither increasing nor decreasing
 
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  • #2
Askhwhelp said:
Suppose this triangle represents the support for a joint continuous probability density
Not sure what that means. Does it mean that the joint density function is uniform over that triangle and zero everywhere else?
 
  • #3
haruspex said:
Not sure what that means. Does it mean that the joint density function is uniform over that triangle and zero everywhere else?

the triangle is just the support which is the 2D plane (x- and y-axis), we do not know what the joint density behaves
 

1. What is a marginal density function?

A marginal density function is a mathematical function that describes the probability distribution of a subset of variables in a larger multivariable system. It is obtained by integrating the joint density function over all possible values of the other variables.

2. How is a marginal density function different from a joint density function?

A joint density function describes the probability distribution of all variables in a multivariable system, while a marginal density function only describes the distribution of a subset of variables. The joint density function includes information about the relationships between the variables, while the marginal density function does not.

3. What does the shape of a marginal density function indicate?

The shape of a marginal density function can indicate the general behavior and characteristics of the variables it describes. For example, a symmetric bell-shaped curve may indicate a normal distribution, while a skewed curve may indicate a non-normal distribution.

4. How is a marginal density function used in statistical analysis?

A marginal density function can be used to calculate probabilities and make predictions about the behavior of individual variables in a multivariable system. It can also be used to compare the distributions of different subsets of variables within the system.

5. What are some real-life applications of marginal density functions?

Marginal density functions are widely used in various fields of science, including economics, biology, and engineering. They are often used to analyze and model complex systems with multiple variables, such as stock market trends, population dynamics, and weather patterns.

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