How to convert a univariate distribution to bivariate distribution

In summary, the conversation discusses how to convert a univariate distribution to a bivariate one, specifically using the MARSHAL-OLKIN exponential weibull distribution. The joint PDF of two independent random variables can be obtained by multiplying their individual PDFs. Another option is to mimic the bivariate normal distribution, where the variables are not independent but related in a specific way. The conversation also mentions the parameters lambda, beta, alpha, and k, and the use of the indicator function I(0,inf). The goal is to write the distribution in product form with the location parameter (alpha) varying.
  • #1
samrah
3
0
hi
i have MARSHAL-OLKIN exponential weibull distribution which have the following cdf and pdf..
how could i convert it to bivariate distribution?
thanks
 
Physics news on Phys.org
  • #2
The joint PDF of two independent random variables would just be the product of the two individual PDFs.
 
  • Like
Likes samrah
  • #3
FactChecker said:
The joint PDF of two independent random variables would just be the product of the two individual PDFs.
thanks.. i have cdf and pdf (latex codes are given)..how can i make bivariate cdf from this univariate? what is shift parameter in this case? kindly guide me

thanks alot

\begin{align} \label{A5}
F(x) &= \frac{1- e^{-\left(\lambda\, x+\beta\, x^k\right)}}{1-(1-\alpha)\,
e^{-\left(\lambda\, x+\beta\, x^k\right)}}\cdot \boldsymbol I_{(0, \infty)}(x)\,,\\ \label{A6}
f(x) &= \frac{\alpha\,\left(\lambda+ \beta \,k\,x^{k-1}\right)\, e^{-\lambda\,x-\beta\,x^k}}
{\left(1-(1-\alpha )\, e^{-\left(\lambda\, x+\beta\, x^k\right)}\right)^2} \cdot \boldsymbol I_{(0, \infty)}(x)\,,
\qquad \lambda, \beta, k, \alpha > 0 \,;
\end{align}
 
  • #4
You need to decide how you want the two variables to be related. It's easy to determine the joint PDF if they are independent -- just multiply them. Alternatively, you may want to mimic the bivariate normal, where the X and Y variables are not independent, but the vector distance from a center point (mean) is in the same class of distributions of the original distribution. For that, you may want to look at how the two coordinate vectors are related in a bivariate normal (see http://mathworld.wolfram.com/BivariateNormalDistribution.html )

PS. My description of the bivariate normal in terms of a vector "distance" is a loose description, not to be taken literally.
 
  • Like
Likes samrah
  • #5
samrah said:
thanks.. i have cdf and pdf (latex codes are given)..how can i make bivariate cdf from this univariate? what is shift parameter in this case? kindly guide me

thanks alot

\begin{align} \label{A5}
F(x) &= \frac{1- e^{-\left(\lambda\, x+\beta\, x^k\right)}}{1-(1-\alpha)\,
e^{-\left(\lambda\, x+\beta\, x^k\right)}}\cdot \boldsymbol I_{(0, \infty)}(x)\,,\\ \label{A6}
f(x) &= \frac{\alpha\,\left(\lambda+ \beta \,k\,x^{k-1}\right)\, e^{-\lambda\,x-\beta\,x^k}}
{\left(1-(1-\alpha )\, e^{-\left(\lambda\, x+\beta\, x^k\right)}\right)^2} \cdot \boldsymbol I_{(0, \infty)}(x)\,,
\qquad \lambda, \beta, k, \alpha > 0 \,;
\end{align}
what is lambda, Beta, alpha, what is I(0, inf) is k the other variable with x. Is this an advanced question, what's I(0,inf)?
 
  • Like
Likes samrah
  • #6
Josh S Thompson said:
what is lambda, Beta, alpha, what is I(0, inf) is k the other variable with x. Is this an advanced question, what's I(0,inf)?
alpha,beta ,lambda and gamma are just parameters... i have to fix three and vary one of them (the location or shift parameter)... i found that alpha is the location parameter...now i have to write these in product form with same this pdf ,beta,lambda and gamma will be fixed and alpha will vary.i don't know about (0,inf)
 

FAQ: How to convert a univariate distribution to bivariate distribution

What is the difference between univariate and bivariate distributions?

Univariate distributions describe the probability of a single variable, while bivariate distributions describe the joint probability of two variables.

Why would someone want to convert a univariate distribution to bivariate?

Converting a univariate distribution to bivariate allows for the analysis of the relationship between two variables and can provide more information about the data.

How do you convert a univariate distribution to bivariate?

To convert a univariate distribution to bivariate, you can use statistical methods such as correlation analysis, regression analysis, or multivariate techniques like principal component analysis.

Are there any assumptions that need to be met when converting a univariate distribution to bivariate?

Yes, there are several assumptions that need to be met, depending on the specific method used for conversion. These may include normality of the data, linearity of the relationship between variables, and independence of the variables.

Can a bivariate distribution be converted back to a univariate distribution?

No, once a bivariate distribution has been created, the conversion back to a univariate distribution is not possible. However, the two variables can still be analyzed separately as individual univariate distributions.

Back
Top