I have two sets of deterministic numbers, collected in the two

  • Thread starter Thread starter PAHV
  • Start date Start date
  • Tags Tags
    Numbers Sets
Click For Summary
The discussion revolves around testing a hypothesis regarding two sets of deterministic numbers, x and y, which are assumed to be stochastic. The user questions whether they can set up a hypothesis test comparing the probability density functions (pdfs) of x and y, specifically H0: f(x)=f(y) versus H1: Not H0. However, it is noted that the current assumptions about x and y being stochastic and the definition of f(.) as a pdf are insufficient to establish a specific probability model. The conversation emphasizes the need for a more detailed description of how to simulate the data to effectively conduct the hypothesis test. Overall, clarity in defining the stochastic nature of the data is crucial for proper analysis.
PAHV
Messages
8
Reaction score
0
I have two sets of deterministic numbers, collected in the two vectors: x=[x(1),...,x(n)] and y=[y(1),...,y(n)]. My (determinstic) theory says that x(i)=y(i) for all i=1,...,n. But instead, I want to assume that the numbers x and y are stochastic. If we let f(.) be a pdf, does this mean that I can test a stochastic version of my theory by setting up a hypothesis test H0: f(x)=f(y) vs. H1: Not H0? That is, is it true that x=y <=> f(x)=f(y) if f is a pdf?
 
Physics news on Phys.org


PAHV said:
I want to assume that the numbers x and y are stochastic. If we let f(.) be a pdf

To do a hypothesis test, you must assume enough information in the hypothesis H0 to be able to compute the probability that the observed data happened. Your statements that "x and y are stochastic" and that we let "f(.) be a pdf" are not sufficient to describe a specific probability model for the data. Think of trying to write a stochastic simulation for the data. What you have said isn't sufficient information to give to a programmer who is supposed to write the simulation. Can you describe how you would simulate the data in a computer program?
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

Similar threads

  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 9 ·
Replies
9
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 8 ·
Replies
8
Views
2K
  • · Replies 13 ·
Replies
13
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 30 ·
2
Replies
30
Views
4K