Just wondering whether anyone has any ideas on this:
I have a matrix filled with data. Every row of the matrix has has a curve fitted formula that best fits the data located in that row.
Every row (and it's accompanying curve fitted formula) has a relation to the the curve fitted formulas to the rows adjacent.
Now, due to data scarcity and the inherent 'noise' from the data in each row, I have curve fitted formulas that sometimes wildly overlap one another. However in theory, these related curve fitting formulas should be exclusive but complementary of each other (no 'overlap').
So in essence, I need to create a series of curve fitted formulas that are exclusive of each other, yet 'snugly' fit above and below each other with limited and noisy data.
I hope I've described this well enough to envision what I am trying to say here.
Does anyone have any thoughts on this? Is there an 'easy' way to create a stable set of exclusive but complementary curve fitted formulas?
Thanks in advance.
Edit: I feel like I need to 'curve fit the curve fits' somehow.
I have a matrix filled with data. Every row of the matrix has has a curve fitted formula that best fits the data located in that row.
Every row (and it's accompanying curve fitted formula) has a relation to the the curve fitted formulas to the rows adjacent.
Now, due to data scarcity and the inherent 'noise' from the data in each row, I have curve fitted formulas that sometimes wildly overlap one another. However in theory, these related curve fitting formulas should be exclusive but complementary of each other (no 'overlap').
So in essence, I need to create a series of curve fitted formulas that are exclusive of each other, yet 'snugly' fit above and below each other with limited and noisy data.
I hope I've described this well enough to envision what I am trying to say here.
Does anyone have any thoughts on this? Is there an 'easy' way to create a stable set of exclusive but complementary curve fitted formulas?
Thanks in advance.
Edit: I feel like I need to 'curve fit the curve fits' somehow.