home *** CD-ROM | disk | FTP | other *** search
- Newsgroups: sci.math.stat
- Path: sparky!uunet!zaphod.mps.ohio-state.edu!moe.ksu.ksu.edu!ux1.cso.uiuc.edu!news.cso.uiuc.edu!uimrl7.mrl.uiuc.edu!ritley
- From: ritley@uimrl2.mrl.uiuc.edu ()
- Subject: Fitting Data and Fitting Integrals
- Message-ID: <BzEoJ0.49v@news.cso.uiuc.edu>
- Sender: usenet@news.cso.uiuc.edu (Net Noise owner)
- Reply-To: ritley@uiucmrl.bitnet ()
- Organization: Materials Research Lab
- Date: Thu, 17 Dec 1992 13:37:47 GMT
- Lines: 29
-
-
- I am trying to determine the coefficients of a nonlinear
- function which fit a probability distribution (x,P(x)). The
- distribution P(x) is a set of data points (usually several
- thousand) generated by a computer model and thus contains
- noise.
-
- I have discovered that it is not only possibly to fit the
- function directly to the data, but also it is possible to
- fit the integral of the function to the integral of the
- data. In fact, the integral of the data can be a smoother
- function.
-
- I would like to know if examining the "fitted integral" in
- this way can be a useful technique for determining the
- "best-fit" coefficients. Although one must be careful about
- what one means by "best-fit" and "goodness of fit", it seems
- to me that examining both the function and its integral
- might give more information than by examining the function
- alone. For example, it is conceivable that the data set as
- well as its integral may both have physical meaning.
- However, it is also conceivable that the integral of the
- data contains less information than the data itself.
-
- I have no background in statistics and thus any
- help/comments/suggestions/pointers-to-the-literature would
- be _greatly_ appreciated!
-
-
-