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- From: mwande@graunt.qal.berkeley.edu (Mike Anderson)
- Newsgroups: sci.math.stat
- Subject: modelling distributions
- Date: 11 Nov 1992 19:47:23 GMT
- Organization: University of California, Berkeley
- Lines: 24
- Distribution: world
- Message-ID: <1dro0bINN8p9@agate.berkeley.edu>
- NNTP-Posting-Host: graunt.qal.berkeley.edu
-
- Suppose I have data on two variables - age and income of a person. I would like
- to estimate income curves by age using spline regression, but my problem is
- this: to protect the identity of the individuals, incomes were topcoded, so
- that any person reporting incomes of > $100,000 gets coded at $100,000. I am
- interested in getting very accurate estimates, and given the long right tail
- of the income distribution, these truncated values may be throwing off my
- estimates quite a bit.
- So I would like to replace the truncated values with my own "tail". My
- question is, how do I go about modelling the income distribution and tacking
- on my own tail? Off the top of my head, I would precede thusly: If there are
- Nt truncated observations and Nu untruncated observations, N = Nt + Nu, I would
- first scale the Nu incomes to mean 0, generate N obs from something like a
- log-normal, lop off the top Nt quantiles, and compare the generated data to the
- observed data with a Q-Q plot, choosing that level of variance in the log-normal
- which gives me the straightest fit. Then I would take the Nt random values I
- lopped off from the generated data and randomly assign them to the Nt truncated
- values. Does this sound reasonable? I'm sure there is a better way to go about
- this, can someone tell me what it is or where to find it?
- Thanks in advance, Mike Anderson
- --
- Mike Anderson Dept. of Demography UC Berkeley mwande@QAL.Berkeley.EDU
- "And I would say to those out around the country. 'Take a hard look now.
- Don't let that rabbit be pulled out of the hat by one hand and 25 other
- rabbits dumped on you in another.'" - George Bush, 1/24/90
-