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- Organization: Statistics, Carnegie Mellon, Pittsburgh, PA
- Path: sparky!uunet!zaphod.mps.ohio-state.edu!cis.ohio-state.edu!news.sei.cmu.edu!fs7.ece.cmu.edu!crabapple.srv.cs.cmu.edu!andrew.cmu.edu!bj20+
- Newsgroups: sci.math.stat
- Message-ID: <Medg7tK00j4e4IMUVi@andrew.cmu.edu>
- Date: Thu, 3 Sep 1992 21:55:05 -0400
- From: Brian Junker <bj20+@andrew.cmu.edu>
- Subject: Re: Simple Proof that c=median minimizes E[ |X-c| ] needed.
- In-Reply-To: <3SEP199213440863@utkvx2.utk.edu>
- References: <3SEP199213440863@utkvx2.utk.edu>
- Lines: 25
-
- Here are some ideas off the top of my head, that should get you going in
- the right direction. You will need some decent calculus skills, but
- that's about it.
-
- Case 1. X is a real-valued continuous random variable with density f(x)
- such that xf(x) -> 0 as x -> +infinity and as x -> -infinity. Write
- E[|X-c|] as the sum of two integrals, one going from -infinity to c and
- the other going from c to +infinity, where neither integral involves
- abs. val's anymore. Differentiate this sum with respect to c and
- explore...
-
- Case 2. x_1, x_2, ... x_n are fixed real numbers (i.e. you have a
- sample of size n). Then c=the sample median minimizes the sum of
- absolute deviations |x_1-c| + |x_2-c| + ... + |x_n-c|. You can do the
- cases n=2, 3, and 4 "by hand" by graphing the sum of abs. deviations as
- a function of c---it is piecewise linear with bends at the data points
- x_1, x_2, ... What do you have to do to give a rigorous proof for n
- data points now?
-
- Case 3. X is a discrete random variable taking on the values x_1, x_2,
- .... with probabilities p_1, p_2, .... The ideas you explored above
- should be useful in proving that the median minimizes E[|X-c|] for this
- case.
-
- -BJ
-