Harold W Kerster wrote:
Look at minitab's trimmed mean. It is a Tukey (I think) invention w/5%
chopped from each end, leaving the central 90%. For the high variance,
high skew, common world, a good approach.
Oh, yeah, especially when setting reserves. Or calculating premiums.
Those
Hola!
But the original poster asked about trimming IN THE MARGINAL
DISTRIBUTION with multivariate data --- and that is totally insane. If
trimming should be used in regression settings , the residuals should be
trimmed - as in Rousseuw and Leroy's LTS (least trimmed mean of
squares), implemented
Look at minitab's trimmed mean. It is a Tukey (I think) invention
w/5% chopped from each end, leaving the central 90%. For the high
variance, high skew, common world, a good approach.
On Tue, 29 Jan 2002, Rich Ulrich wrote:
On 17 Jan 2002 00:05:02 -0800, [EMAIL PROTECTED] (Hekon) wrote:
here is an example from minitab ... in moore and mccabe's book intro. to
practice of statistics ... 3rd edition ... they have an example of speed of
light measurements ... with newcomb in his lab on the bank of the potomac
... bouncing light bursts off the base of the washington monument ...
On 17 Jan 2002 00:05:02 -0800, [EMAIL PROTECTED] (Håkon) wrote:
I have noticed a practice among some people dealing with enterprise
data to cut the left and right tails off their samples (including
census data) in both dependent and independent variables. The reason
is that outliers tend to
I have noticed a practice among some people dealing with enterprise
data to cut the left and right tails off their samples (including
census data) in both dependent and independent variables. The reason
is that outliers tend to be extreme. The effects can be stunning. How
is this practice to be
