Dear listers,
Thank you for your comments and help. Yes indeed, my regressor is
"censored" in a way that it has unobserved values <0 coded as zero.
Sorry if I didn't make it clear.
melanie b.
Steven Samuels schrieb:
> There is a literature on censored regressors. A quick Google search on
> "censored regressors" turned up, for example:
>
> http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1071239
> http://web.mit.edu/tstoker/www/research.htm
>
> The original poster has not yet responded to Al Feiveson, so we do not
> know whether the regressor "x", say, is "censored" in the technical
> sense (has unobserved values <0 that are coded as zero). If there are
> many zeros, perhaps x was generated by one of two processes: the first
> for whether x would be non-zero, the second for the value of x if it
> were non-zero.
>
> One way to handle a mixture would be to generate two variables, an
> indicator that x is zero and, for non-zero x, the actual value.
>
> x_zero = x ==0 & x<.
> x_pos = x*(x > 0 & x<.) or xlog_pos = log(x)*(x > 0 & x<.)
>
> Insert x_zero and either x_pos or xlog_pos into the predictor list. In
> fact, it is not necessary to choose between logged and unlogged
> versions; -fracpoly- could model the best transformation of x_pos.
>
> The references above suggest that the indicator approach is biased if x
> is truly censored.
>
> -Steve
>
> On Jan 8, 2009, at 11:29 AM, Lachenbruch, Peter wrote:
>
>> Since the goal is to look at a logarithmic relationship, I'm wondering
>> if using glm with a log-link for a normal family wouldn't be helpful.
>> That way you don't need to worry about 0 values.
>>
>> Tony
>>
>> Peter A. Lachenbruch
>> Department of Public Health
>> Oregon State University
>> Corvallis, OR 97330
>> Phone: 541-737-3832
>> FAX: 541-737-4001
>>
>>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Feiveson,
>> Alan H. (JSC-SK311)
>> Sent: Wednesday, January 07, 2009 11:54 AM
>> To: [email protected]
>> Subject: st: RE: RE: RE: RE: how to deal with a censored and skewed
>> regressor?
>>
>> If there were other X-variables, one way (probably not the best) would
>> be to use multiple imputation. More generally, some sort of structural
>> model that relates Y to true X and includes the censoring mechanism
>> could be estimated (ha!). I suspect there are econometric models out
>> there that do this sort of thing - possibly even already programmed in
>> Stata.
>>
>> AL F.
>>
>>
>>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Nick Cox
>> Sent: Wednesday, January 07, 2009 1:06 PM
>> To: [email protected]
>> Subject: st: RE: RE: RE: how to deal with a censored and skewed
>> regressor?
>>
>> And how would you do that? Other than knowing that c.i.s and P-values
>> are not as good as they seem, what difference does this knowledge make
>> to what you do?
>>
>> Nick
>> [email protected]
>>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Feiveson,
>> Alan H. (JSC-SK311)
>> Sent: 07 January 2009 18:27
>> To: [email protected]
>> Subject: st: RE: RE: how to deal with a censored and skewed regressor?
>>
>> Nick wrote: "If you regard such a regressor as error-free, as one
>> usually does, then I am not clear that procedure need otherwise be
>> affected."
>>
>> But if the variable (say X )is censored, then it's real value is unknown
>> except for an upper or lower bound and there is error ,hence bias in the
>> regression parameter estimates if X is used as is. So in mbaier's case,
>> if X is really censored at zero, that means it's true value is some
>> negative number. This needs to be taken into account in the estimation.
>>
>> Al Feiveson
>>
>>
>>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Nick Cox
>> Sent: Wednesday, January 07, 2009 12:09 PM
>> To: [email protected]
>> Subject: st: RE: how to deal with a censored and skewed regressor?
>>
>> (x - r(min)) / (r(max) - r(min))
>>
>> does not yield missing when r(min) is 0 unless x is missing or r(max) is
>> also zero. But that's neither here or there. The above is just a linear
>> rescaling of a variable and will thus leave skewness unchanged.
>>
>> Skewness of a regressor is not itself fatal to anything.
>>
>> Censoring of a regressor is something to take account of in
>> interpretation. If you regard such a regressor as error-free, as one
>> usually does, then I am not clear that procedure need otherwise be
>> affected.
>>
>> Nick
>> [email protected]
>>
>> mbaier
>>
>> I tried to transform it according to ln(skewed variable), but my
>> regressor has a lots of values at zero, for which ln is not defined. I
>> also tried to create an index like I=100*(x-r(min))/(r(max)-r(min)),
>> which again leads to many missings (due to many x's being zero).
>> What can I do?
>> Besides, do I have to account for the censoring of my regressor? If so,
>> what can I do?
>> w.ats.ucla.edu/stat/stata/
>
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>
--
Dipl.-Volkswirtin
Melanie Baier
C-LAB
Business Development
Fuerstenallee 11
33102 Paderborn, Germany
Phone: +49 5251 60 61 35
Fax: +49 5251 60 60 66
URL: www.c-lab.de
*
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