I agree with Ben . without providing any information of the data, we
cannot make inference on the distribution itself.
actually density estimation is a tough problem( and classical ). Of
course,
there are methods like GMM or some other method based on statistical
learning,
however, they make assumptions and only do the approximation.
I hope anyone can explain more on this topic using more R specific
language.
On 2008-6-30, at 下午7:50, Ben Bolker wrote:
Jenny Barnes <jmb <at> mssl.ucl.ac.uk> writes:
Dear R-help community,
Does anybody know of a stats function in R that tells you which
distribution best fits your data? I have tried look through the
archives
but have only found functions that tell you if it's normal or log
etc.
specifically - I am looking for a function that tells you (given a
timeseries) what the distribution is.
Any help/advice will be greatly appreciated,
All the best,
Jenny Barnes
jmb <at> mssl.ucl.ac.uk
The problem is that it's not generally a good
idea to data-dredge in this way. Your best bet is
to think about the characteristics of the
data (discrete or continuous, non-negative or real,
symmetric or skewed) and try to narrow it down to
a few distributions -- then you can use fitdistr()
(from the MASS package) or something similar
to compare among them.
If you say a little bit more about what
you're trying to do with the data you might
get some more specific advice.
Ben Bolker
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-----------------------------------------------
Peng Jiang 江鹏 ,Ph.D. Candidate
Antai College of Economics & Management
安泰经济管理学院
Department of Mathematics
数学系
Shanghai Jiaotong University (Minhang Campus)
800 Dongchuan Road
200240 Shanghai
P. R. China
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