[Numpy-discussion] weighted mean; weighted standard error of the mean (sem)

[Keith Goodman]

On Thu, Sep 9, 2010 at 7:22 PM, cpblpublic <cpblpublic+numpy at gmail.com> wrote:
> I am looking for some reaally basic statistical tools. I have some
> sample data, some sample weights for those measurements, and I want to
> calculate a mean and a standard error of the mean.

How about using a bootstrap?

Array and weights:

>> a = np.arange(100)
>> w = np.random.rand(100)
>> w = w / w.sum()

Initialize:

>> n = 1000
>> ma = np.zeros(n)

Save mean of each bootstrap sample:

>> for i in range(n):
   ....:     idx = np.random.randint(0, 100, 100)
   ....:     ma[i] = np.dot(a[idx], w[idx])
   ....:
   ....:

Error in mean:

>> ma.std()
   3.854023384833674

Sanity check:

>> np.dot(w, a)
   49.231127299096954
>> ma.mean()
   49.111478821225127

Hmm...should w[idx] be renormalized to sum to one in each bootstrap sample?