ENH: `min_weight` in addition to `min_periods` for ewma
Currently the exponential functions, such as `pd.ewma`, use a `min_periods` argument to ensure there's enough data to specify generate a valid value. While this works well for the rolling functions, it's not effective for exponential functions because points have weight forever, albeit ever decreasing:

``` python
In [4]: series=pd.Series(range(200))

In [5]: series[20:190]=pd.np.nan

In [6]: pd.ewma(series, span=10, min_periods=15)
Out[6]: 
0             NaN
1             NaN
2             NaN
3             NaN
4             NaN
5             NaN
6             NaN
7             NaN
8             NaN
9             NaN
10            NaN
11            NaN
12            NaN
13            NaN
14      10.277660
15      11.172348
16      12.080052
17      12.999407
18      13.929141
19      14.868084
20      14.868084
21      14.868084
22      14.868084
23      14.868084
24      14.868084
25      14.868084
26      14.868084
27      14.868084
28      14.868084
29      14.868084
          ...    
170     14.868084
171     14.868084
172     14.868084
173     14.868084
174     14.868084
175     14.868084
176     14.868084
177     14.868084
178     14.868084
179     14.868084
180     14.868084
181     14.868084
182     14.868084
183     14.868084
184     14.868084
185     14.868084
186     14.868084
187     14.868084
188     14.868084
189     14.868084
190    190.000000
191    190.550000
192    191.132890
193    191.748020
194    192.394502
195    193.071240
196    193.776953
197    194.510212
198    195.269468
199    196.053089
dtype: float64
```

I think what we want is to have a `min_weight` argument, so if you specify 0.5, it needs 50% of the weight in order to calculate a value. For rolling functions, this would be equivalent to `min_periods` being half of `window`.

What are people's thoughts?
