BUG: centered moving windows mishandle series edges
Seems like there is a bug in the centered mooving window functions, although the examples [here](http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments) don't show it. 
Also `np.convolve` used like in [this post](http://stackoverflow.com/a/11352216/1301710) on Stackoverflow shows different results for the first values:

``` python
In [109]: ser = pd.Series(randn(20), index=pd.date_range('1/1/2000', periods=20))

In [110]: df = pd.DataFrame(ser)

In [111]: df['rc'] = pd.rolling_count(ser, 10, center=True)

In [112]: df['rm'] = pd.rolling_mean(ser, 10, center=True)

In [113]: df['rm_min_1'] = pd.rolling_mean(ser, 10, center=True, min_periods=1)

In [114]: def movingaverage(interval, window_size):
   .....:         window = numpy.ones(int(window_size))/float(window_size)
   .....:         return numpy.convolve(interval, window, 'same')
   .....: 

In [116]: df['ma'] = movingaverage(ser, 10)

In [117]: df
Out[117]: 
                   0  rc        rm  rm_min_1        ma
2000-01-01  1.177692   5       NaN  0.048306  0.024153
2000-01-02 -0.068567   6       NaN -0.094287 -0.056572
2000-01-03  0.194942   7       NaN -0.225103 -0.157572
2000-01-04 -0.179778   8       NaN -0.160040 -0.128032
2000-01-05 -0.882760   9       NaN -0.262176 -0.235959
2000-01-06 -0.807252  10 -0.250828 -0.250828 -0.250828
2000-01-07 -1.009999  10 -0.365703 -0.365703 -0.365703
2000-01-08  0.295402  10 -0.431110 -0.431110 -0.431110
2000-01-09 -1.079267  10 -0.408968 -0.408968 -0.408968
2000-01-10 -0.148689  10 -0.456946 -0.456946 -0.456946
2000-01-11  0.028935  10 -0.290453 -0.290453 -0.290453
2000-01-12 -0.722632  10 -0.204166 -0.204166 -0.204166
2000-01-13  0.416364  10 -0.175837 -0.175837 -0.175837
2000-01-14 -0.659560  10 -0.266773 -0.266773 -0.266773
2000-01-15  0.782165  10 -0.184003 -0.184003 -0.184003
2000-01-16  0.055617  10 -0.338359 -0.338359 -0.338359
2000-01-17 -0.726706   0       NaN       NaN -0.341252
2000-01-18 -0.613954   0       NaN       NaN -0.268989
2000-01-19 -0.251574   0       NaN       NaN -0.310625
2000-01-20 -1.692243   0       NaN       NaN -0.244669
```

```
In [5]: import numpy

In [6]: In [20]: ser = pd.Series(range(10))
   ...:     ...: df = pd.DataFrame(ser)
   ...:     ...: N=3
   ...:     ...: df['rc'] = ser.rolling(N, center=True).count()
   ...:     ...: df['rm'] = ser.rolling(N, center=True).mean()
   ...:     ...: df['rm_min_1'] = ser.rolling(N, center=True, min_periods=1).mean()
   ...:     ...: def movingaverage(interval, window_size):
   ...:     ...:     window = numpy.ones(int(window_size))/float(window_size)
   ...:     ...:     return numpy.convolve(interval, window, 'same')
   ...:     ...:
   ...:     ...: df['ma'] = movingaverage(ser, N)

In [7]: df
Out[7]:
   0   rc   rm  rm_min_1        ma
0  0  2.0  NaN       0.5  0.333333
1  1  3.0  1.0       1.0  1.000000
2  2  3.0  2.0       2.0  2.000000
3  3  3.0  3.0       3.0  3.000000
4  4  3.0  4.0       4.0  4.000000
5  5  3.0  5.0       5.0  5.000000
6  6  3.0  6.0       6.0  6.000000
7  7  3.0  7.0       7.0  7.000000
8  8  3.0  8.0       8.0  8.000000
9  9  2.0  NaN       8.5  5.666667
```