Ted: Thanks for the pointer. I've been looking for a replacement for cold
for a while because it doesn't allow the merge operation for QuantileBin1D
and as you pointed out it is a generally messy library. stream-lib could be
the replacement I"m looking for ... if it passes other requirements. Will
On Sat, Aug 10, 2013 at 8:59 AM, Ajo Fod wrote:
> If the data doesn't fit, you probably need a StorelessQuantile estimator
> like QuantileBin1D from the colt libraries. Then pick a resolution and do
> the single pass search.
>
Peripheral to the actual topic, but the Colt libraries are out of dat
On 8/10/13 11:00 AM, Phil Steitz wrote:
> On 8/10/13 10:41 AM, Ajo Fod wrote:
>> This paper provides some approximations and situations where you could use
>> exact computations:
>> http://www.iro.umontreal.ca/~lecuyer/myftp/papers/ksdist.pdf
>> ... they even refer to a java program at the end of p
On 8/10/13 10:41 AM, Ajo Fod wrote:
> This paper provides some approximations and situations where you could use
> exact computations:
> http://www.iro.umontreal.ca/~lecuyer/myftp/papers/ksdist.pdf
> ... they even refer to a java program at the end of page 11.
He he. This is the reference that ou
This paper provides some approximations and situations where you could use
exact computations:
http://www.iro.umontreal.ca/~lecuyer/myftp/papers/ksdist.pdf
... they even refer to a java program at the end of page 11.
Cheers,
Ajo.
On Sat, Aug 10, 2013 at 10:06 AM, Phil Steitz wrote:
> On 8/10/1
On 8/10/13 9:35 AM, Ajo Fod wrote:
> In:
> http://ocw.mit.edu/courses/mathematics/18-443-statistics-for-applications-fall-2006/lecture-notes/lecture14.pdf
>
> Take a look at 2 sample KS stats and the relationship to the 1 sample ...
> page 88. You already have the 1 sample table.
The problem is th
In:
http://ocw.mit.edu/courses/mathematics/18-443-statistics-for-applications-fall-2006/lecture-notes/lecture14.pdf
Take a look at 2 sample KS stats and the relationship to the 1 sample ...
page 88. You already have the 1 sample table.
Cheers,
Ajo
On Sat, Aug 10, 2013 at 9:16 AM, Phil Steitz w
On 8/10/13 8:59 AM, Ajo Fod wrote:
> This depends on data size. If it fits in memory, a single pass through the
> sorted array to find the biggest differences would suffice.
>
> If the data doesn't fit, you probably need a StorelessQuantile estimator
> like QuantileBin1D from the colt libraries. Th
This depends on data size. If it fits in memory, a single pass through the
sorted array to find the biggest differences would suffice.
If the data doesn't fit, you probably need a StorelessQuantile estimator
like QuantileBin1D from the colt libraries. Then pick a resolution and do
the single pass
I am working on MATH-437 (turning K-S distribution into a proper K-S
test impl) and have to decide how to implement 2-sample tests.
Asymptotically, the 2-sample D_n,m test statistic (see [1]) has a
K-S distribution, so for large samples just using the cdf we already
have is appropriate. For small
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