Timeline for Efficient Empirical CDF Computation / Storage
Current License: CC BY-SA 2.5
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 23, 2010 at 23:43 | vote | accept | bnaul | ||
| Nov 23, 2010 at 23:33 | comment | added | whuber♦ | It sounds like you really need only to store one tail accurately. That could simplify your precalculations somewhat. After all, there's little practical difference between a p-value of 50% and 25%, but a huge difference between 5% and 0.001%. | |
| Nov 23, 2010 at 23:29 | answer | added | whuber♦ | timeline score: 6 | |
| Nov 23, 2010 at 23:28 | answer | added | csgillespie | timeline score: 3 | |
| Nov 23, 2010 at 23:14 | comment | added | bnaul | Further info: the functions represent different measures of proximity to various types of genes. I want to try out different metrics, but they all can be boiled down to "(some kind of) distance to the nearest gene of type X". So the values would probably look something like this graph repeated over and over again (with different shapes), and a histogram could have pretty much any shape, depending on how far apart the relevant genes are. Using the CDF, I could compute a score for a position and the probability of getting this high a score by chance. | |
| Nov 23, 2010 at 23:02 | history | asked | bnaul | CC BY-SA 2.5 |
