Timeline for Skewed and fat-tailed error process in Kalman filter
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 26, 2018 at 19:45 | vote | accept | Will Gu | ||
| Jan 11, 2017 at 20:59 | answer | added | Taylor | timeline score: 1 | |
| Jan 11, 2017 at 17:13 | comment | added | Will Gu | @Taylor I know it may sound absurd in theory but in reality this could happen. Suppose people trade some kind of security and we are trying to obtain the price continuously. Occasionally a trade happens, and we believe that we observe that "price" at that moment. Other times, people quote the price that want to buy or sell at, no trade happens. This can be regarded as a noisy measure of the price. | |
| Jan 11, 2017 at 16:48 | comment | added | Taylor | "of which I occasionally can observe the state variable variable but not always." I suspect your model is mis-specified, and in some other way than in which you suggest (normality instead of t-errors). The 'state' process in a linear gaussian state space model can represent many different things, but one thing that it may not be is partially or sometimes observed. I suggest talking more about the specifics of your data to get it sorted out | |
| Jan 11, 2017 at 1:09 | history | asked | Will Gu | CC BY-SA 3.0 |