p********0
发帖数: 186 | 1 For the state-space linear dynamic system,
y_t+1 = A_t * x_t + R
x_t+1 = B_t * x_t + Q
I read some paper about using EM - maximum likelihood to estimate the
parameter A and B
In simple case assume A_t = A_t-1 = ... A_1, we can maximize \hat{x_t} = E[x
_t | Y_1,,,n]
How do we handle the situaion A_t change over time, like ARMA(1, 1) case,
just use fewer observation Y_t-1, Y_t to estimat the A_t/B_t ??? | p********0
发帖数: 186 | 2 Ding,
I may not be clear about my question, let me rephrase it.
For time series data, state space model, if the parameter is not static,
change over time
X_t = F_t * X_t-1 + R.
Y_t = H_t * X_t
What's the best way for finding the online parameter?
I found resurve Least Sqaure Error/Gaussian Newton method/Kalman filter all
can be used to do the online parameter estimate, what's the best method?
Anyone has experience?
[x
【在 p********0 的大作中提到】
: For the state-space linear dynamic system,
: y_t+1 = A_t * x_t + R
: x_t+1 = B_t * x_t + Q
: I read some paper about using EM - maximum likelihood to estimate the
: parameter A and B
: In simple case assume A_t = A_t-1 = ... A_1, we can maximize \hat{x_t} = E[x
: _t | Y_1,,,n]
: How do we handle the situaion A_t change over time, like ARMA(1, 1) case,
: just use fewer observation Y_t-1, Y_t to estimat the A_t/B_t ???
| g****t
发帖数: 31659 | 3 你得知道F_t大概变化的快慢。对F_t一无所知，那就没办法辨识。
例如200个点之内F可看作常数，那你就每200个点作一次least square。
all
【在 p********0 的大作中提到】
: Ding,
: I may not be clear about my question, let me rephrase it.
: For time series data, state space model, if the parameter is not static,
: change over time
: X_t = F_t * X_t-1 + R.
: Y_t = H_t * X_t
: What's the best way for finding the online parameter?
: I found resurve Least Sqaure Error/Gaussian Newton method/Kalman filter all
: can be used to do the online parameter estimate, what's the best method?
: Anyone has experience?
|
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