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Changes between Version 13 and Version 14 of Ticket #664


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Timestamp:
Mar 25, 2009, 7:58:29 PM (16 years ago)
Author:
Víctor de Buen Remiro
Comment:

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  • Ticket #664 – Description

    v13 v14  
    1616Let be the last full calculated and stored system
    1717
    18 [[LatexEquation(Y' = X' \beta' + z' $$)]][[BR]]
    19 [[LatexEquation(A \beta' <= a $$)]][[BR]]
     18[[LatexEquation(Y' = X' \beta + z' $$)]][[BR]]
     19[[LatexEquation(A \beta <= a $$)]][[BR]]
    2020[[LatexEquation(\phi'\left(B\right) z'_t = \theta'\left(B\right) e'_t $$)]][[BR]]
    2121[[LatexEquation(e' \sim N\left(0,\sigma'^{2} I\right) $$)]][[BR]]
    2222
    23 Following these steps for each Metropolis-Hastings step we can generate a candidate of vector [[LatexEquation(\beta' $$)]] calculating simultaneously its exact density:
    24  1. Since this system has been previously decomposed is very fast to generate a pre-candidate vector [[LatexEquation(\beta' $$)]] matching it
    25  1. The corresponding ARIMA noise is simply [[BR]][[LatexEquation(z' = Y' - X' \beta' $$)]]
    26  1. By means of Almagro method it's posible to calculate residuals [[LatexEquation(e' $$)]] and initial values [[LatexEquation(u' $$)]] that solve difference equation [[BR]] [[LatexEquation(e'_t = \frac{\phi'\left(B\right)}{\theta'\left(B\right)} z'_t $$)]]
    27  1. Let be the standarized multinormal residuals [[BR]] [[LatexEquation( \epsilon' =\frac{1}{\sigma'}e' $$)]] [[BR]]
    28  1. Since [[LatexEquation(\beta' $$)]] are determined by [[LatexEquation(\epsilon' $$)]] by means of a full range linear equation, their densities have constant ratio, and logarithm of density of candidate vector can be calculated directly as [[BR]] [[LatexEquation(cte -\frac{T}{2}\log\left(2\pi\right)-\frac{1}{2}\sum_{t=1}^{T}\epsilon'_{t}^{2}$$)]]
    29  1. Then we can propose residuals and initial values for current system as [[BR]][[LatexEquation(e^{*} = \frac{\sigma}{\sigma'}e' $$)]] [[BR]] [[LatexEquation(u^{*} = \frac{\sigma}{\sigma'}u' $$)]]
    30  1. ARIMA noise for current system becomes simply [[BR]][[LatexEquation(z^{*}_t = \frac{\theta\left(B\right)}{\phi\left(B\right)} e^{*}_t $$)]]
    31  1. In order to get the refined candidate, we will take minimum residuals solution of sparse linear system  [[BR]][[LatexEquation(X \beta = Y-z^{*} $$)]]
    32  1. In order to calculate its exact density, it will be obtained the ARIMA noise [[BR]][[LatexEquation(z = Y - X \beta $$)]] [[BR]] to get standarized residuals from ARIMA equations.
    33  1. If resulting vector doesn't match constraining inequations [[BR]] [[LatexEquation(A \beta <= a $$)]][[BR]] density will be toggled to [[LatexEquation(-\infty $$)]] in order to force rejection.
     23Following these steps for each Metropolis-Hastings step we can generate a candidate of vector [[LatexEquation(\beta $$)]] calculating simultaneously its exact density:
     24 1. Since this system has been previously decomposed is very fast to generate a candidate vector and its density [[LatexEquation( \log\left(Q\left(\beta\right)\right) $$)]], that is not depending on previous state.
     25 1. In order to calculate the density for current system we will get corresponding ARIMA noise [[BR]][[LatexEquation(z = Y - X \beta $$)]]
     26 1. By means of Levinson or Almagro method of ARMA evaluation it's posible to calculate in a very fast way differential equation [[BR]][[LatexEquation(\phi\left(B\right) z_t = \theta\left(B\right) e_t $$)]][[BR]] getting also residuals likelihood which logarithm is, but a constant, the exact density [[LatexEquation( \log\left(P\left(\beta\right)\right) $$)]]
    3427
    35 The number of internal Metropolis-Hastings non rejected iterations must be a parameter of user configuration and should not be more than 10 or 20 iterations. When number of rejected iterations growns is the moment of remake full calculations and store a new preconditioner regression. If this happens after a large number of iterations, then a lot of time will be saved.
     28Due to candidates are independent of previous chain it is enought to have just one non rejected sample. When number of rejected iterations growns is the moment of remake full calculations and store a new preconditioner regression. If this happens after a large number of iterations, then a lot of time will be saved.