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Bayesian predictions of low time series
McCabe, Brendan Peter Martin
;
Martin, Gael M.
- In:
International journal of forecasting
21
(
2005
)
2
,
pp. 315-330
Persistent link: https://www.econbiz.de/10002687880
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Persistence and nonstationary models
McCabe, Brendan Peter Martin
;
Martin, Gael M.
; …
-
2003
Persistent link: https://www.econbiz.de/10001854477
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3
Testing for dependence in non-Gaussian time series data
McCabe, Brendan Peter Martin
;
Martin, Gael M.
; …
-
2004
Persistent link: https://www.econbiz.de/10002121951
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4
A quasi-locally most powerful test for correlation in the conditional variance of positive data
McCabe, Brendan Peter Martin
;
Martin, Gael M.
; …
-
2010
Persistent link: https://www.econbiz.de/10008661679
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5
Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models
Ng, Jason
;
Forbes, Catherine Scipione
;
Martin, Gael M.
; …
- In:
International journal of forecasting
29
(
2013
)
3
,
pp. 411-430
Persistent link: https://www.econbiz.de/10009787038
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6
Approximate Bayesian computation in state space models
Martin, Gael M.
;
McCabe, Brendan Peter Martin
; …
-
2014
Persistent link: https://www.econbiz.de/10011780814
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7
Data-driven particle filters for Particle Markov Chain Monte Carlo
Leung, Patrick
;
Forbes, Catherine Scipione
;
Martin, Gael M.
-
2016
Persistent link: https://www.econbiz.de/10011781784
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8
Approximate Bayesian forecasting
Frazier, David T.
;
Maneesoonthorn, Worapree
;
Martin, Gael M.
-
2018
Persistent link: https://www.econbiz.de/10012583287
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9
Approximate Bayesian forecasting
Frazier, David T.
;
Maneesoonthorn, Worapree
;
Martin, Gael M.
- In:
International journal of forecasting
35
(
2019
)
2
,
pp. 521-539
Persistent link: https://www.econbiz.de/10012300696
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10
Forecasting observables with particle filters : any filter will do!
Leung, Patrick
;
Forbes, Catherine Scipione
;
Martin, Gael M.
-
2019
Persistent link: https://www.econbiz.de/10012606152
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