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Search: subject:"Continuous Time"
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Germany
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binomial time series
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continuous time modelling
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continuous-time modelling
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disattenuation
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latent variables
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measurement errors
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non-Gaussian state space modeling
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nonlinear panel data model
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panel data
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population change
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recidivism behavior
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spatial dependence
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structural equation modelling
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unemployment change
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Folmer, Henk
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Geest, Victor van der
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Koopman, Siem Jan
2
Lucas, André
2
Montfort, Kees van
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Nijkamp, Peter
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Ooms, Marius
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Tinbergen Institute Discussion Papers
Physica A: Statistical Mechanics and its Applications
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TOP: An Official Journal of the Spanish Society of Statistics and Operations Research
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1
Continuous-Time
Modelling with Spatial Dependence
Oud, Johan H.L.
;
Folmer, Henk
;
Patuelli, Roberto
; …
-
Tinbergen Institute
-
2011
continuous
time
(CT) modelling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical …
Persistent link: https://www.econbiz.de/10009201135
Saved in:
2
Continuous-Time
Modelling with Spatial Dependence
Oud, Johan H.L.
;
Folmer, Henk
;
Patuelli, Roberto
; …
-
Tinbergen Instituut
-
2011
> (Spatial) panel data are routinely modelled in discrete time (DT). However, there are compelling arguments for
continuous
time
…
Persistent link: https://www.econbiz.de/10011257042
Saved in:
3
Estimating Systematic
Continuous-time
Trends in Recidivism using a Non-Gaussian Panel Data Model
Koopman, Siem Jan
;
Lucas, André
;
Ooms, Marius
; …
-
Tinbergen Instituut
-
2007
systematic conditions, (4) changing recidivism probabilities in
continuous
time
, (5) missing observations. We adopt a non … Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare
continuous-time
trends …
Persistent link: https://www.econbiz.de/10011256683
Saved in:
4
Estimating Systematic
Continuous-time
Trends in Recidivism using a Non-Gaussian Panel Data Model
Koopman, Siem Jan
;
Lucas, André
;
Ooms, Marius
; …
-
Tinbergen Institute
-
2007
systematic conditions, (4) changing recidivism probabilities in
continuous
time
, (5) missing observations. We adopt a non … Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare
continuous-time
trends …
Persistent link: https://www.econbiz.de/10005144538
Saved in:
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