A maximum entropy approach to estimation and inference in dynamic models or Counting fish in the sea using maximum entropy
References (24)
- et al.
Estimating the objectives of a public firm in a natural resource industry
Journal of Environmental Economics and Management
(1989) - et al.
Formulating and estimating dynamic linear rational expectations models
Journal of Economic Dynamics and Control
(1980) - et al.
Estimating structural resource models when stock is uncertain: Theory and an application to pacific halibut
Bayesian analysis of linear models
(1985)Analysis and control of dynamic economic systems
(1975)Econometric analysis by control methods
(1981)Econometrics
(1983)Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems
Annals of Statistics
(1991)- et al.
Recovering information from multinomial response data
Journal of the American Statistical Association
(1996) - et al.
Inverse problem of linear optimal control
SIAM Journal of Control
(1973)
Information theory and statistical mechanics
Physics Review
Information theory and statistical mechanics II
Physics Review
Cited by (47)
Driving mechanism of concentrated rural resettlement in upland areas of Sichuan Basin: A perspective of marketing hierarchy transformation
2020, Land Use PolicyCitation Excerpt :In the case of a small sample size, the estimation by classical methods (e.g., least squares) may provide parameter estimates with high variance. As a solution, the Generalized Maximum Entropy (GME) estimator was employed to deal with the problem of recovering information when the underling model is not completely known and the data are relatively limited, partial or incomplete (Golan et al., 1996). The GME estimator builds on the Shannon entropy-information measure, the classical maximum entropy principle and the generalized maximum entropy theory (Golan et al., 1997; Jaynes, 1957; Shannon, 1948).
Exploitable carrying capacity and potential biomass yield of sectors in the East China Sea, Yellow Sea, and East Sea/Sea of Japan large marine ecosystems
2019, Deep-Sea Research Part II: Topical Studies in OceanographyCitation Excerpt :The HPM, which estimates ECC using surplus production models with time-series catch and fishing effort data, is simple and easy to apply, and requires much less data. A further advantage is that the non-equilibrium surplus production model's fit is reasonably good and the reliability of estimated parameters is relatively high compared with the EMM approach, since the parameters are usually estimated from a theoretically optimal range (Golan et al., 1996). This is the reason why many studies have used this approach (Garcia and Newton, 1997; Kim, 2016).
A preliminary study on ABC estimation approach for ecosystem-based TAC management
2018, Ocean and Coastal ManagementCitation Excerpt :The parameters for estimation of biomass, yield, and fishing effort, such as intrinsic growth rate r = 0.37/year, carrying capacity K = 2,738,800, and catchability coefficient q = 2.58 × 10−5, were used. These parameters were assumed by the maximum entropy model (Golan et al., 1996), applying catch (mt) and fishing effort (haul) data of the Korean chub mackerel by large purse seine fishery from 1990 to 2008. Table 3 shows the input parameters for the prediction of reproductive potential and mean total length indicators.
A Generalized Maximum Entropy (GME) estimation approach to fuzzy regression model
2015, Applied Soft Computing JournalA density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources
2013, Journal of Environmental Economics and ManagementThe economics of spatial-dynamic processes: Applications to renewable resources
2009, Journal of Environmental Economics and Management