Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships

Wang, Yanjun; Liu, Qun

doi:10.1016/j.fishres.2005.08.011

Cited by 132 publications

(82 citation statements)

References 11 publications

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“…8 The Akaike Information Criterion (AIC) as well as Schwartz Bayesian Criterion (SBC) are used to select the proper lag length of the autoregressive process. When the two criteria differ, we use the more parsimonious SBC criteria (Enders, 1995, p. 88;Wang and Liu, 2006). Results are available from the authors upon request.…”

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confidence: 99%

Price transmission in the Spanish bovine sector: the BSE effect

Hassouneh

Serra

Gil

2010

Agricultural Economics

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A regime-switching vector error correction model is applied to monthly price data to assess the impact of BSE outbreaks on price relationships and patterns of transmission among farm and retail markets for bovine in Spain. To evaluate the degree to which price transmission is affected by BSE food scares, a BSE food scare index is developed and used to determine regime-switching.Results suggest that BSE scares affect beef producers and retailers differently. Consumer prices are found to be weakly exogenous and not found to react to BSE scares, while producer prices conversely adjusted. The magnitude of the adjustment is found to depend on the magnitude of the BSE scare.

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confidence: 99%

Price transmission in the Spanish bovine sector: the BSE effect

Hassouneh

Serra

Gil

2010

Agricultural Economics

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“…Furthermore, information theoretic measures such as the Akaike (AIC) and Bayesian (BIC) [5] information criteria may fail in providing unique guidelines to the choice of f , see e.g. [36]. Simulation studies have also shown that the AIC can lead to a choice different from that which generated the observation data [9].…”

Section: Problem 1 (The General Srf Problem)mentioning

confidence: 99%

“…For instance, methods such as GCV and unbiased risk (UBR) have been developed under the assumption that the data is from independent observations [36]. When the independent observation assumption is violated, the results obtained are underestimates of the optimal smoothing parameter.…”

Section: Choice Of Functional Representationmentioning

confidence: 99%

Regularization of a Parameter Estimation Problem using Monotonicity and Convexity Constraints

Subbey¹

2017

ESAIM: ProcS

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Abstract. In marine science, it is usually assumed that there is a functional relationship between the parental population size and subsequent offsprings. The function is referred to as the Stock Recruitment Function (SRF). Determining the SRF translates to the optimization problem of estimating a set of parameters using past and sparse observation, which are usually of modest accuracy. The problem is challenging because several candidate functions exist in the literature, and the choice of best function is non-trivial, due to data sparsity and uncertainty.This paper formulates the problem as a constrained optimization task, and uses B-spline basis functions to represent the functional family to which the SRF belongs. Regularized solutions are obtained by requiring that the derived functions are both monotone and convex.The approach presents two major contributions to the existing computational challenges:• It avoids the non-trivial problem of choosing the functional form a priori.• Regularization of the problem using constraints ensures that parameter estimates are realistic. Numerical examples are presented to compare 1 and 2-norm solutions.Résumé. The paper presents a method that uses a prori information to regularize an inverse problem in fisheries science. It demonstrates the efficacy of the methodology in dealing with sparse and uncertain data, and limiting assumptions on the solution space.

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“…For a fixed length-at-age data set, adding more parameters to the model reduces that distance but further increases uncertainty in the estimation process. That trade-off between underfitting and overfitting is directly expressed in the AIC as a term that penalizes the model scores as a function of the number of estimated parameters in the model (Wang & Liu 2006). According to Pardo et al (2013) the AIC approach, used in age and growth studies, balances model complexity expressed in the number of parameters in each candidate growth model and goodnessof-fit ex pressed in the sum of squares algorithm.…”

Section: Model Selectionmentioning

confidence: 99%