Extending production models to include process error in the population dynamics

2003 ◽  
Vol 60 (10) ◽  
pp. 1217-1228 ◽  
Author(s):  
Andre E Punt
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Dynamics ◽  
Total Error ◽  
Error Estimator ◽  
Observation Error ◽  
Production Models ◽  
Fitting Production ◽  
Process Error ◽  
Relative Errors

Four methods for fitting production models, including three that account for the effects of error in the population dynamics equation (process error) and when indexing the population (observation error), are evaluated by means of Monte Carlo simulation. An estimator that represents the distributions of biomass explicitly and integrates over the unknown process errors numerically (the NISS estimator) performs best of the four estimators considered, never being the worst estimator and often being the best in terms of the medians of the absolute values of the relative errors. The total-error approach outperforms the observation-error estimator conventionally used to fit dynamic production models, and the performance of a Kalman filter based estimator is particularly poor. Although the NISS estimator is the best-performing estimator considered, its estimates of quantities of management interest are severely biased and highly imprecise for some of the scenarios considered.

Fitting Surplus Production Models: Comparing Methods and Measuring Uncertainty

1993 ◽  
Vol 50 (12) ◽  
pp. 2597-2607 ◽  
Author(s):  
Tom Polacheck ◽  
Ray Hilborn ◽  
Andre E. Punt
Keyword(s):  
Maximum Sustainable Yield ◽  
Parameter Estimates ◽  
Error Estimator ◽  
Sustainable Yield ◽  
Observation Error ◽  
Error Estimators ◽  
Averaging Methods ◽  
Surplus Production ◽  
Production Models ◽  
Process Error

Three approaches are commonly used to fit surplus production models to observed data: effort-averaging methods; process-error estimators; and observation-error estimators. We compare these approaches using real and simulated data sets, and conclude that they yield substantially different interpretations of productivity. Effort-averaging methods assume the stock is in equilibrium relative to the recent effort; this assumption is rarely satisfied and usually leads to overestimation of potential yield and optimum effort. Effort-averaging methods will almost always produce what appears to be "reasonable" estimates of maximum sustainable yield and optimum effort, and the r2 statistic used to evaluate the goodness of fit can provide an unrealistic illusion of confidence about the parameter estimates obtained. Process-error estimators produce much less reliable estimates than observation-error estimators. The observation-error estimator provides the lowest estimates of maximum sustainable yield and optimum effort and is the least biased and the most precise (shown in Monte-Carlo trials). We suggest that observation-error estimators be used when fitting surplus production models, that effort-averaging methods be abandoned, and that process-error estimators should only be applied if simulation studies and practical experience suggest that they will be superior to observation-error estimators.

Parameter estimation in modelling the dynamics of fish stock biomass: are currently used observation-error estimators reliable?

1998 ◽  
Vol 55 (3) ◽  
pp. 749-760 ◽  
Author(s):  
Y Chen ◽  
N Andrew
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Likelihood Method ◽  
Fish Stock ◽  
Error Estimator ◽  
Observation Error ◽  
Estimation Errors ◽  
Abundance Index ◽  
Error Estimators ◽  
Production Models

Production models are used in fisheries when only a time series of catch and abundance indices are available. Observation-error estimators are commonly used to fit the models to the data with a least squares type of objective function. An assumption associated with observation-error estimators is that errors occur only in the observed abundance index but not in the dynamics of stock and observed catch. This assumption is usually unrealistic. Because the least squares methods tend to be sensitive to error assumptions, results derived from these methods may be unreliable. In this study, we propose a robust observation-error estimator. We evaluate the performance of this method, together with the commonly used maximum likelihood method, under different error assumptions. When there was only observation error in the abundance index, maximum likelihood tended to perform better. However, with both observation and process errors, maximum likelihood yielded much larger estimation errors compared with the proposed method. This study suggests that the proposed method is robust to error assumptions. Because the magnitude and types of error cannot often be specified with confidence, the proposed method offers a potentially useful addition to methods used to fit production models to abundance index and catch data.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

A Monte Carlo simulation study of associated liquid crystals

1999 ◽  
Vol 97 (11) ◽  
pp. 1173-1184 ◽  
Author(s):  
R. Berardi, M. Fehervari, C. Zannoni
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Liquid Crystals ◽  
Simulation Study ◽  
Monte Carlo Simulation Study

Applying Meta-Analysis to Adverse Impact Assessment: A Monte Carlo Simulation

2006 ◽  
Author(s):  
John F. Skinner ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Meta Analysis ◽  
Adverse Impact

Monte Carlo simulation for reference tissue-based linear analysis of [11C]MP4A AND [11C]MP4P: Assessment of optimal regions and optimization for precise measurement of brain AChE activity

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S644-S644
Author(s):  
Koichi Sato ◽  
Kiyoshi Fukushi ◽  
Hitoshi Shinotoh ◽  
Shin-ichiro Nagatsuka ◽  
Noriko Tanaka ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ache Activity ◽  
Precise Measurement ◽  
Linear Analysis ◽  
Reference Tissue ◽  
Brain Ache ◽  
Brain Ache Activity
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