A robust regression analysis of recruitment in fisheries

1995 ◽  
Vol 52 (5) ◽  
pp. 993-1006 ◽  
Author(s):  
Y. Chen ◽  
J. E. Paloheimo
Keyword(s):  
Environmental Variables ◽  
Measurement Errors ◽  
Robust Regression ◽  
Estimation Method ◽  
Fish Stock ◽  
Estimation Errors ◽  
Ricker Model ◽  
Stock Recruitment ◽  
Atypical Values ◽  
Special Studies

Variations in environmental variables and (or) errors in measuring stock and recruitment often result in large and heterogeneous variations in fitting fish stock–recruitment (SR) data to a regression model. This makes the commonly used least squares (LS) method inappropriate in estimating the SR relationship. Hence, we propose the following procedure: (i) identify possible outliers in fitting data to a given SR model using the least median of the squared orthogonal distance that is not sensitive to atypical values and requires no assumption on distribution of errors and (ii) apply the LS method to the SR data with defined outliers being down weighted. We showed by simulation that the SR parameters of the Ricker model could be estimated with smaller estimation errors and biases using the proposed procedures than with the traditional LS approach. Examination of four sets of published field data leads us to suggest fitting fish SR data to suitable models using the proposed estimation method and interpreting the results with the assistance of knowledge on the relevant environmental variables and measurement errors. However, our interpretation should be viewed as a working hypothesis requiring special studies to clarify the causal links between environmental variables and recruitment.

scholarly journals Non-linearity in stock–recruitment relationships of Atlantic cod: insights from a multi-model approach

2019 ◽  
Vol 77 (4) ◽  
pp. 1492-1502 ◽  
Author(s):  
Camilla Sguotti ◽  
Saskia A Otto ◽  
Xochitl Cormon ◽  
Karl M Werner ◽  
Ethan Deyle ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Gadus Morhua ◽  
Fishery Management ◽  
Atlantic Cod ◽  
Stock Assessment ◽  
Fish Stock ◽  
Recovery Potential ◽  
Stock Recruitment ◽  
Model Approach ◽  
Non Parametric

Abstract The stock–recruitment relationship is the basis of any stock prediction and thus fundamental for fishery management. Traditional parametric stock–recruitment models often poorly fit empirical data, nevertheless they are still the rule in fish stock assessment procedures. We here apply a multi-model approach to predict recruitment of 20 Atlantic cod (Gadus morhua) stocks as a function of adult biomass and environmental variables. We compare the traditional Ricker model with two non-parametric approaches: (i) the stochastic cusp model from catastrophe theory and (ii) multivariate simplex projections, based on attractor state-space reconstruction. We show that the performance of each model is contingent on the historical dynamics of individual stocks, and that stocks which experienced abrupt and state-dependent dynamics are best modelled using non-parametric approaches. These dynamics are pervasive in Western stocks highlighting a geographical distinction between cod stocks, which have implications for their recovery potential. Furthermore, the addition of environmental variables always improved the models’ predictive power indicating that they should be considered in stock assessment and management routines. Using our multi-model approach, we demonstrate that we should be more flexible when modelling recruitment and tailor our approaches to the dynamical properties of each individual stock.

V̇o2, V̇co2, and RQ in a respiratory chamber: accurate estimation based on a new mathematical model using the Kalman-Bucy method

2004 ◽  
Vol 96 (3) ◽  
pp. 1045-1054 ◽  
Author(s):  
L. Granato ◽  
A. Brandes ◽  
C. Bruni ◽  
A. V. Greco ◽  
G. Mingrone
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Production Rates ◽  
Estimation Errors ◽  
Respiratory Chamber ◽  
Residual Noise ◽  
Proposed Model

A respiratory chamber is used for monitoring O2 consumption (V̇o2), CO2 production (V̇co2), and respiratory quotient (RQ) in humans, enabling long term (24-h) observation under free-living conditions. Computation of V̇o2 and V̇co2 is currently done by inversion of a mass balance equation, with no consideration of measurement errors and other uncertainties. To improve the accuracy of the results, a new mathematical model is suggested in the present study explicitly accounting for the presence of such uncertainties and error sources and enabling the use of optimal filtering methods. Experiments have been realized, injecting known gas quantities and estimating them using the proposed mathematical model and the Kalman-Bucy (KB) estimation method. The estimates obtained reproduce the known production rates much better than standard methods; in particular, the mean error when fitting the known production rates is 15.6 ± 0.9 vs. 186 ± 36 ml/min obtained using a conventional method. Experiments with 11 humans were carried out as well, where V̇o2 and V̇co2 were estimated. The variance of the estimation errors, produced by the KB method, appears relatively small and rapidly convergent. Spectral analysis is performed to assess the residual noise content in the estimates, revealing large improvement: 2.9 ± 0.8 vs. 3,440 ± 824 (ml/min)2 and 1.8 ± 0.5 vs. 2,057 ± 532 (ml/min)2, respectively, for V̇o2 and V̇co2 estimates. Consequently, the accuracy of the computed RQ is also highly improved (0.3 × 10-4 vs. 800 × 10-4). The presented study demonstrates the validity of the proposed model and the improvement in the results when using a KB estimation method to resolve it.

How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters?

2001 ◽  
Vol 58 (11) ◽  
pp. 2284-2297 ◽  
Author(s):  
E Rivot ◽  
E Prévost ◽  
E Parent
Keyword(s):  
Stochastic Model ◽  
Measurement Errors ◽  
Methodological Approach ◽  
Data Sets ◽  
Posterior Distributions ◽  
Informative Priors ◽  
Ricker Model ◽  
Stock Recruitment

We present a Bayesian approach of a Ricker stock-recruitment (S/R) analysis accounting for measurement errors on S/R data. We assess the sensitivity of posterior inferences to (i) the choice of Ricker model parameterizations, with special regards to management-related ones, and (ii) prior parameter distributions. Closed forms for Ricker parameter posterior distributions exist given S/R data known without error. We use this property to develop a procedure based on the Rao–Blackwell formula. This procedure achieves integration of measurement errors by averaging these closed forms over possible S/R data sets sampled from distributions derived from a stochastic model relating field data to the S and R variables. High-quality Bayesian estimates are obtained. The analysis of the influence of different parameterizations and of the priors is made easier. We illustrate our methodological approach by a case study of Atlantic salmon (Salmo salar). Posterior distributions for S and R are computed from a mark–recapture stochastic model. Ignoring measurement errors underestimates parameter uncertainty and overestimates both stock productivity and density dependence. We warn against using management-related parameterizations because it makes the strong prior assumption of long-term sustainability of stocks. Posterior inferences are sensitive to the choice of prior. The use of informative priors as a remedy is discussed.

Detecting environmental regimes in fish stock-recruitment relationships by fuzzy logic

2001 ◽  
Vol 58 (11) ◽  
pp. 2139-2148 ◽  
Author(s):  
D G Chen
Keyword(s):  
Fuzzy Logic ◽  
Environmental Variable ◽  
Clupea Harengus ◽  
Fish Stock ◽  
Environmental Regimes ◽  
Fuzzy Logic Approach ◽  
Stock Recruitment ◽  
Logic Approach ◽  
Traditional Approaches ◽  
The Impact

A fuzzy logic approach is developed to model and test the impact of environmental regimes on fish stock–recruitment relationships. Traditional methods use environmental variables to classify stock–recruitment data into different membership percentiles followed by fitting the stock–recruitment models for each subset. In contrast, the fuzzy logic approach uses a continuous membership function to provide a rational basis for the classification. Thus, parameter estimation is based on a more logically consistent foundation without resorting to subjective partitions. This new approach is applied to herring stock from the west coast of Vancouver Island (Clupea harengus pallasi) using sea surface temperature as the environmental variable and to Pacific halibut stock (Hippoglossus stenolepis) using the Pacific Decadal Oscillation as the environmental variable. From these applications, the herring stock–recruitment relationships were found to vary significantly during different regimes, whereas this was not the case for halibut. However, in both instances, the fuzzy logic approach demonstrated that density-dependent effects differed between regimes. The fuzzy logic model consistently outperformed traditional approaches as measured by several diagnostic criteria. Because fuzzy logic models address uncertainty better than traditional approaches, they have the potential to improve our ability to understand factors influencing stock–recruitment relationships and thereby manage fisheries more effectively.

Alternative Recruitment Models of Adams River Sockeye Salmon, Oncorhynchus nerka

1987 ◽  
Vol 44 (9) ◽  
pp. 1551-1561 ◽  
Author(s):  
Jeremy S. Collie ◽  
Carl J. Walters
Keyword(s):  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Status Quo ◽  
Management Policy ◽  
Best Management ◽  
Interaction Terms ◽  
Ricker Model ◽  
The Status ◽  
Stock Recruitment ◽  
High Yields

Despite evidence of depensatory interactions among year-classes of Adams River sockeye salmon (Oncorhynchus nerka), the best management policy is one of equal escapement for all year-classes. We fit alternative models (Ricker model and Larkin model) to 32 yr of stock–recruitment data and checked, using simulation tests, that the significant interaction terms in the Larkin model are not caused by biases in estimating the parameters. We identified a parameter set (Rationalizer model) for which the status quo cyclic escapement policy is optimal, but this set fits the observed data very poorly. Thus it is quite unlikely that the Rationalizer model is correct or that the status quo escapement policy is optimal. Using the fitted stock–recruitment parameters, we simulated the sockeye population under several management policies. The escapement policy optimal under the Ricker model is best overall because of the high yields if it should be correct. If the three stock–recruitment models are equally likely to be correct, the simulations predict that adopting a constant-escapement policy would increase long-term yield 30% over the current policy and that an additional 15% increase in yield could be obtained if the policy were actively adaptive.

Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

2021 ◽  
Author(s):  
Hongmei Xu ◽  
Juan Liu ◽  
Kun Wang ◽  
Songtao Kong ◽  
Yong Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Properties ◽  
Measurement Errors ◽  
Fuzzy Inference ◽  
Quantum Particle ◽  
Estimation Method ◽  
Particle Swarm ◽  
Temperature Dependent ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

scholarly journals Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 399 ◽  
Author(s):  
Marco Riani ◽  
Anthony C. Atkinson ◽  
Aldo Corbellini ◽  
Domenico Perrotta
Keyword(s):  
Robust Regression ◽  
Robust Statistics ◽  
Estimation Method ◽  
Estimation Procedure ◽  
Breakdown Point ◽  
Parameter Estimates ◽  
Normal Density ◽  
Density Power Divergence ◽  
Numerical Minimization ◽  
Power Divergence

Minimum density power divergence estimation provides a general framework for robust statistics, depending on a parameter α , which determines the robustness properties of the method. The usual estimation method is numerical minimization of the power divergence. The paper considers the special case of linear regression. We developed an alternative estimation procedure using the methods of S-estimation. The rho function so obtained is proportional to one minus a suitably scaled normal density raised to the power α . We used the theory of S-estimation to determine the asymptotic efficiency and breakdown point for this new form of S-estimation. Two sets of comparisons were made. In one, S power divergence is compared with other S-estimators using four distinct rho functions. Plots of efficiency against breakdown point show that the properties of S power divergence are close to those of Tukey’s biweight. The second set of comparisons is between S power divergence estimation and numerical minimization. Monitoring these two procedures in terms of breakdown point shows that the numerical minimization yields a procedure with larger robust residuals and a lower empirical breakdown point, thus providing an estimate of α leading to more efficient parameter estimates.

scholarly journals Population variability under stressors is dependent on body mass growth and asymptotic body size

2020 ◽  
Vol 7 (2) ◽  
pp. 192011
Author(s):  
Leonie Färber ◽  
Rob van Gemert ◽  
Øystein Langangen ◽  
Joël M. Durant ◽  
Ken H. Andersen
Keyword(s):  
Body Mass ◽  
Life Histories ◽  
Random Noise ◽  
Fish Stock ◽  
Vital Rates ◽  
Anthropogenic Pressures ◽  
Fish Stocks ◽  
Asymptotic Size ◽  
Wide Range ◽  
Stock Recruitment

The recruitment and biomass of a fish stock are influenced by their environmental conditions and anthropogenic pressures such as fishing. The variability in the environment often translates into fluctuations in recruitment, which then propagate throughout the stock biomass. In order to manage fish stocks sustainably, it is necessary to understand their dynamics. Here, we systematically explore the dynamics and sensitivity of fish stock recruitment and biomass to environmental noise. Using an age-structured and trait-based model, we explore random noise (white noise) and autocorrelated noise (red noise) in combination with low to high levels of harvesting. We determine the vital rates of stocks covering a wide range of possible body mass (size) growth rates and asymptotic size parameter combinations. Our study indicates that the variability of stock recruitment and biomass are probably correlated with the stock's asymptotic size and growth rate. We find that fast-growing and large-sized fish stocks are likely to be less vulnerable to disturbances than slow-growing and small-sized fish stocks. We show how the natural variability in fish stocks is amplified by fishing, not just for one stock but for a broad range of fish life histories.

scholarly journals Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Camilo Cortés ◽  
Luis Unzueta ◽  
Ana de los Reyes-Guzmán ◽  
Oscar E. Ruiz ◽  
Julián Flórez
Keyword(s):  
Estimation Method ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Marker Position ◽  
Human Movements ◽  
Posture Estimation ◽  
Kinematic Models ◽  
Method Accuracy

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

scholarly journals Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1259 ◽  
Author(s):  
Guodong Li ◽  
Jinsong Wu ◽  
Taolin Tang ◽  
Zhixin Chen ◽  
Jun Chen ◽  
...  
Keyword(s):  
Time Delay ◽  
Correlation Functions ◽  
Estimation Method ◽  
Digital Signal ◽  
Low Complexity ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Estimation Errors ◽  
Underwater Acoustic ◽  
Amplitude Fluctuations

This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments.

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