Empirical and theoretical analyses of correction of time-series bias in stock-recruitment relationships of sockeye salmon (Oncorhynchus nerka)

1995 ◽  
Vol 52 (10) ◽  
pp. 2174-2189 ◽  
Author(s):  
Josh Korman ◽  
Randall M. Peterman ◽  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Bias Correction ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Empirical Relationship ◽  
Estimation Method ◽  
Estimation Methods ◽  
Theoretic Approach ◽  
Stock Recruitment

Using data from 30 sockeye salmon (Oncorhynchus nerka) stocks and Monte Carlo simulations, we examined the importance of time-series bias on estimates of optimal harvest rate, optimal escapement, and sustainable yield. We compared the performance of the least-squares procedure for fitting a Ricker curve with an existing bias-correction method. Simulations showed that the effect of time-series bias is greatest for low-productivity stocks that exhibit a high degree of autocorrelation among residuals of the stock-recruitment relationship. A strong inverse empirical relationship between autocorrelation and stock productivity among the 30 stocks suggests that time-series bias is a more important concern for low-productivity northern stocks than for more productive southern stocks. The corrected method reduced bias in optimal escapement estimates under a limited set of conditions but at the price of increased variance in the estimates. For a constant escapement goal policy, using the bias correction thus resulted in sustainable yields slightly lower than or equal to expected values for 28 of the 30 stocks compared with yields obtained using the standard least-squares estimation method. We demonstrate the value of using a decision theoretic approach to evaluate the performance of estimation methods.

scholarly journals X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION

2013 ◽  
Vol 30 (1) ◽  
pp. 201-251 ◽  
Author(s):  
Chirok Han ◽  
Peter C. B. Phillips ◽  
Donggyu Sul
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Limit Distribution ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Estimation Method ◽  
Estimation Methods ◽  
Finite Sample ◽  
Dynamic Panel ◽  
Dynamic Panel Model

This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, generalized method of moments (GMM), and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of then/Tratio, which makes the approach appealing for practical work. In the time series AR(1) case (n= 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even smalln. Some simulation results are reported, giving comparisons with other dynamic panel estimation methods.

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.

Application of Bayesian decision analysis to management of a sockeye salmon (Oncorhynchus nerka) fishery

1998 ◽  
Vol 55 (1) ◽  
pp. 86-98 ◽  
Author(s):  
Christina A Robb ◽  
Randall M Peterman
Keyword(s):  
Decision Analysis ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Data Uncertainty ◽  
Optimal Decision ◽  
Management Decision ◽  
Bayesian Decision ◽  
Stock Recruitment ◽  
Salmon Stock ◽  
Parameter Values

We developed a decision-making framework for management of a sockeye salmon (Oncorhynchus nerka) fishery on the Nass River, British Columbia, that explicitly accounts for uncertainties in (i) the stock-recruitment relationship, (ii) annual recruitment, (iii) run timing, and (iv) catchability. The method used Monte Carlo simulation within a decision analysis framework and used Bayesian statistics to calculate probabilities for parameter values in the Shepherd stock-recruitment model. The decision dealt with when to open a fishery, upstream of all normal fishing areas, that is intended to harvest fish that are considered surplus to spawning requirements. The optimal decision rule for opening this fishery depended on (i) the relative importance of different management objectives and (ii) the range of shapes of the stock-recruitment relationship that were admitted as possible within the decision analysis. The management decision that was optimal if we assumed a dome-shaped stock-recruitment relationship was not optimal when we admitted the possibility of other shapes of the relationship. Therefore, given the variability in salmon stock-recruitment data, uncertainty in the shape of the stock-recruitment relationship should be routinely considered in analyses of management decisions.

scholarly journals Effects of marine-derived nutrients on population dynamics of sockeye salmon (Oncorhynchus nerka)

2008 ◽  
Vol 65 (8) ◽  
pp. 1635-1648 ◽  
Author(s):  
Tadayasu Uchiyama ◽  
Bruce P. Finney ◽  
Milo D. Adkison
Keyword(s):  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Isotope Analysis ◽  
Juvenile Salmon ◽  
Juvenile Survival ◽  
Water Residence Time ◽  
Nitrogen Stable Isotope ◽  
Factors Affecting ◽  
Stock Recruitment ◽  
Marine Derived Nutrients

The effects of marine-derived nutrients (MDN) on the productivity of sockeye salmon ( Oncorhynchus nerka ) stocks in Alaska, USA, were examined through nitrogen stable isotope analysis of smolts and mathematical models of the sockeye stock–recruit relationship. Smolt δ15N was used to infer the degree to which smolts depend on MDN for their growth. We found that characteristics of sockeye nursery lakes and watersheds significantly affected the availability of MDN to juvenile sockeye. The magnitude of escapement and water residence time were the most important factors affecting the MDN availability to juvenile salmon. Analysis of stock–recruit models indicated that regional environmental fluctuations had a large effect on stock productivities. However, stock–recruitment data showed little evidence that increasing MDN input to nursery lakes increased stock productivities. Stock–recruitment data may be poorly suited to detection of the influence of MDN because of the multitude of factors that influence juvenile survival in the first several years of their life.

Different inference approaches for the estimators of the sushila distribution

2021 ◽  
Vol 16 (4) ◽  
pp. 251-260
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Ricardo Puziol de Oliveira ◽  
Edson Zangiacomi Martinez ◽  
Jorge Alberto Achcar
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Bayesian Method ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Method ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Estimation Methods

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series

2020 ◽  
Author(s):  
Souleymane Sy ◽  
Fabio Madonna ◽  
Emanuele Tramutola ◽  
Marco Rosoldi ◽  
Monica Proto ◽  
...  
Keyword(s):  
Time Series ◽  
Estimation Method ◽  
Estimation Methods ◽  
Climate Trend ◽  
New Approach ◽  
Future Evolution ◽  
Regression Methods ◽  
Current State ◽  
The Difference ◽  
Night And Day

<p>Inaccurate climate trend detections may lead to incorrect conclusions about the current state and future evolution of the climate. Trend estimation based on the use of radiosonde historical time series may be significantly affected by the choice of the estimation method. In addition, the dataset subsampling both in time (due to gaps in the data records) and in space (due to need of selecting the most reliable subset of stations for each specific application) can further increase the trend uncertainty. </p><p>Uncertainties of trend estimations have been quantified in few past investigations, considering the difference between pairs of regression methods, although limited to datasets affected by several inhomogeneities and characterized by smaller trend rates than those observed over the last two decades.</p><p>This work, carried out in the frame of the Copernicus Climate Change Service (C3S), aims to examine the sensitivity of trend estimations to linear estimation methods and to subsampling effects. The analysis is carried out using about 600 historical radiosounding time series for the period 1978-2018 available within version 2 of the Integrated Global Radiosonde Archive (IGRA).</p><p>The sensitivity of linear trends to the choice regression methods and the subsampling effects have been quantified through the comparison of four regression methods (parametric and non-parametric). The uncertainties introduced by missing data in each time series has been also quantified using a new approach, selecting different samples of stations with different amounts of missing monthly data equivalent to 0, 5, 10 and 20 years from 1978 to present. Instead, the spatial subsampling effects are quantified artificially reducing the size of the IGRA stations.</p><p>The presented work will shortly discuss results obtained for temperature and relative humidity for both night and day times  (at 0000 and 1200 UTC, respectively) at different pressure levels and latitudes.</p>

scholarly journals Nonparametric and parametric methods of spectral analysis

2019 ◽  
Vol 283 ◽  
pp. 07002 ◽  
Author(s):  
Hangfang Zhao ◽  
Lin Gui
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
High Resolution ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Stationary Time Series ◽  
Estimation Methods ◽  
Practical Applications ◽  
Small Variance

Spectral Analysis is one of the most important methods in signal processing. In practical application, it is critical to discuss the power spectral density estimation of finite data sampled from some stationary time series. A spectral estimator is expected to have good statistical properties such as consistency, high resolution and small variance. For one spectral estimation method, there exists a trade-off between high resolution and small variance. The paper provides a comparison of several popular spectral methods from both theoretical properties and practical applications. We first address several basic nonparametric methods, whose statistical characters are analysed. Then we explain the connections and differences between temporal windowing and lag windowing. Thereafter, the confidence intervals of both windows are given and used to evaluate the estimated results. Besides, several different parametric estimation methods of autoregressive time series are compared, and whose properties and effects are also introduced. Building on our understanding of these studies, we then apply parametric and nonparametric spectral estimation methods on the data of ocean surface wave height.

scholarly journals Detection of main tidal frequencies using least squares harmonic estimation method

2012 ◽  
Vol 2 (3) ◽  
pp. 224-233 ◽  
Author(s):  
R. Mousavian ◽  
M. Mashhadi Hossainali
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Estimation Method ◽  
Maximum Amplitude ◽  
Eastern Coast ◽  
Level Data ◽  
Tidal Constituents ◽  
Least Squares Harmonic Estimation ◽  
Bandar Abbas ◽  
The Uk

AbstractIn this paper the efficiency of the method of Least Squares Harmonic Estimation (LS-HE) for detecting the main tidal frequencies is investigated. Using this method, the tidal spectrum of the sea level data is evaluated at two tidal stations: Bandar Abbas in south of Iran and Workington on the eastern coast of the UK. The amplitudes of the tidal constituents at these two tidal stations are not the same. Moreover, in contrary to the Workington station, the Bandar Abbas tidal record is not an equispaced time series. Therefore, the analysis of the hourly tidal observations in Bandar Abbas and Workington can provide a reasonable insight into the efficiency of this method for analyzing the frequency content of tidal time series. Furthermore, applying the method of Fourier transform to the Workington tidal record provides an independent source of information for evaluating the tidal spectrum proposed by the LS-HE method. According to the obtained results, the spectrums of these two tidal records contain the components with the maximum amplitudes among the expected ones in this time span and some new frequencies in the list of known constituents. In addition, in terms of frequencies with maximum amplitude; the power spectrums derived from two aforementioned methods are the same. These results demonstrate the ability of LS-HE for identifying the frequencies with maximum amplitude in both tidal records.

Cyclic Dominance and Optimal Escapement of Adams River Sockeye Salmon (Oncorhynchus nerka)

1990 ◽  
Vol 47 (4) ◽  
pp. 838-849 ◽  
Author(s):  
D. W. Welch ◽  
D. J. Noakes
Keyword(s):  
Sockeye Salmon ◽  
Net Present Value ◽  
Oncorhynchus Nerka ◽  
Total Yield ◽  
Confidence Regions ◽  
Parameter Estimates ◽  
Magnitude Comparison ◽  
Ricker Model ◽  
Stock Recruitment ◽  
Potential Benefits

We examined escapement policies for a stock-recruitment model with negative between-year interactions. Regardless of the degree of interaction present, the optimal policy is to always equalize escapement. Parameter estimates obtained for the Adams River sockeye salmon (Oncorhynchus nerka) indicate that between-year interactions may occur, but confidence regions include the null hypothesis of no interaction at all (the Ricker model). We conclude that the extreme amplitude of the current recruitment cycle in this stock frustrates statistical identification of interaction. It seems unlikely that between-year interactions will be measurable until the off year runs increase by at least two to three orders of magnitude. Comparison of total yields for the Adams River sockeye shows that an equal escapement policy could increase yields by at least 35% over that obtained by the current cyclic escapement pattern. This is equivalent to obtaining an additional $27 million in total yield per annum from the Adams River stock alone and, assuming a discount rate of 4%, translates into an increase in net present value of $675 million. If between-year interactions do not exist, the potential benefits of moving to an equal escapement policy are even larger, on the order of $3 to $4 billion.

scholarly journals Comparative analysis of sigma-based, quantile-based and time series VaR estimators

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Marta Małecka
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Risk Measure ◽  
Estimation Method ◽  
Monte Carlo Study ◽  
Estimation Methods ◽  
Prediction Errors ◽  
International Banking ◽  
Series Length ◽  
Historical Simulation

Since its inception at the end of the XX century, VaR risk measure has gained massive popularity. It is synthetic, easy in interpretation and offers comparability of risk levels reported by different institutions. However, the crucial idea of comparability of reported VaR levels stays in contradiction with the differences in estimation procedures adopted by companies. The issue of the estimation method is subject to the internal company decision and is not regulated by the international banking supervision.The paper was dedicated to comparative analysis of the prediction errors connected with competing VaR estimation methods. Four methods, among which two stationarity-based – variance-covariance and historical simulation – and two time series methods – GARCH and RiskMetricsTM – were compared through the Monte Carlo study. The analysis was conducted with respect to the method choice, series length and VaR tolerance level.The study outcomes showed the superiority of the sigma-based method of variance-covariance over the quantile-based historical simulation. Furthermore the comparison of the stationarity-based estimates to the time series results showed that allowing for time-varying parameters in the estimation technique significantly reduces the estimator bias and variance.

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