scholarly journals A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices?

2008 ◽  
Vol 65 (6) ◽  
pp. 1024-1035 ◽  
Author(s):  
Verena M. Trenkel
Keyword(s):  
Random Effects ◽  
Stock Assessment ◽  
Random Effect ◽  
Simulated Data ◽  
Fish Stock ◽  
Model Parameters ◽  
Biomass Growth ◽  
Two Stage ◽  
Commercial Catch ◽  
Nested Models

A simple two-stage biomass random effects population dynamics model is presented for carrying out fish stock assessments based on survey indices using no commercial catch information. Recruitment and biomass growth are modelled as random effects, reducing the number of model parameters while maintaining model flexibility. No assumptions regarding natural mortality rates are required. The performance of the method was evaluated using simulated data with emphasis on identifying parameter redundancy, which showed that the variance of the biomass growth random effect might only be estimable if large (>0.2). The full and two nested models were fitted to European anchovy ( Engraulis encrasicolus ) in the Bay of Biscay using two survey series. The best-fitting model had fixed biomass growth and random recruitment following a lognormal distribution.

scholarly journals The Bayesian Covariance Structure Model for Testlets

2020 ◽  
pp. 107699862094120
Author(s):  
Jean-Paul Fox ◽  
Jeremias Wenzel ◽  
Konrad Klotzke
Keyword(s):  
Random Effects ◽  
Random Effect ◽  
Covariance Structure ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Random Effects Model ◽  
Structure Model ◽  
Modeling Framework ◽  
Irt Models ◽  
Covariance Structure Model

Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.

Impacts of outliers and mis-specification of priors on Bayesian fisheries-stock assessment

2000 ◽  
Vol 57 (11) ◽  
pp. 2293-2305 ◽  
Author(s):  
Y Chen ◽  
P A Breen ◽  
N L Andrew
Keyword(s):  
Normal Distribution ◽  
Stock Assessment ◽  
Management Strategies ◽  
Distribution Functions ◽  
Assessment Model ◽  
Fish Stock ◽  
Model Parameters ◽  
Prior Distributions ◽  
Likelihood Functions ◽  
Robust Likelihood

Bayesian inference is increasingly used in estimating model parameters for fish-stock assessment, because of its ability to incorporate uncertainty and prior knowledge and to provide information for risk analyses in evaluating alternative management strategies. Normal distributions are commonly used in formulating likelihood functions and informative prior distributions; these are sensitive to data outliers and mis-specification of prior distributions, both common problems in fisheries-stock assessment. Using a length-structured stock-assessment model for a New Zealand abalone fishery as an example, we evaluate the robustness of three likelihood functions and two prior-distribution functions, with respect to outliers and mis-specification of priors, in 48 different combinations. The two robust likelihood estimators performed slightly less well when no data outliers were present and much better when data outliers were present. Similarly, the Cauchy distribution was less sensitive to prior mis-specification than the normal distribution. We conclude that replacing the normal distribution with "fat-tailed" distributions for likelihoods and priors can improve Bayesian assessments when there are data outliers and mis-specification of priors, with relatively minor costs when there are none.

scholarly journals Properties of the full random effect modelling approach with missing covariates

2019 ◽  
Author(s):  
Joakim Nyberg ◽  
E. Niclas Jonsson ◽  
Mats O. Karlsson ◽  
Jonas Häggström ◽  
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Simulated Data ◽  
Reference Method ◽  
Parameter Estimates ◽  
Full Model ◽  
Covariate Data ◽  
Model Method ◽  
Covariate Information

SummaryTwo full model approaches was compared with respect to their ability to handle missing covariate information. The reference data analysis approach was the full model method in which the covariate effects are estimated conventionally using fixed effects, and missing covariate data is imputed with the median of the non-missing covariate information. This approach was compared to a novel full model method which treats the covariate data as observed data and estimates the covariates as random effects. A consequence of this way of handling the covariates is that no covariate imputation is required and that any missingness in the covariates is handled implicitly. The comparison between the two analysis methods was based on simulated data from a model of height for age z-scores as a function of age. Data was simulated with increasing degrees of randomly missing covariate information (0-90%) and analyzed using each of the two analysis approaches. Not surprisingly, the precision in the parameter estimates from both methods decreased with increasing degrees of missing covariate information. However, while the bias in the parameter estimates increased in a similar fashion for the reference method, the full random effects approach provided unbiased estimates for all degrees of covariate missingness.

scholarly journals Using survey data independently from commercial data in stock assessment: an example using haddock in ICES Division VIa

2005 ◽  
Vol 62 (5) ◽  
pp. 996-1005 ◽  
Author(s):  
D.J. Beare ◽  
C.L. Needle ◽  
F. Burns ◽  
D.G. Reid
Keyword(s):  
Survey Data ◽  
Stock Assessment ◽  
Assessment Process ◽  
Fish Stock ◽  
Trawl Survey ◽  
Commercial Catch ◽  
Spatially Resolved ◽  
Abundance Estimates ◽  
Class Strength ◽  
Downward Spiral

Abstract Currently standard fish stock biomass estimates are based most directly on commercial catch-at-age data. The main contribution made by research-vessel trawl survey data to the stock assessment process is to “tune” trends in the commercial data and provide estimates of incoming year-class strength. In this process much of the information contained with the survey data (e.g. spatial detail) is lost because the data are first aggregated into numbers-at-age indices for given areas. Another problem is that increasingly restrictive total allowable catches (TACs) imposed on the fishing industry have led to what is suspected to be widespread misreporting, i.e. the scientists do not know how many fish have been landed. This leads to negative biases in the catch data, low stock abundance estimates by scientists, even lower TACs, followed by even more misreporting. One potential way to escape this downward spiral is to explore scientific trawl survey data in more detail since trawl surveys are more straightforward to regulate. Traditionally, there has been resistance to this idea since, in comparison to commercial catch-at-age data, trawl survey data are very sparse in space and time. In this study, the potential for using trawl survey data independently in stock assessments is explored for the case of ICES Area VIa haddock, using two different tools. Findings suggest that it is possible to get qualitatively useful information from trawl survey data alone as well as quantitative, spatially resolved, estimates of fish abundance by making simple swept-area assumptions. In addition, interesting differences between survey and commercial data are highlighted by the study. The mean age of fish reported by the commercial fleet, for example, is higher than that reflected by the survey data, while fishing mortality estimates tend to be higher when estimated from survey data alone.

scholarly journals Applying a two-stage Bayesian dynamic model to a short lived species, the anchovy in the Aegean Sea (Eastern Mediterranean). Comparison with an Integrated Catch at Age stock assessment model.

2014 ◽  
Vol 15 (2) ◽  
pp. 350 ◽  
Author(s):  
M. GIANNOULAKI ◽  
L. IBAIBARRIAGA ◽  
K. ANTONAKAKIS ◽  
A. URIARTE ◽  
A. MACHIAS ◽  
...  
Keyword(s):  
Egg Production ◽  
Missing Values ◽  
Stock Assessment ◽  
Eastern Mediterranean ◽  
Aegean Sea ◽  
Production Method ◽  
Assessment Model ◽  
Two Stage ◽  
Commercial Catch ◽  
The North

Two different stock assessment models were applied to the North Aegean Sea anchovy stock (Eastern Mediterranean Sea): an Integrated Catch at age Analysis and a Bayesian two-stage biomass based model. Commercial catch data over the period 2000-2008 as well as acoustics and Daily Egg Production Method estimates over the period 2003-2008 were used. Both models results were consistent, indicating that anchovy stock is exploited sustainably in relation to an exploitation rate reference point. Further, the stock biomass appears stable or increasing. However, the limitations in age-composition data, potential problems related to misinterpretation of age readings along with the existence of missing values in the survey data seem to favour the two-stage biomass method, which is based on a simplified age structure.  

scholarly journals Subjective judgement in data subsetting: implications for CPUE standardisation and stock assessment of non-target chondrichthyans

2011 ◽  
Vol 62 (6) ◽  
pp. 734 ◽  
Author(s):  
J. M. Braccini ◽  
M.-P. Etienne ◽  
S. J. D. Martell
Keyword(s):  
Ad Hoc ◽  
Stock Assessment ◽  
Fishing Effort ◽  
Reference Points ◽  
Fish Stock ◽  
Model Parameters ◽  
Total Allowable Catch ◽  
Subjective Judgement ◽  
Definition Of ◽  
Catch Per Effort

Standardisation of catch-per-effort (CPUE) data is an essential component for nearly all stock assessments. The first step in CPUE standardisation is to separate the comparable from the non-comparable catch and effort records and this is normally done based on subjective rules. In the present study, we used catch-and-effort data from the elephant fish (Callorhinchus milii) to illustrate the differences in CPUE when using expert judgement to define different ad hoc selection criteria used to subset these data. The data subsets were then used in the standardisation of CPUE and the stock assessment of elephant fish. The catch-and-effort subsets produced different patterns of precision and trends, each of which led to different estimates (and related uncertainty) of model parameters and management reference points. For most CPUE series, there was a very high probability that the elephant fish stock is overexploited and that overfishing is occurring. The estimates of total allowable catch (TAC) and the uncertainty around these estimates also varied considerably depending on the CPUE series used. Our study shows how sensitive TAC estimation is when there is high uncertainty in the definition of the fishing effort targeted at the species analysed.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

scholarly journals Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽  
2021 ◽  
Author(s):  
Marco Zuin ◽  
Gianluca Rigatelli ◽  
Claudio Bilato ◽  
Carlo Cervellati ◽  
Giovanni Zuliani ◽  
...  
Keyword(s):  
Random Effects ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Final Analysis ◽  
Short Term ◽  
Random Effects Models ◽  
Pooled Prevalence ◽  
Statistical Heterogeneity ◽  
Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

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