scholarly journals Standardizing compositional data for stock assessment

2014 ◽  
Vol 71 (5) ◽  
pp. 1117-1128 ◽  
Author(s):  
James T. Thorson
Keyword(s):  
Sample Size ◽  
Approximation Method ◽  
Compositional Data ◽  
Normal Approximation ◽  
Stock Assessment ◽  
Simulated Data ◽  
Future Research ◽  
Effective Sample Size ◽  
Sampling Intensity ◽  
Index Standardization

Abstract Stock assessment models frequently integrate abundance index and compositional (e.g. age, length, sex) data. Abundance indices are generally estimated using index standardization models, which provide estimates of index standard errors while accounting for: (i) differences in sampling intensity spatially or over time; (ii) non-independence of available data; and (iii) the effect of covariates. However, compositional data are not generally processed using a standardization model, so effective sample size is not routinely estimated and these three issues are unresolved. I therefore propose a computationally simple “normal approximation” method for standardizing compositional data and compare this with design-based and Dirichlet-multinomial (D-M) methods for analysing compositional data. Using simulated data from a population with multiple spatial strata, heterogeneity within strata, differences in sampling intensity, and additional overdispersion, I show that the normal-approximation method provided unbiased estimates of abundance-at-age and estimates of effective sample size that are consistent with the imprecision of these estimates. A conventional design-based method also produced unbiased age compositions estimates but no estimate of effective sample size. The D-M failed to account for known differences in sampling intensity (the proportion of catch for each fishing trip that is sampled for age) and hence provides biased estimates when sampling intensity is correlated with variation in abundance-at-age data. I end by discussing uses for “composition-standardization models” and propose that future research develop methods to impute compositional data in strata with missing data.

scholarly journals Spatiotemporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment

2019 ◽  
Vol 76 (3) ◽  
pp. 401-414 ◽  
Author(s):  
James T. Thorson ◽  
Melissa A. Haltuch
Keyword(s):  
Sample Size ◽  
Compositional Data ◽  
Stock Assessment ◽  
Future Research ◽  
Sex Composition ◽  
Abundance Index ◽  
Composition Data ◽  
Model Based ◽  
Stock Assessments ◽  
The Impact

Stock assessment models are fitted to abundance-index, fishery catch, and age–length–sex composition data that are estimated from survey and fishery records. Research has developed spatiotemporal methods to estimate abundance indices, but there is little research regarding model-based methods to generate age–length–sex composition data. We demonstrate a spatiotemporal approach to generate composition data and a multinomial sample size that approximates the estimated imprecision. A simulation experiment comparing spatiotemporal and design-based methods demonstrates a 32% increase in input sample size for the spatiotemporal estimator. A Stock Synthesis assessment used to manage lingcod (Ophiodon elongatus) in the California Current also shows a 17% increase in sample size and better model fit using the spatiotemporal estimator, resulting in smaller standard errors when estimating spawning biomass. We conclude that spatiotemporal approaches are feasible for estimating both abundance-index and compositional data, thereby providing a unified approach for generating inputs for stock assessments. We hypothesize that spatiotemporal methods will improve statistical efficiency for composition data in many stock assessments and recommend that future research explore the impact of including additional habitat or sampling covariates.

Simulation testing a new multi-stage process to measure the effect of increased sampling effort on effective sample size for age and length data

2020 ◽  
Vol 77 (5) ◽  
pp. 1728-1737 ◽  
Author(s):  
James T Thorson ◽  
Meaghan D Bryan ◽  
Peter-John F Hulson ◽  
Haikun Xu ◽  
André E Punt
Keyword(s):  
Sample Size ◽  
Stock Assessment ◽  
Model Misspecification ◽  
Size Analysis ◽  
Effective Sample Size ◽  
Sampling Effort ◽  
Age Composition ◽  
Composition Data ◽  
Multi Stage ◽  
The Impact

Abstract Ocean management involves monitoring data that are used in biological models, where estimates inform policy choices. However, few science organizations publish results from a recurring, quantitative process to optimize effort spent measuring fish age. We propose that science organizations could predict the likely consequences of changing age-reading effort using four independent and species-specific analyses. Specifically we predict the impact of changing age collections on the variance of expanded age-composition data (“input sample size”, Analysis 1), likely changes in the variance of residuals relative to stock-assessment age-composition estimates (“effective sample size”, Analysis 2), subsequent changes in the variance of stock status estimates (Analysis 3), and likely impacts on management performance (Analysis 4). We propose a bootstrap estimator to conduct Analysis 1 and derive a novel analytic estimator for Analysis 2 when age-composition data are weighted using a Dirichlet-multinomial likelihood. We then provide two simulation studies to evaluate these proposed estimators and show that the bootstrap estimator for Analysis 1 underestimates the likely benefit of increased age reads while the analytic estimator for Analysis 2 is unbiased given a plausible mechanism for model misspecification. We conclude by proposing a formal process to evaluate changes in survey efforts for stock assessment.

scholarly journals An Effective Sample Size for Predicting Plant Disease Incidence in a Spatial Hierarchy

1999 ◽  
Vol 89 (9) ◽  
pp. 770-781 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Sample Size ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Simulated Data ◽  
Heterogeneous Data ◽  
Sampling Unit ◽  
Effective Sample Size ◽  
Incidence Data ◽  
Eutypa Dieback ◽  
Number Of Individuals

For aggregated or heterogeneous disease incidence, one can predict the proportion of sampling units diseased at a higher scale (e.g., plants) based on the proportion of diseased individuals and heterogeneity of diseased individuals at a lower scale (e.g., leaves) using a function derived from the beta-binomial distribution. Here, a simple approximation for the beta-binomial-based function is derived. This approximation has a functional form based on the binomial distribution, but with the number of individuals per sampling unit (n) replaced by a parameter (v) that has similar interpretation as, but is not the same as, the effective sample size (ndeff ) often used in survey sampling. The value of v is inversely related to the degree of heterogeneity of disease and generally is intermediate between ndeff and n in magnitude. The choice of v was determined iteratively by finding a parameter value that allowed the zero term (probability that a sampling unit is disease free) of the binomial distribution to equal the zero term of the beta-binomial. The approximation function was successfully tested on observations of Eutypa dieback of grapes collected over several years and with simulated data. Unlike the beta-binomial-based function, the approximation can be rearranged to predict incidence at the lower scale from observed incidence data at the higher scale, making group sampling for heterogeneous data a more practical proposition.

scholarly journals Unbalanced Sample Size Introduces Spurious Correlations to Genome-wide Heterozygosity Analyses

2020 ◽  
Author(s):  
Li Liu ◽  
Richard J Caselli
Keyword(s):  
Genetic Diversity ◽  
Sample Size ◽  
High Throughput Sequencing ◽  
Rare Variants ◽  
Meta Analysis ◽  
Simulated Data ◽  
Disease Status ◽  
Future Research ◽  
Data Sets ◽  
Genome Wide

AbstractExcess of heterozygosity (H) is a widely used measure of genetic diversity of a population. As high-throughput sequencing and genotyping data become readily available, it has been applied to investigating the associations of genome-wide genetic diversity with human diseases and traits. However, these studies often report contradictory results. In this paper, we present a meta-analysis of five whole-exome studies to examine the association of H scores with Alzheimer’s disease. We show that the mean H score of a group is not associated with the disease status, but is associated with the sample size. Across all five studies, the group with more samples has a significantly lower H score than the group with fewer samples. To remove potential confounders in empirical data sets, we perform computer simulations to create artificial genomes controlled for the number of polymorphic loci, the sample size and the allele frequency. Analyses of these simulated data confirm the negative correlation between the sample size and the H score. Furthermore, we find that genomes with a large number of rare variants also have inflated H scores. These biases altogether can lead to spurious associations between genetic diversity and the phenotype of interest. Based on these findings, we advocate that studies shall balance the sample sizes when using genome-wide H scores to assess genetic diversities of different populations, which helps improve the reproducibility of future research.

Model-based estimates of effective sample size in stock assessment models using the Dirichlet-multinomial distribution

2017 ◽  
Vol 192 ◽  
pp. 84-93 ◽  
Author(s):  
James T. Thorson ◽  
Kelli F. Johnson ◽  
Richard D. Methot ◽  
Ian G. Taylor
Keyword(s):  
Sample Size ◽  
Stock Assessment ◽  
Multinomial Distribution ◽  
Effective Sample Size ◽  
Model Based

Evaluation of alternative modelling approaches to account for spatial effects due to age-based movement

2017 ◽  
Vol 74 (11) ◽  
pp. 1832-1844 ◽  
Author(s):  
Hui-Hua Lee ◽  
Kevin R. Piner ◽  
Mark N. Maunder ◽  
Ian G. Taylor ◽  
Richard D. Methot
Keyword(s):  
Population Dynamics ◽  
Stock Assessment ◽  
Simulated Data ◽  
Assessment Method ◽  
Error Modeling ◽  
Assessment Model ◽  
Future Research ◽  
Observation Error ◽  
Time Varying ◽  
Spatial Effects

Spatial patterns due to age-specific movement have been a source of unmodelled process error. Modeling movement in spatially explicit stock assessments is feasible, but hampered by a paucity of data from appropriate tagging studies. This study uses simulation analyses to evaluate alternative model structures that either explicitly or implicitly account for the process of time-varying age-based movement in a population dynamics model. We simulated synthetic populations using a two-area stochastic population dynamics operating model. Simulated data were fit in seven different estimation models. Only the model that includes the correct spatial dynamic results in unbiased and precise estimates of derived and management quantities. In a single-area assessment model, using the fleets-as-area (FAA) approach may be the second best option to estimate both length-based and time-varying age-based selectivity to implicitly account for the contact selectivity and annual availability. An FAA approach adds additional observation error performed nearly as well. Future research could evaluate which stock assessment method is robust to uncertainty in movement and is more appropriate for achieving intended management objectives.

scholarly journals Effects of process and observation errors on effective sample size of fishery and survey age and length composition using variance ratio and likelihood methods

2011 ◽  
Vol 68 (7) ◽  
pp. 1548-1557 ◽  
Author(s):  
Peter-John F. Hulson ◽  
Dana H. Hanselman ◽  
Terrance J. Quinn
Keyword(s):  
Sample Size ◽  
Likelihood Function ◽  
Stock Assessment ◽  
Age Distribution ◽  
Multinomial Distribution ◽  
Effective Sample Size ◽  
Observation Error ◽  
Variance Ratio ◽  
Likelihood Methods ◽  
Process Error

Abstract Hulson, P-J. F., Hanselman, D. H., and Quinn, T. J. II. 2011. Effects of process and observation errors on effective sample size of fishery and survey age and length composition using variance ratio and likelihood methods. – ICES Journal of Marine Science, 68: 1548–1557. Observations of age or length composition from fisheries or research surveys are modelled frequently with the multinomial distribution. Violations of multinomial assumptions in data collection usually cause overdispersion of observations and consequent underestimation of uncertainty. This has led to the adoption of an effective sample size less than the actual sample size to approximate the likelihood function for age or length composition better in, for example, fishery stock assessment models. The behaviour of effective sample size is examined under different scenarios for population age distribution and sampling design. Effective sample size was approximated with three approaches: (i) the ratio of multinomial to empirical variance; (ii) sampling estimation; and (iii) the Dirichlet likelihood. The most significant changes in effective sample size were attributable to process error involving aggregation of ages within schools. In terms of observation error, effective sample size can be increased by increasing the number of tows from which samples are obtained for age or length composition, then, because of the reduced uncertainty in effective sample size, the Dirichlet likelihood can be integrated into the objective function of fishery stock assessment models to estimate the effective sample size in future assessments.

Antecedents of Family Conflict in Uganda

2020 ◽  
Vol 12 (3) ◽  
pp. 35-54
Author(s):  
Rogers Matama ◽  
Kezia H. Mkwizu
Keyword(s):  
Content Analysis ◽  
Social Identity ◽  
Sample Size ◽  
Social Identity Theory ◽  
Family Conflict ◽  
Qualitative Approach ◽  
Future Research ◽  
Family Conflicts ◽  
The Core ◽  
Quantitative Studies

The purpose of this study was to explore the antecedents of family conflict in Uganda. A qualitative approach was used in this study. A sample size of 139 participants provided data which was subjected to content analysis. Results revealed that the core themes associated with family conflict are finances and priority of resources. Further findings show that differences in tastes and interests, selfishness and lack of communication played a key role as causes of family conflicts. The implication of this study is that finances and priority of resources are antecedents of family conflict in the context of Uganda. Therefore, the antecedents of family conflict that emerged from this study can be understood, defined and analyzed through the lens of social identity theory. Future research may include conducting quantitative studies with a particular demographic using the themes that have emerged from this study.

A Note on the Accuracy of Normal Approximation of Random Quantities

2021 ◽  
Vol 73 (1) ◽  
pp. 62-67
Author(s):  
Ibrahim A. Ahmad ◽  
A. R. Mugdadi
Keyword(s):  
Sample Size ◽  
Order Statistic ◽  
Normal Approximation ◽  
Order Approximation ◽  
Random Variables ◽  
Random Variable ◽  
Limiting Distribution ◽  
Exact Order ◽  
Fixed Sample ◽  
Independent Identically Distributed

For a sequence of independent, identically distributed random variable (iid rv's) [Formula: see text] and a sequence of integer-valued random variables [Formula: see text], define the random quantiles as [Formula: see text], where [Formula: see text] denote the largest integer less than or equal to [Formula: see text], and [Formula: see text] the [Formula: see text]th order statistic in a sample [Formula: see text] and [Formula: see text]. In this note, the limiting distribution and its exact order approximation are obtained for [Formula: see text]. The limiting distribution result we obtain extends the work of several including Wretman[Formula: see text]. The exact order of normal approximation generalizes the fixed sample size results of Reiss[Formula: see text]. AMS 2000 subject classification: 60F12; 60F05; 62G30.

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