Estimates Of Population Parameters Research Articles

Incorporating stakeholders' values into environmental decision support: A Bayesian Belief Network approach

Participatory modelling increases the transparency of environmental planning and management processes and enhances the mutual understanding among different parties. We present a sequential probabilistic approach to involve stakeholders' views in the formal decision support process. A continuous Bayesian Belief Network (BBN) model is used to estimate population parameters for stakeholder groups, based on samples of individual value judgements. The approach allows quantification and visualization of the variability in views among and within stakeholder groups. Discrete BBN is populated with these parameters, to summarize and visualize the information and to link it to a larger decision analytic influence diagram (ID). As part of ID, the resulting discrete BBN element serves as a distribution-form decision criteria in probabilistic evaluation of alternative management strategies, to help find a solution that represents the optimal compromise in the presence of potentially conflicting objectives. We demonstrate our idea using example data from the field of marine spatial planning. However, this approach is applicable to many types of management cases. We suggest that by advancing the mutual understanding and concrete participation this approach can further facilitate the stakeholder involvement also during the various stages of the environmental management process.

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

Integrating distance sampling with minimum counts to improve monitoring

ABSTRACTMinimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection‐corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape‐scale distance sampling surveys with local‐scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south‐central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52–0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise ( = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split‐panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. © 2019 The Wildlife Society.

Development and Evaluation of an Optimal Composite Estimator in Spatial Microsimulation Small Area Estimation

A range of data is of geographic interest but is not available at a small area level from existing data sources. Small area estimation (SAE) offers techniques to estimate population parameters of target variables to detailed scales based on relationships between those target variables and relevant auxiliary variables. The resulting indirect small area estimate can deliver a lower mean squared error compared to its direct survey estimate, given that variance can be reduced markedly even if bias increases. Spatial microsimulation SAE approaches are widely utilized but only beginning to engage with the potential of composite estimators that use a weighted combination of indirect and direct estimators to reduce further the mean squared error of the small area estimate compared to an indirect SAE estimator alone. This article advances these approaches by constructing for the first time in the microsimulation literature an optimal composite estimator for such SAE approaches in which the combining weight is calculated from the mean squared errors of the two estimators; thus, optimizing the reduction in MSE of the resulting small area estimates. This optimal composite estimator is demonstrated and evaluated in a model‐based simulation study and application based on the real data.

Incorporating citizen science data in spatially explicit integrated population models.

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

On use of Ranked Set Sampling for estimating Super-population Total: Gamma Population Model

Utilization of superpopulation models for estimation of population parameters is an advantageous practice, when it is easy to recognize the relationship between the study variable with one or more auxiliary variables. This article is concerned with estimation of finite population total under a new ranked set sampling approach, ranked set sampling without replacement (RSSWOR), using so called gamma population model (GPM). Behavior of the proposed estimator, in term of relative efficiency, is studied for various choices of a constant γ via Monte Carle experiment. The provided simulation study shows the superiority of the proposed estimator over existing estimator under same model. The sampling procedure, especially, aids in collecting data from a continuous production process.

Towards a reliable assessment of Asian elephant population parameters: the application of photographic spatial capture\u2013recapture sampling in a priority floodplain ecosystem

The hitherto difficult task of reliably estimating populations of wide-ranging megafauna has been enabled by advances in capture–recapture methodology. Here we combine photographic sampling with a Bayesian spatially-explicit capture–recapture (SCR) model to estimate population parameters for the endangered Asian elephant Elephas maximus in the productive floodplain ecosystem of Kaziranga National Park, India. Posterior density estimates of herd-living adult females and sub-adult males and females (herd-adults) was 0.68 elephants/km2 (95% Credible Intervals, CrI = 0.56−0.81) while that of adult males was 0.24 elephants/km2 (95% CrI = 0.18−0.30), with posterior density estimates highlighting spatial heterogeneity in elephant distribution. Estimates of the space-usage parameter suggested that herd-adults ({hat{sigma }}_{HA} = 5.91 km, 95% CrI = 5.18–6.81) moved around considerably more than adult males ({hat{sigma }}_{AM} = 3.64 km, 95% CrI = 3.09–4.34). Based on elephant movement and age–sex composition, we derived the population that contributed individuals sampled in Kaziranga to be 908 herd-adults, 228 adult males and 610 young (density = 0.46 young/km2, SD = 0.06). Our study demonstrates how SCR is suited to estimating geographically open populations, characterising spatial heterogeneity in fine-scale density, and facilitating reliable monitoring to assess population status and dynamics for science and conservation.

Use of coefficient of variation in calibration estimation of population mean in stratified sampling

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets.

Mixture of Truthful–Untruthful Responses in Public Surveys

SummaryWhen sensitive issues are surveyed, collecting truthful data and obtaining reliable estimates of population parameters is a persistent problem in many fields of applied research mostly in sociological, economic, demographic, ecological and medical studies. In this context, and moving from the so‐called negative survey, we consider the problem of estimating the proportion of population units belonging to the categories of a sensitive variable when collected data are affected by measurement errors produced by untruthful responses. An extension of the negative survey approach is proposed herein in order to allow respondents to release a true response. The proposal rests on modelling the released data with a mixture of truthful and untruthful responses that allows researchers to obtain an estimate of the proportions as well as the probability of receiving the true response by implementing the EM‐algorithm. We describe the estimation procedure and carry out a simulation study to assess the performance of the EM estimates vis‐à‐vis certain benchmark values and the estimates obtained under the traditional data‐collection approach based on direct questioning that ignores the presence of misreporting due to untruthful responding. Simulation findings provide evidence on the accuracy of the estimates and permit us to appreciate the improvements that our approach can produce in public surveys, particularly in election opinion polls, when the hidden vote problem is present.

Population Pharmacokinetics of Pemetrexed in Adult Non-Small Cell Lung Cancer in Indian Patients.

The objective of the study was to develop a population pharmacokinetic model of pemetrexed and identify factors contributing to variability in exposure in Indian patients. Plasma samples were obtained from a cohort of 85 patients following 500 mg/m2 intravenous infusion and population pharmacokinetic analysis was performed using NONMEM (version 7.3.0). The stochastic approximation expectation maximization method was used to estimate parameters. The full covariate model approach was used by specifying clinically meaningful covariates a priori. Credible intervals obtained using Markov chain Monte Carlo Bayesian analysis were used to reduce the full covariate model by eliminating the covariates whose CI included the null. Model qualification was performed using visual predictive check and bootstrap. The final population parameter estimates and relative standard error for clearance (CL) was 3.3 L/h (10.8), central volume of distribution (V1) was 5.2 L (7.8), peripheral volume of distribution (V2) was 5.9 L (14.5) and intercompartmental clearance (Q) was 6.8 L/h (14.3). A large between-subject variability (50%-108% coefficient of variation) was observed in pharmacokinetic parameters. The percent coefficient of variation for the area under the plasma concentration-time curve from time zero to infinity was 72% and for maximum concentration was 68.25%. Diagnostic plots showed no major bias in the model. The final model included V1, V2, and Q scaled to body surface area raised to a fixed exponent of 1. Creatinine clearance and sex on clearance and albumin on V1 were statistically significant covariates based on Bayesian credible interval. However, traditional bootstrap resulted in a 95% confidence interval of the sex effect parameter including null. Given the size and nonsignificant sex effect in traditional bootstrap, it is considered clinically not significant.

Reliability estimation under type-II censored data from the generalized Bilal distribution

The main object of this article is the estimation of the unknown population parameters and the reliability function for the generalized Bilal model under type-II censored data. Both maximum likelihood and Bayesian estimates are considered. In the Bayesian framework, although we have discussed mainly the squared error loss function, any other loss function can easily be considered. Gibb’s sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples, which have been used to compute the Bayes estimates and also to construct their corresponding credible intervals with the help of two different importance sampling techniques. A simulation study is carried out to examine the accuracy of the resulting Bayesian estimates and compare them with their corresponding maximum likelihood estimates. Application to a real data set is considered for the sake of illustration.

Pseudo-population bootstrap methods for imputed survey data.

The most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and population quantiles. A simulation study suggests that the methods perform well in terms of relative bias and coverage probability.

Subsampling populations with spatially structured traits: a field comparison of stratified and random strategies

Scientific surveys are widely used for stock assessment, but the estimated population parameters are based on the size-at-age relationship and age structure derived from a small subsample of the catch that is aged. This calls for an assessment of subsampling strategies, especially when population’s life history traits are spatially structured. In the Eastern Bering Sea, Pacific cod (Gadus macrocephalus) size and age are spatially structured, with younger and smaller individuals being more abundant at shallower depths. We conducted parallel subsamplings during Pacific cod surveys to compare two contrasting subsampling strategies: length-stratified and random. Geographical heterogeneity of Pacific cod length resulted in divergent estimates of ages between subsampling strategies. When this spatial variability was taken into account to estimate population parameters, random strategy provided more accurate mean and modal size-at-age and estimated age structure. Bias in the length-stratified subsampling arises from the poor efficacy in capturing the geographical patterns of size observed in the population. However, combining age data samples from multiple years helps to minimize the divergences between the two strategies.

IMPUTATION OF MISSING VALUES BY USING RAW MOMENTS

Abstract The estimation of population parameters might be quite laborious and inefficient, when the sample data have missing values. In comparison follow-up visits, the method of imputation has been found to be a cheaper procedure from a cost point of view. In the present study, we can enhance the performance of imputation procedures by utilizing the raw moments of the auxiliary information rather than their ranks, especially, when the ranking of the auxiliary variable is expensive or difficult to do so. Equations for bias and mean squared error are obtained by large sample approximation. Through the numerical and simulation studies it can be easily understood that the proposed method of imputation can outperform their counterparts.

Population pharmacokinetics of lenalidomide in patients with B-cell malignancies.

Lenalidomide is an immunomodulatory imide drug used broadly in the treatment of multiple myeloma and lymphoma. It continues to be evaluated in chronic lymphocytic leukaemia (CLL) at lower doses due to dose-related toxicities including tumour flare and tumour lysis syndrome. This study aimed to develop a population pharmacokinetic model for lenalidomide in multiple cancers, including CLL, to identify any disease-related differences in disposition. Lenalidomide concentrations from 4 clinical trials were collated (1999 samples, 125 subjects), covering 4 cancers (multiple myeloma, CLL, acute myeloid leukaemia and acute lymphoblastic leukaemia) and a large dose range (2.5-75mg). A population pharmacokinetic model was developed with NONMEM and patient demographics were tested as covariates. The data were best fitted by a 1-compartment kinetic model with absorption described by 7 transit compartments. Clearance and volume of distribution were allometrically scaled for fat-free mass. The population parameter estimates for apparent clearance, apparent volume of distribution and transit rate constant were 12L/h (10.8-13.6), 68.8L (61.8-76.3), and 13.5h-1 (11.9-36.8) respectively. Patients with impaired renal function (creatinine clearance <30mL/min) exhibited a 22% reduction in lenalidomide clearance compared to patients with creatinine clearance of 90mL/min. Cancer type had no discernible effect on lenalidomide disposition. This is the first report of a lenalidomide population pharmacokinetic model to evaluate lenalidomide pharmacokinetics in patients with CLL and compare its pharmacokinetics with other B-cell malignancies. As no differences in pharmacokinetics were found between the observed cancer-types, the unique toxicities observed in CLL may be due to disease-specific pharmacodynamics.

Application of the extractable lipofuscin aging method to estimate mortality and population dynamics of the burrowing shrimp, Neotrypaea californiensis

Lack of robust aging methods for crustaceans has inhibited the use of age-structured population models. Individuals are often classified based on body size, but differences in growth can bias parameter estimates. Our study applied the lipofuscin aging method combined with catch-curve analysis to estimate mortality rate for the burrowing shrimp, Neotrypaea californiensis. This species is an important member of the estuarine community with an impact on oyster production along the US West Coast. Randomized surveys were conducted from 2011 to 2014 to estimate population abundance, average density, and age structure in Yaquina Bay, Oregon. Mortality rate was estimated to be 0.719 yr−1 (95% CI; 0.633–0.793 yr−1) and did not vary significantly across cohorts. The spatial extent of the survey revealed spatial patterns in shrimp density that could be explained by variation in mortality and recruitment rates. This is the first study to apply lipofuscin aging to estimate population parameters of an estuarine crustacean and the methods we present can inform managers seeking to incorporate population ecology into management plans for N. californiensis and other crustacean species worldwide.

K-Sample comparisons using propensity analysis.

In this paper, we investigate K-group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. In order to overcome this issue, we conduct propensity analysis and match the subjects with similar propensity scores across treatment groups or compare weighted group means (or weighted survival curves for censored outcome variables) using the inverse probability weighting (IPW). To this end, multinomial logistic regression has been a popular propensity analysis method to estimate the weights. We propose to use decision tree method as an alternative propensity analysis due to its simplicity and robustness. We also propose IPW rank statistics, called Dunnett-type test and ANOVA-type test, to compare 3 or more treatment groups on survival endpoints. Using simulations, we evaluate the finite sample performance of the weighted rank statistics combined with these propensity analysis methods. We demonstrate these methods with a real data example. The IPW method also allows us for unbiased estimation of population parameters of each treatment group. In this paper, we limit our discussions to survival outcomes, but all the methods can be easily modified for any type of outcomes, such as binary or continuousvariables.

Kernel density estimation from complex surveys in the presence of complete auxiliary information

Auxiliary information is widely used in survey sampling to enhance the precision of estimators of finite population parameters, such as the finite population mean, percentiles, and distribution function. In the context of complex surveys, we show how auxiliary information can be used effectively in kernel estimation of the superpopulation density function of a given study variable. We propose two classes of “model-assisted” kernel density estimators that make efficient use of auxiliary information. For one class we assume that the functional relationship between the study variable Y and the auxiliary variable X is known, while for the other class the relationship is assumed unknown and is estimated using kernel smoothing techniques. Under the first class, we show that if the functional relationship can be written as a simple linear regression model with constant error variance, the mean of the proposed density estimator will be identical to the well-known regression estimator of the finite population mean. If we drop the intercept from the linear model and allow the error variance to be proportional to the auxiliary variable, the mean of the proposed density estimator matches the ratio estimator of the finite population mean. The properties of the new density estimators are studied under a combined design-model-based inference framework, which accounts for the underlying superpopulation model as well as the randomization distribution induced by the sampling design. Moreover, the asymptotic normality of each estimator is derived under both design-based and combined inference frameworks when the sampling design is simple random sampling without replacement. For the practical implementation of these estimators, we discuss how data-driven bandwidth estimators can be obtained. The finite sample properties of the proposed estimators are addressed via simulations and an example that mimics a real survey. These simulations show that the new estimators perform very well compared to standard kernel estimators which do not utilize the auxiliary information.

PKconverter: R package to convert the pharmacokinetic parameters.

Population pharmacokinetic analysis and modeling procedures typically require estimates of both population and individual pharmacokinetic parameters. However, only some of these parameters are contained in models and only parameters in the model can be estimated. In this paper, we introduce a new R package, PKconverter, to calculate pharmacokinetic parameters using the relationships among them. After fitting the model, other parameters can be calculated from the functional relationship among the parameters. PKconverter provides the functions to calculate whole parameters along with a Shiny application for converting the parameters. With this package, it is also possible to calculate the standard errors of the other parameters that are not in the model and estimate individual parameters simultaneously.

Estadísticos demográficos de Dactylopius opuntiae Cockerell (Hemiptera: Dactylopiidae) en condiciones de invernadero

A nivel nacional e internacional, la cochinilla silvestre Dactylopius opuntiae es considerada el enemigo principal del nopal (Opuntia spp.)debido a los daños severos y a las pérdidas económicas que ocasiona. En otros países se han realizado estudios para conocer su biología, comportamiento y crecimiento poblacional; pero a pesar de su importancia socioeconómica, en México se han realizado pocas investigaciones sobre esta especie. El objetivo de este estudio fue estimar la supervivencia y reproducción de D. opuntiae en condiciones semicontroladas, a fin de generar información básica que permita implementar una estrategia de manejo y reducir su impacto nocivo. Para conocer el ciclo de vida y determinar los parámetros poblacionales de D. opuntiae se establecieron cohortes en cladodios individuales de Opuntia ficus-indicacv. Villanueva; éstas se monitorearon de agosto a diciembre del 2017. Durante su ciclo de desarrollo se registraron supervivencia (lx), proporción sexual ♀/♂ y fertilidad de hembras adultas (mx). Los resultados mostraron que la tasa neta de reproducción (Ro) fue de 26.09, expresada en el número de hembras recién nacidas por hembra; el tiempo de generación (T) fue de 63.61 d y la tasa intrínseca de crecimiento poblacional (rm) fue de 0.05 hembras∙d-1. Esta estrategia reproductiva y otras características permiten a D. opuntiae colonizar rápidamente la planta; debido a ello, la especie puede ser un fitófago de importancia económica en zonas donde el género Opuntiaes silvestre o cultivo.