An integrated likelihood for distance sampling data
A Model-Based Approach for Making Ecological Inference from Distance Sampling Data
... for sampling of small areas or complex habitats (Ramsey and Harrison, 2004) such as surveys of river dolphin (Vidal et ...the likelihood for fitting the detec- tion function (Laake et ...an integrated ...
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Model selection with overdispersed distance sampling data
... We simulated movements of 10, 12, and 15 animals within 1 km 2 study areas in old growth, regrowing, and recently-logged habitats, respectively. Each animal started with a random initial location and heading, after which ...
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Adaptive distance sampling
... survey data with an effort factor of less than ...completed sampling in the region, allowing these data to be included in the estimate without biasing the abundance ...
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Distance sampling in R
... Acknowledgments The authors thank the two anonymous reviewers for their constructive comments, which have greatly improved the paper and package. The authors would like to thank the many users of Distance, mrds ...
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Model based distance sampling
... Model-Based Distance Sampling ...Conventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal ...
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Statistical efficiency in distance sampling
... Distance sampling is a technique for estimating the abundance of animals or other objects in a region, allowing for imperfect ...tance sampling estimator arising from uncertainty about the unknown ...
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Spatial models for distance sampling data : recent developments and future directions
... 2006). Our aims in creating a spatial model of a biological popu- lation are usually twofold: (i) estimating overall abundance and (ii) investigating the relationship between abundance and environmental covariates. As ...
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An integrated data management and video system for sampling aquatic benthos
... allow data acquired in the field to be captured and stored in a database, and retrieved and summarised to allow the progress of the field sampling to be followed and modified if necessary ...1). Data ...
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Estimating from Cross sectional Categorical Data Subject to Misclassification and Double Sampling: Moment based, Maximum Likelihood and Quasi Likelihood Approaches
... categorical data in the presence of misclassification and double ...double sampling context we assume that along with the main measurement device, which is subject to misclassification, we have a secondary ...
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Mixed effect models in distance sampling
... analysing distance sampling count data from such ...the distance data, from which an offset is estimated to account for imperfect detection within the surveyed strip or ...
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Incorporating animal movement into distance sampling
... Abstract Distance sampling is a popular statistical method to esti- mate the density of wild animal ...Conventional distance sampling represents animals as fixed points in space that are ...
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INTEGRATED EFFECT OF DATA CLEANING AND SAMPLING ON DECISION TREE LEARNING OF LARGE DATA SETS
... of data available for data ...The data contains the noise or outlier data to some extent which hampers the classification performance of classifier built on that training ...large data ...
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The effect of data analysis strategies in density estimation of mountain ungulates using distance sampling
... Our results, however, agree with those obtained by Southwell and Weaver ( 1993 ) regarding the loss of precision in estimates when working with truncated and/or binned data. This work also suggests that DS ...
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A Composite Likelihood Approach to Analysis of Survey Data with Sampling Weights Incorporated under Two-Level Models
... 3. Corresponding author: Grace Y. Yi, E-mail: [email protected] Abstract Multi-level models provide a convenient framework for analyzing data from survey sam- ples with hierarchical structures. Inferential ...
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Modified maximum likelihood estimators using ranked set sampling
... set sampling by using the modified maximum likelihood ...maximum likelihood estimators of these parameters under RSS do not exist since the likelihood equations involve nonlinear functions and ...
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Semiparametric fractional imputation using empirical likelihood in survey sampling
... The results under model (E2) are similar and the coverage rates are close to the nominal rate. 6.2 Simulation Two In the second simulation study, we use 2013–2014 U.S. National Health Examination and Nutrition Survey ...
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From distance sampling to spatial capture recapture
... Abstract Distance sampling and capture–recapture are the two most widely used wildlife abundance estimation ...for distance sampling methods to incorporated spatial models rather than to rely ...
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Mixture models for distance sampling detection functions
... example data sets on specific taxa, we note that there is no limitation to the species or survey setup that mixture model detection functions can be used ...survey data where traditional methods produce ...
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Bayesian methods for hierarchical distance sampling models
... 4) which cannot be due to prior sensitivity as we used uniform priors on all parameters for the Bayesian approach. We assume these differences may have been due to the fact that – as opposed to the two-stage approach – ...
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Non uniform coverage estimators for distance sampling
... coverage sampling estimator. However, the unequal coverage sampling estimator was also implemented using the true animal distribution to compute the ...
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