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Adaptive Line Transect Sampling
Adaptive line transect sampling offers the potential of improved population density estimation efficiency over conventional line transect sampling when populations are spatially clustered. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of observations.
S T Buckland
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Line Transect Sampling in Small and Large Regions
Biometrics, 2005SummaryMelville and Welsh(2001,Biometrics57,1130–1137) consider an approach to line transect sampling using a separate calibration study to estimate the detection functiong. They present a simulation study contrasting their results with poor results from a traditional estimator, labeled the “Buckland” estimator and referenced toBuckland et al.(1993 ...
Rachel Fewster +2 more
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Some Mathematical Models for Line Transect Sampling
Biometrical Journal, 1985AbstractANDERSON and POSPAHALA (1970) investigated the estimation of wildlife population size using the belt or line transect sampling method and devised a correction for bias, thus leading to an estimator with interesting characteristics. This work was given a uniform mathematical framework in BURNHAM and ANDERSON (1976).
Anderson, D. R. +2 more
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Line Transect Sampling from a Curving Path
Biometrics, 2001Cutting straight line transects through dense forest is time consuming and expensive when large areas need to be surveyed for rare or highly clustered species. We argue that existing paths or game trails may be suitable as transects for line transect sampling even though they will not, in general, run straight.
Lex Hiby
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Local likelihood density estimation in line transect sampling
Environmetrics, 2000A novel semiparametric estimator for the probability density function of detected distances in line transect sampling is proposed. The estimator is obtained using a local likelihood density estimation approach, a technique recently proposed which affords the advantages of both parametric and nonparametric methods, i.e. accuracy and robustness. Moreover,
Lucio Barabesi
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A bias-corrected histogram estimator for line transect sampling
Communications in Statistics - Theory and Methods, 2018ABSTRACTThe classical histogram method has already been applied in line transect sampling to estimate the parameter f(0), which in turns is used to estimate the population abundance D or the popula...
Omar Eidous, Fahid Al-Eibood
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A Semiparametric Model for Line Transect Sampling
Communications in Statistics - Theory and Methods, 2006This paper introduces an appealing semiparametric model for estimating wildlife abundance based on line transect data. The proposed method requires the existence of a parametric model and then improves the estimator using a kernel method. Properties of the resultant estimator are derived and an expression for the asymptotic mean square error (AMSE) of ...
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On Improving Kernel Estimators Using Line Transect Sampling
Communications in Statistics - Theory and Methods, 2005ABSTRACT We investigate some possibilities for improvements of kernel density estimates using line transect sampling. A bias correction approach is used which produces a new estimator for density of object D. The asymptotic bias of kernel estimator is subtracted from the kernel estimator to form the proposed estimator.
Omar Eidous
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On the Line Transect Sampling Method
Biometrics, 1974Theory has been developed in the present paper for estimating wildlife density and its standard error by the line transect method of sampling using both right angle and radial distances and based on different assumptions than those made by Gates et al. [1968]. It is shown that relatively better fits are realized when the assumptions are tested with the
Sen, A. R. +2 more
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Size Bias in Line Transect Sampling
Biometrics, 1987Line transect sampling is used to estimate the number of individuals in a study area. It is usually assumed that the detections of individuals constitute independent events, and that the probability of detecting an item is a function of its perpendicular distance from the transect.
Drummer, Thomas D., McDonald, Lyman L.
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