Bias correction for histogram estimator using line transect sampling

2005 ◽  
Vol 16 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Omar M. Eidous
Keyword(s):  
Bias Correction ◽  
Line Transect ◽  
Line Transect Sampling ◽  
Transect Sampling ◽  
Histogram Estimator

A bias-corrected histogram estimator for line transect sampling

2018 ◽  
Vol 47 (15) ◽  
pp. 3675-3686
Author(s):  
Omar Eidous ◽  
Fahid Al-Eibood
Keyword(s):  
Line Transect ◽  
Line Transect Sampling ◽  
Transect Sampling ◽  
Histogram Estimator

scholarly journals Estimating a resource selection function with line transect sampling

2002 ◽  
Vol 6 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Bryan F. J. Manly
Keyword(s):  
Resource Selection ◽  
Probability Function ◽  
Resource Selection Function ◽  
Line Transect ◽  
Selection Function ◽  
Line Transect Sampling ◽  
Selection Probability ◽  
Transect Sampling ◽  
Conditional Maximum ◽  
Habitat Variables

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.

scholarly journals KEPADATAN POPULASI MAMALIA DARAT KARNIVORA DI CAMP LEAKEY KAWASAN TAMAN NASIONAL TANJUNG PUTING, KALIMANTAN TENGAH

Jurnal BIOMA ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. 83
Author(s):  
Siwi Arthapati Mandiri ◽  
Paskal Sukandar ◽  
Yossa Istiadi
Keyword(s):  
Data Collection ◽  
Population Density ◽  
National Park ◽  
Line Transect ◽  
Line Transect Sampling ◽  
Clouded Leopard ◽  
Helarctos Malayanus ◽  
Transect Sampling ◽  
Palm Civet ◽  
Leopard Cat

Borneo has wide land that support high biodiversity. One of them is Tanjung Puting National Park (TPNP), which have biodiversity such as terrestrial mammalian carnivore. Carnivore has a role to maintain its ecosystems. But, there are no data for population density of terrestrial mammalian carnivore. The object of this research is to find out population density of terrestrial mammalian carnivore in Camp Leakey, TPNP, Central Borneo. This research accomplished in September-October 2015 in Camp Leakey. Using line-transect sampling. Data collection was accomplished at 18.00-24.00 Central Indonesian Time (WITA) on eight transects with three times replication by direct surveys and indirect surveys. This research has obtained five species, malayan sun bear (Helarctos malayanus), sunda clouded leopard (Neofelis diardi), leopard cat, and group of civet, like small-toothed palm civet (Arctogalidia trivirgata) and asian palm civet (Paradoxurus hermaphroditus). Population density of each species from the highest to the lowest is 13,5 Individual of leopard cat/km2, 9,84 Individual of malayan sun bears/km2, 4,31 Individual of sunda clouded leopard/km2, and 3,65 Individual of civet/km2. Malayan sun bears, sunda clouded leopards and civets prefer to be in land forest. Leopard cats prefers to be in transition forest.

scholarly journals Performance of Parametric Model for Line Transect Data

2020 ◽  
pp. 1-7
Author(s):  
Noryanti Muhammad ◽  
Gamil A.A. Saeed ◽  
Wan Nur Syahidah Wan Yusoff
Keyword(s):  
Simulation Study ◽  
Present Model ◽  
Parametric Model ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Detection Function ◽  
Transect Sampling ◽  
Proposed Model ◽  
Shoulder Condition

One of the most important sides of life is wildlife. There is growing research interest in monitoring wildlife. Line transect sampling is one of the techniques widely used for estimating the density of objects especially for animals and plants. In this research, a parametric estimator for estimation of the population abundance is developed. A new parametric model for perpendicular distances for detection function is utilised to develop the estimator. In this paper, the performance of the parametric model which was developed using a simulation study is presented. The detection function has non-increasing curve and a perfect probability at zero. Theoretically, the parametric model which has been developed is guar-anteed to satisfy the shoulder condition assumption. A simulation study is presented to validate the present model. Relative mean error (RME) and Relative Bias (RB) are used to compare the estimator with well-known existing estimators. The results of the simulation study are discussed, and the performance of the proposed model shows promising statistical properties which outperformed the existing models. Keywords: detection function, line transect data, parametric model

scholarly journals Double Kernel Method Using Line Transect Sampling

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Omar Eidous ◽  
M.K. Shakhatreh
Keyword(s):  
Kernel Method ◽  
Kernel Estimator ◽  
Real Data ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Numerical Examples ◽  
Bootstrap Techniques ◽  
Transect Sampling ◽  
Kernel Type

A double kernel method as an alternative to the classical kernel method is proposed to estimate the population abundance by using line transect sampling. The proposed method produces an estimator that is essentially a kernel type of estimator use the kernel estimator twice to improve the performances of the classical kernel estimator. The feasibility of using bootstrap techniques to estimate the bias and variance of the proposed estimator is also addressed. Some numerical examples based on simulated and real data are presented. The results show that the proposed estimator outperforms existingclassical kernel estimator in most considered cases.

Line Transect Sampling

2006 ◽  
Author(s):  
E. C. Pielou
Keyword(s):  
Line Transect ◽  
Line Transect Sampling ◽  
Transect Sampling

Some Aspects of Line Transect Sampling When the Target Populations Moves

Biometrics ◽  
1979 ◽  
Vol 35 (1) ◽  
pp. 323 ◽  
Author(s):  
G. E. J. Smith
Keyword(s):  
Line Transect ◽  
Line Transect Sampling ◽  
Target Populations ◽  
Transect Sampling

Asymptotic properties of estimators derived from line transect sampling

1983 ◽  
Vol 12 (8) ◽  
pp. 849-864 ◽  
Author(s):  
James R. Lackritz ◽  
Richard L. Scheaffer
Keyword(s):  
Asymptotic Properties ◽  
Line Transect ◽  
Line Transect Sampling ◽  
Transect Sampling
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