Bias correction for histogram estimator using line transect sampling

Eidous, Omar

doi:10.1002/env.671

Cited by 13 publications

(4 citation statements)

References 14 publications

(17 reference statements)

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“…The negative exponential model and the half normal model are the most prominent models. Gates et al (1968) suggested the negative exponential model with detection function, The corresponding pdf is,…”

Section: Some Parametric Estimatorsmentioning

confidence: 99%

Vol. 12, No. 2 (Full Issue)

2013

J. Mod. App. Stat. Meth.

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. Normality is a distributional requirement of classical test statistics. In order for the test statistic to provide valid results leading to sound and reliable conclusions this requirement must be satisfied. In the not too distant past, it was claimed that violations of normality would not likely jeopardize scientific findings (See Hsu & Feldt, 1969; Lunney, 1970). Recent revelations suggest otherwise (See e.g., Micceri, 1989; Keselman, Huberty, Lix et al., 1998;Erceg-Hurn, Wilcox, & Keselman, 2013; Wilcox and Keselman, 2003; Wilcox, 2012a, b). Unfortunately the data obtained in psychological investigations rarely, if ever, meet the requirement of normally distributed data (Micceri, 1989; Wilcox, 2012a, b). Consequently, it could be the case that the results from many of the investigations conducted in psychology provide invalid results. Accordingly, authors recommend that researchers attempt to assess the validity of assuming data are normal in form prior to conducting a test of significance (Erceg-Hurn, et al., 2013; Keselman, et al., 1998). Present evidence suggests that a popular fit-statistic, the Kolmogorov-Smirnov test does a poor job of evaluating whether data are normal. Our investigation based on this statistic and other fit-statistics provides a more favorable picture of preliminary testing for normality. Keywords:Assessing normality, fit statistics, g-and-h non-normal skewed and kurtotic data, contaminated mixed-normal distributions; outlying value(s), Likert scales IntroductionPsychological researchers are often reminded that the validity of their statistical tests and the conclusions derived from these tests depends to a great extent on whether the derivational assumptions of the test procedures have been satisfied (e.g., See Keselman, Huberty, Lix et al., 1998; Wilcox, 2012a, b; Wilcox & KESELMAN ET AL 3 Keselman, 2003). Consequently, though not a common practice, researchers are still reminded about assessing derivational assumptions (See Erce...

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Section: Some Parametric Estimatorsmentioning

confidence: 99%

Vol. 12, No. 2 (Full Issue)

2013

J. Mod. App. Stat. Meth.

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“…In other words, given that the kernel function that satisfies (6) is selected, then the performance of the kernel estimator remains almost the same as any other kernel estimator when the kernel function is changed. However, it becomes very well know that the way to select the smoothing parameter h is very sensitive on the performance of the kernel estimator (see for example, Gerard andSchucany, 1999 andEidous, 2005). The popular method that used to select h using line transect data is the reference method.…”

Section: The Optimal Smoothing Parametermentioning

confidence: 99%

Akaike Information Criterion to Select the Parametric Detection Function for Kernel Estimator Using Line Transect Data

Eidous

Al-Salman

2013

J. Mod. App. Stat. Meth.

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Among different candidate parametric detection functions, it is suggested to use Akaike Information Criterion (AIC) to select the most appropriate one of them to fit line transect data. Four different detection functions are considered in this paper. Two of them are taken to satisfy the shoulder condition assumption and the other two estimators do not satisfy this condition. Once the appropriate detection function is determined, it also can be used to select the smoothing parameter of the nonparametric kernel estimator. For a wide range of target densities, a simulation results show the reasonable and good performances of the resulting estimators comparing with some existing estimator, particularly the usual kernel estimator when the half normal model is use as a reference to select the smoothing parameter.

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“…There are some parametric models which were proposed to estimate ( ) (See for example, Gates et al, 1968 andAbabned andEidous, 2012). On the other hand, the nonparametric methods to estimate ( ) have been more attention in the last two decates (See Chen, 1996;Eidous 2005aEidous , 2005bEidous , 2006Eidous , 2009Eidous , 2011aEidous , 2011bEidous , 2012Eidous , 2014Eidous , 2015 and Eidous and Al-Shakhatreh, 2011). However, Nonparametric methods can be used when the perpendicular distances are ungrouped, none of these nonparametric methods can be applied when the data are grouped.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Exponential Model for Grouped Line Transect Data

Eibood¹,

Eidous²

2017

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This paper considers a parametric model for grouped data collected via line transect technique. The weighted exponential model is studied and investigated when the data are assumed to be grouped in the intervals. The maximum likelihood method is adopted for purpose of estimation. The resultant estimator of the population abundance is compared with the corresponding estimator that developed for ungrouped data by using the Laake stakes real data.

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Bias correction for histogram estimator using line transect sampling

Cited by 13 publications

References 14 publications

Vol. 12, No. 2 (Full Issue)

Vol. 12, No. 2 (Full Issue)

Akaike Information Criterion to Select the Parametric Detection Function for Kernel Estimator Using Line Transect Data

A Weighted Exponential Model for Grouped Line Transect Data

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