Wrapped Lindley distribution

Joshi, S. W.; Jose, Kim

doi:10.1080/03610926.2017.1280168

Cited by 22 publications

(15 citation statements)

References 9 publications

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“…This dataset was recently used by Joshi and Jose [7] as an application of the wrapped Lindley () distribution. In order to make a comparison, maximized log likelihood values (L), Akaike information criterion (AIC), Kolmogorov-Smirnov with p values (KS) and Watson's U 2 (W 2 ) statistics values for the TWE, WE and  distributions are given in Table 3.…”

Section: Application To Real Datamentioning

confidence: 99%

A new wrapped exponential distribution

Yilmaz

Biçer

2018

Math Sci

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We introduce a new wrapped exponential distribution named transmuted wrapped exponential (TWE) distribution, for the modeling of circular datasets by using the Transmutation Rank-Map method. This method is employed for the first time for a wrapped distribution with this study. The introduced distribution is more flexible than traditional wrapped exponential distribution. The paper provides the explicit form of important distributional properties of the introduced distribution such as expectation, median, moments, characteristic function, quantile function, hazard rate function and stress-strength reliability. Rényi and Shannon entropies are also obtained. The statistical inference problem for the TWE distribution is investigated using maximum likelihood, least squares and weighted least squares and comparative numerical study results are presented. Furthermore, we present a real dataset analysis.

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Section: Application To Real Datamentioning

confidence: 99%

A new wrapped exponential distribution

Yilmaz

Biçer

2018

Math Sci

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“…A wrapped probability distribution is a continuous probability distribution that describes data points that lie on a unit n-sphere. The cases of wrapped Lindley distribution have been studied extensively by Joshi 52 or parameters induction can be used to handle various real data sets with complex structure. These models will be useful for constructing probability models and may help the development of new classes from the Lindley distribution in future.…”

Section: Wrapped Lindley Distributionmentioning

confidence: 99%

“…Joshi52 showed that wrapped Lindley distribution give good fit to the data data set (orientations of 76 turtles after laying eggs and is given inTable 1(Rao 53 ) than wrapped exponential distribution.…”

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confidence: 97%

A retrospective study on Lindley distribution

Tomy¹

2018

BBIJ

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Generalizations make a distribution more flexible especially for studying the tail properties. There exist many generalized family of continuous univariate distributions. In this article, a survey on Lindley distribution, its extensions and classes is conducted. Several available generalizations of the distribution are reviewed and recent trends in the construction of generalized classes are discussed.

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“…while the wrapped Lindley distribution was introduced by Joshi and Jose (2018) [11]. They defined the PDF and the CDF of the wrapped LD, respectively, as follows:…”

Section: Introductionmentioning

confidence: 99%

On Wrapping of Quasi Lindley Distribution

Al-Khazaleh

Alkhazaleh

2019

Mathematics

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In this paper, as an extension of Wrapping Lindley Distribution (WLD), we suggest a new circular distribution called the Wrapping Quasi Lindley Distribution (WQLD). We obtain the probability density function and derive the formula of a cumulative distribution function, characteristic function, trigonometric moments, and some related parameters for this WQLD. The maximum likelihood estimation method is used for the estimation of parameters.

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