A new class of probability distributions via cosine and sine functions with applications

In this paper, we introduce a new class of (probability) distributions, based on a cosine-sine transformation, obtained by compounding a baseline distribution with cosine and sine functions. Some of its properties are explored. A special focus is given to a particular cosine-sine transformation usin...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 48; no. 8; pp. 2287 - 2300
Main Authors Chesneau, Christophe, Bakouch, Hassan S., Hussain, Tassaddaq
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 14.09.2019
Taylor & Francis Ltd
Subjects
2000 MSC
Cosine-sine transformation
Economic models
Estimation
Goodness of fit
Goodness-of-fit statistics
Hazard rate function
Maximum likelihood estimation
Parameter estimation
Probability distribution functions
Statistical distributions
Statistical tests
Transformations
Trigonometric functions
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Summary:In this paper, we introduce a new class of (probability) distributions, based on a cosine-sine transformation, obtained by compounding a baseline distribution with cosine and sine functions. Some of its properties are explored. A special focus is given to a particular cosine-sine transformation using the exponential distribution as baseline. Estimations of parameters of a particular cosine-sine exponential distribution are performed via the maximum likelihood estimation method. A simulation study investigates the performances of these estimates. Applications are given for four real data sets, showing a better fit in comparison to some existing distributions based on some goodness-of-fit tests.
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content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2018.1440303