Bivariate inverse lomax distribution

In this article, we estimate the parameters of the bivariate Inverse Lomax distribution of Marshall-Olkin based on right censored sample. Utilizing EM algorithm is a priority because the vector of the observed data is not complete but viewed as an observable function of complete data. After that, the EM algorithm makes use of the simplicity of maximum likelihood estimation for complete data. In addition, normal deviations of the estimates of bivariate Lomax distribution are derived. A comparison is conducted via a simulation study between estimates obtained by using the EM algorithm and without the EM algorithm.

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Biometrics & Biostatistics International Journal ( biometrics@medcraveonline.org )

Biometrics & Biostatistics International Journal (BBIJ) is an internationally peer-reviewed open access journal with a strong motto to promote information regarding the development of mathematical and statistical methods including their applications in various fields like biological sciences, clinical and public health research. This journal covers a wide range of experimental modeling and analysis of data observed & generated in various fields including agriculture, animal studies, biological experiments, biomedical engineering, clinical trials, climate change, environmental studies, epidemiological studies, human genetics, genomics, patient safety evaluation studies and several other research areas. The innate theme of the journal is to spread the advanced research technologies in biometrics and biostatistics. All manuscripts published in this journal are subjected to rigorous peer review. The journal delightfully welcomes research papers, review articles, short communications, mini- reviews, opinions, letter to editors etc.,


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