Haitham Yousof | Faculty of commerce (original) (raw)

Papers by Haitham Yousof

We introduce a new class of continuous distributions called the complementary generalized transmu... more We introduce a new class of continuous distributions called the complementary generalized transmuted Poisson-G family, which extends the transmuted class pioneered by Shaw and Buckley (2007). We provide some special models and derive general mathematical properties including quantile function, explicit expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies and order statistics. The estimation of the model parameters is performed by maximum likelihood. The flexibility of the new family is illustrated by means of two applications to real data sets.

We introduce a new class of continuous distributions called the Kumaraswamy transmuted-G family w... more We introduce a new class of continuous distributions called the Kumaraswamy transmuted-G family which extends the transmuted class deÖned by Shaw and Buckley (2007). Some special models of the new family are provided. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Renyi and Shannon entropies, order statistics and probability weighted moments are derived. The maximum likelihood is used for estimating the model parameters. The flexibility of the generated family is illustrated by means of two applications to real data sets.

This paper introduces a new lifetime model which is a generalization of the transmuted exponentia... more This paper introduces a new lifetime model which is a generalization of the transmuted exponentiated additive Weibull distribution by using the Kumaraswamy generalized (Kw-G) distribution. With the particular case no less than seventy nine sub models as special cases, the so-called Kumaraswamy transmuted exponentiated additive Weibull distribution, introduced by Cordeiro and de Castro (2011) is one of this particular cases. Further, expressions for several probabilistic measures are provided, such as probability density function, hazard function, moments, quantile function, mean, variance and median, moment generation function, Rényi and q entropies, order estatistics, etc. Inference is maximum likelihood based and the usefulness of the model is showed by using a real dataset.

We introduce a new family of continuous distributions called the beta transmuted-H family which e... more We introduce a new family of continuous distributions called the beta transmuted-H family which extends the transmuted family pioneered by Shaw and Buckley [34]. Some of its mathematical properties including explicit expressions for the ordinary moments, quantiles, generating
functions and order statistics are derived. Some special models of the new family are provided. The maximum likelihood method is used for estimating the model parameters, and the finite sample performance of the estimators is assessed by simulation. The importance and flexibility of the proposed family are illustrated by applications to two real data sets.

We introduce a new class of continuous distributions called the transmuted exponentiated generali... more We introduce a new class of continuous distributions called the transmuted exponentiated generalized-G family which extends the exponentiated generalized-G class introduced by Cordeiro et al. (2013). We provide some special models for the new family. Some of its mathematical properties including explicit
expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies,
order statistics and probability weighted moments are derived. The estimation of the model parameters is performed by maximum likelihood. The flexibility of the proposed family is illustrated by means of an application to a real dataset.

A new five parameter model is proposed and stutied. The new distribution generalizes the Weibull ... more A new five parameter model is proposed and stutied. The new distribution generalizes the Weibull Lomax distribution introduced by Tahir et al. (2015) and is referred to as transmuted Weibull Lomax (TWL) distribution. Various structural properties of the new model including ordinary and incomplete moments, quantiles, generating function, probability weighted moments, Rényi and q-entropies and order statistics are derived. We proposed the method of maximum likelihood for estimating the model parameters. The usefulness of the new model is illustrated through an application to a real data set.

In this article we propose a Bayesian regression model called the Bayesian generalized partial li... more In this article we propose a Bayesian regression model called the Bayesian generalized partial linear model which extends the generalized partial linear model. We consider Bayesian estimation and inference of parameters for the generalized partial linear model (GPLM) using some multivariate conjugate prior distributions under the square error loss function. We propose an algorithm for estimating the GPLM parameters using Bayesian theorem in more detail. Finally, comparisons are made between the GPLM estimators using Bayesian approach and the classical approach via a simulation study.

We introduce a new class of continuous distributions called the generalized transmuted-G family w... more We introduce a new class of continuous distributions called the generalized transmuted-G family which extends the quadratic rank trans-
mutation map pioneered by Shaw and Buckley (2007). We provide six
special models of the new family. Some of its mathematical properties
including explicit expressions for the ordinary and incomplete moments,
generating function, Rényi and Shannon entropies, order statistics and
probability weighted moments are derived. The estimation of the model
parameters is performed by maximum likelihood. The flexibility of the
proposed family is illustrated by means of three applications to real data
sets.

This dissertation provides a new bayesian estimation and inference of the unknown parameters... more This dissertation provides a new bayesian estimation and inference of the unknown parameters for the generalized partial linear model (GPLM) and the semi-parametric logistic regression model (SLRM) using some multivariate prior distributions under the square error loss function.

Pakistan Journal of Statistics and Operation Research, 2015