scispace - formally typeset
Search or ask a question
Author

Haitham M. Yousof

Bio: Haitham M. Yousof is an academic researcher from Banha University. The author has contributed to research in topics: Order statistic & Weibull distribution. The author has an hindex of 26, co-authored 118 publications receiving 2193 citations.

Papers published on a yearly basis

Papers
More filters
Journal ArticleDOI
TL;DR: The generalized transmuted-G (G-G) family as mentioned in this paper extends the G-G class with explicit expressions for the ordinary and incomplete moments, generating function, Renyi and Shannon entropies, order statistics and probability weighted moments.
Abstract: We introduce a new class of continuous distributions called the generalized transmuted-G family which extends the transmuted-G class. 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, Renyi 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.

126 citations

Journal ArticleDOI
TL;DR: A new class of continuous distributions called the transmuted exponentiated generalized-G family is introduced which extends the exponentiated G class introduced by Cordeiro et al. (2013) and 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.
Abstract: 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, Renyi 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 applications to real dataset.

105 citations

Journal ArticleDOI
TL;DR: In this article, a new four-parameter lifetime model called the Weibull Frechet distribution is defined and studied, which can serve as an alternative model to other lifetime distributions in the existing literature.
Abstract: A new four-parameter lifetime model called the Weibull Frechet distribution is defined and studied. Various of its structural properties including ordinary and incomplete moments, quantile and generating functions, probability weighted moments, Renyi and δ-entropies and order statistics are investigated. The new density function can be expressed as a linear mixture of Frechet densities. The maximum likelihood method is used to estimate the model parameters. The new distribution is applied to two real data sets to prove empirically its flexibility. It can serve as an alternative model to other lifetime distributions in the existing literature for modeling positive real data in many areas.

103 citations

Journal ArticleDOI
TL;DR: The statistical literature contains many new classes of distributions which have been constructed by extending common families of continuous distributions by means of adding one or more shape parameters, and these new families have been used for modeling data in many applied areas such as engineering, economics, biological studies, environmental sciences and many more.
Abstract: The statistical literature contains many new classes of distributions which have been constructed by extending common families of continuous distributions by means of adding one or more shape parameters. The inducted extra parameter(s) to the existing probability distribution have been shown to improve the flexibility and goodness of fits of the distribution against the intuition of model parsimony. Therefore, many methods of adding a parameter to distributions have been proposed by several researchers and these new families have been used for modeling data in many applied areas such as engineering, economics, biological studies, environmental sciences and many more. In fact the modern computing technology has made many of these techniques accessible if the analytical solutions are very complicated. Gupta et al. [18] defined the exponentiated-G (exp-G) class, which consists of raising the cumulative distribution function (cdf) to a positive power parameter and proposed the exponentiated exponential (EE) distribution, defined by the cdf (for x > 0) F(x) = [1− exp(−λx)]θ , where λ ,θ > 0. This equation is simply the θ th power of the standard exponential cumulative distribution. Many Journal of Statistical Theory and Applications, Vol. 16, No. 3 (September 2017) 288–305 ___________________________________________________________________________________________________________

101 citations

Journal ArticleDOI
TL;DR: In this article, the authors introduce a new class of distributions called the Burr XII system of densities with two extra positive parameters, and estimate the model parameters by maximum likelihood, and assess the performance of the estimators in terms of biases and mean squared errors.
Abstract: We introduce a new class of distributions called the Burr XII system of densities with two extra positive parameters. We provide a comprehensive treatment of some of its mathematical properties. We estimate the model parameters by maximum likelihood. We assess the performance of the maximum likelihood estimators in terms of biases and mean squared errors by means of a simulation study. We also introduce a new family of regression models based on this system of densities. The usefulness of the proposed models is illustrated by means of three real data sets.

89 citations


Cited by
More filters
01 Jan 2016
TL;DR: The table of integrals series and products is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.

4,085 citations

Journal Article
TL;DR: Alho and Spencer as discussed by the authors published a book on statistical and mathematical demography, focusing on mature population models, the particular focus of the new author (see, e.g., Caswell 2000).
Abstract: Here are two books on a topic new to Technometrics: statistical and mathematical demography. The first author of Applied Mathematical Demography wrote the first two editions of this book alone. The second edition was published in 1985. Professor Keyfritz noted in the Preface (p. vii) that at age 90 he had no interest in doing another edition; however, the publisher encouraged him to find a coauthor. The result is an additional focus for the book in the world of biology that makes it much more relevant for the sciences. The book is now part of the publisher’s series on Statistics for Biology and Health. Much of it, of course, focuses on the many aspects of human populations. The new material focuses on mature population models, the particular focus of the new author (see, e.g., Caswell 2000). As one might expect from a book that was originally written in the 1970s, it does not include a lot of information on statistical computing. The new book by Alho and Spencer is focused on putting a better emphasis on statistics in the discipline of demography (Preface, p. vii). It is part of the publisher’s Series in Statistics. The authors are both statisticians, so the focus is on statistics as used for demographic problems. The authors are targeting human applications, so their perspective on science does not extend any further than epidemiology. The book actually strikes a good balance between statistical tools and demographic applications. The authors use the first two chapters to teach statisticians about the concepts of demography. The next four chapters are very similar to the statistics content found in introductory books on survival analysis, such as the recent book by Kleinbaum and Klein (2005), reported by Ziegel (2006). The next three chapters are focused on various aspects of forecasting demographic rates. The book concludes with chapters focusing on three areas of applications: errors in census numbers, financial applications, and small-area estimates.

710 citations

Journal ArticleDOI
TL;DR: In this paper, survival distributions for reliability applications in the Biomedical Sciences are discussed, with a focus on the reliability of the distribution of survival distributions in the field of bio-medical applications.
Abstract: (1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

513 citations

Journal ArticleDOI
TL;DR: This book aims to introduce simulation techniques for practitioners in the financial and risk management industry at an intermediate level by having extensive simulation examples using S–PLUS or Visual Basics.
Abstract: (2007). Stochastic Ageing and Dependence for Reliability. Technometrics: Vol. 49, No. 2, pp. 222-222.

314 citations

Journal ArticleDOI
TL;DR: In this article, Statistical Methods for Survival Data Analysis (SVMDA) is used to analyze survival data in the context of statistical methods for survival data analysis (SDFA).
Abstract: (1993). Statistical Methods for Survival Data Analysis. Technometrics: Vol. 35, No. 1, pp. 101-101.

272 citations