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Journal Cover International Journal of Advanced Statistics and Probability
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  This is an Open Access Journal Open Access journal
   ISSN (Print) 2307-9045 - ISSN (Online) 2307-9045
   Published by Science Publishing Corporation Homepage  [12 journals]
  • Prediction of blood lead level in maternal and fetal us-ing generalized
           linear model

    • Authors: Zakariya Algamal, Haithem Ali
      Pages: 65 - 69
      Abstract: Over the past decades, with advanced data collection techniques, a different type of data continues to appear in various biological, sciences, medical, social, and economical studies. Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. Generalized linear models (GLMs) are generalization of the linear regression models, which allow fitting regression models to response variable that is non normal and follows a general exponential family. The aim of this study is to encourage and initiate the application of GLMs to predict the maternal and fetal blood-lead level. The inverse Gaussian distribution with inverse quadratic link function is considered. Four main effects were significant in the prediction of the maternal blood-lead level (pica, smoking of mother, dairy products intake of mother, calcium intake of mother), while in the prediction of the fetal blood-lead level, two main effects showed significance (dairy products intake of mother and hemoglobin of mother).
      PubDate: 2017-06-04
      DOI: 10.14419/ijasp.v5i2.7615
      Issue No: Vol. 5, No. 2 (2017)
  • A modified class of exponential-type estimator of population-mean in
           simple random sampling

    • Authors: Ekaette Enang, Joy Uket, Emmanuel Ekpenyong
      Pages: 70 - 76
      Abstract: The problem of obtaining better ratio estimators of the population means are dominating in survey sampling. This paper provides a modified class of exponential type estimators using combinations of some existing estimators. Expressions for the bias and Mean Square Error (MSE) with the optimality conditions for this class of estimators have been established. Both analytical and numerical comparison with some existing estimators shows better performances from members of the proposed class.
      PubDate: 2017-06-27
      DOI: 10.14419/ijasp.v5i2.7345
      Issue No: Vol. 5, No. 2 (2017)
  • Equiradial designs under changing axial distances, design sizes and
           varying center runs with their relationships to the central composite

    • Authors: Mary Iwundu, Henry Onu
      Pages: 77 - 82
      Abstract: In assessing the preferences of equiradial designs based on design size, axial distance and number of center points in relation to the central composite designs, D-absolute deviation (D-AD) and G-absolute deviation (G-AD) are proposed as new design measures of closeness of experimental designs. Each absolute deviation is positive or zero. The G-absolute deviation is zero or approximately zero at  equals 1 center point. For  greater than 1, G-absolute deviation decreases for increasing values of . On the other hand, the D-absolute deviation decreases as the design size increases. Designs having G-AD values of zero or approximately zero are identical or near identical in G-optimality properties. Also, designs having D-AD values of zero or approximately zero are identical or near identical in D-optimality properties. It is conjecturally hoped that at some increased design size, the equiradial designs and the central composite designs, having same axial or radial distance will coincide (be identical) in their properties, with D-AD value of zero or approximately zero.
      PubDate: 2017-07-13
      Issue No: Vol. 5, No. 2 (2017)
  • The beta-burr type v distribution: its properties and application to real
           life data

    • Authors: Hussaini Garba Dikko, Yakubu Aliyu, Saidu Alfa
      Pages: 83 - 86
      Abstract: A new distribution called the beta-Burr type V distribution that extends the Burr type V distribution was defined, investigated and estab-lished. The properties examined provide a comprehensive mathematical treatment of the distribution. Additionally, various structural proper-ties of the new distribution verified include probability density function verification, asymptotic behavior, Hazard Rate Function and the cumulative distribution. Subsequently, we used the maximum likelihood estimation procedure to estimate the parameters of the new distribu-tion. Application of real data set indicates that this new distribution would serve as a good alternative distribution function to model real- life data in many areas.
      PubDate: 2017-07-15
      Issue No: Vol. 5, No. 2 (2017)
  • The exact extreme value distribution – applied study

    • Authors: Aisha Fayomi, Neamat Qutb, Ohoud Al-Beladi
      Pages: 87 - 90
      Abstract: Extreme value theory is used to develop models for describing the distribution of extreme events. Exact extreme value or compound distri-bution which is based on the theory of the maximum of random variables of random numbers is one of the most important models that are applicable in various situations, for instance of interest, it uses partial duration series (PDF) data to analyze extreme hydrological. As part of our earlier study, the parameters of this model were estimated by two methods, maximum likelihood (ML) and Bayesian- based on non-informative and informative priors. Moreover, a comparative study using simulated data showed that the Bayesian based on informative prior is the best estimation method. In this paper, a real data set taken from records of the largest daily rainfall data of Jeddah city in Saudi Arabia is used to fit the model when the parameters are estimated by Bayesian method. A comparative applied study indicates that the exact extreme value model under Bayesian estimates (BE) of its parameters provides appropriate fit for this data set and it is more applicable than the same model when the parameters are estimated by ML method and other three classical extreme value models.
      PubDate: 2017-07-26
      Issue No: Vol. 5, No. 2 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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