This research is an attempt to study aspects of syntactic deviation in AbdulWahhab Al-Bayyati with reference to English. It reviews this phenomenon from an extra-linguistic viewpoint. It adopts a functional approach depending on the stipulates of systemic Functional Grammar as developed by M.A.K. Halliday and others adopting this approach. Within related perspective, fairly’s taxonomy (1975) has been chosen to analyze the types of syntactic deviation because it has been found suitable and relevant to describe this phenomenon. The research hypothesizes that syntactic deviation is pervasive in Arabic poetry, in general and in Abdul-Wahhab Al-Bayyati Poetry in specific, and can be analyzed in the light of systemic Functional Grammar. It also hypothesize that syntactic deviation intensifies cohesion in poetic texts and does not disrupt or dismantle links between the structures of the clause. The devices tackled in this research are: ellipsis, repetition, functional shift, and thematization
Syntactic Deviation in Abdul-Wahhab Al-Bayyati Poetry
With Reference to English Syntax A Functional Study
ellipsis
repetition
functional shift
and thematization.
Publication Date
Tue Dec 31 2019
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Different Estimators for the shape Parameter and the Reliability function of Kumaraswamy Distribution
Kumaraswamy distribution
the shape parameter
reliability function
maximum likelihood
Bayes method
prior distributions (exponential distribution
chi-squared distribution and Erlang-2 type distribution)
the squared error loss function.
توزيع Kumaraswamy
معلمة الشكل
دالة المعولية
الإمكان الاعظم
طريقة بيز
التوزيعات الأولية (التوزيع الاسي
توزيع مربع كاي
توزيع ارلنك)
دالة الخسارة التربيعية.
In this paper, we used maximum likelihood method and the Bayesian method to estimate the shape parameter (θ), and reliability function (R(t)) of the Kumaraswamy distribution with two parameters l , θ (under assuming the exponential distribution, Chi-squared distribution and Erlang-2 type distribution as prior distributions), in addition to that we used method of moments for estimating the parameters of the prior distributions. Bayes
