September 11th attacks held the biggest tragedy in American history. It was a day of grief, and it proved that America was not immune to attacks and threat. Afterwards life has changed not only for the American Muslims but also American Christians and Jews and to people from other religions. The cruelty of that day has left its shed particularly on the Muslims’ life in America who in reality had nothing to do with the attacks. Arab American Muslim writer Laila Halaby’s novel, Once in a Promised Land, intensely displays the problems that Arab Muslims went through after September 11th attacks. This paper discusses this issue through analysing Halaby’s novel, where she deals with the issues such as discrimination, stereotype, and prejudice. This paper presents the two main characters of the novel Salma and Jassim and the crisis they encountered after the attacks.
MUSLIM AMERICANS DELIMMA POST 9/11 IN LAILA HALABY’S ONCE IN A PROMISED LAND
: alteration
discrimination
identity loss
Muslim writers
stereotype
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
