کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4634582 | 1340695 | 2008 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Monte Carlo EM algorithm in logistic linear models involving non-ignorable missing data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Many data sets obtained from surveys or medical trials often include missing observations. Since ignoring the missing information usually cause bias and inefficiency, an algorithm for estimating parameters is proposed based on the likelihood function of which the missing information is taken account. A binomial response and normal exploratory model for the missing data are assumed. We fit the model using the Monte Carlo EM (Expectation and Maximization) algorithm. The E-step is derived by Metropolis-Hastings algorithm to generate a sample for missing data, and the M-step is done by Newton-Raphson to maximize the likelihood function. Asymptotic variances and the standard errors of the MLE (maximum likelihood estimates) of parameters are derived using the observed Fisher information.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 197, Issue 1, 15 March 2008, Pages 440-450
Journal: Applied Mathematics and Computation - Volume 197, Issue 1, 15 March 2008, Pages 440-450
نویسندگان
Jeong-Soo Park, Guoqi Q. Qian, Yuna Jun,