کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7358151 | 1478571 | 2018 | 46 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Efficient estimation and computation for the generalised additive models with unknown link function
ترجمه فارسی عنوان
برآورد و محاسبه کارآیی برای مدل های افزایشی تعمیم یافته با عملکرد پیوند ناشناخته
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کلمات کلیدی
مدل افزایشی عمومی، هموار خطی محلی، تقریبا یک احتمال، خواص متضاد، بازده نیمه پارامتریک،
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
چکیده انگلیسی
The generalised additive models (GAM) are widely used in data analysis. In the application of the GAM, the link function involved is usually assumed to be a commonly used one without justification. Motivated by a real data example with binary response where the commonly used link function does not work, we propose a generalised additive models with unknown link function (GAMUL) for various types of data, including binary, continuous and ordinal. The proposed estimators are proved to be consistent and asymptotically normal. Semiparametric efficiency of the estimators is demonstrated in terms of their linear functionals. In addition, an iterative algorithm, where all estimators can be expressed explicitly as a linear function of Y, is proposed to overcome the computational hurdle for the GAM type model. Extensive simulation studies conducted in this paper show the proposed estimation procedure works very well. The proposed GAMUL are finally used to analyze a real dataset about loan repayment in China, which leads to some interesting findings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 202, Issue 2, February 2018, Pages 230-244
Journal: Journal of Econometrics - Volume 202, Issue 2, February 2018, Pages 230-244
نویسندگان
Huazhen Lin, Lixian Pan, Shaogao Lv, Wenyang Zhang,