دورية أكاديمية

Empirical-Likelihood-Based Inference for Partially Linear Models

التفاصيل البيبلوغرافية
العنوان: Empirical-Likelihood-Based Inference for Partially Linear Models
المؤلفون: Haiyan Su, Linlin Chen
المصدر: Mathematics, Vol 12, Iss 1, p 162 (2024)
بيانات النشر: MDPI AG
سنة النشر: 2024
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: partially linear models, empirical likelihood, confidence interval, Mathematics, QA1-939
الوصف: Partially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a nonparametric empirical likelihood approach. The proposed method involves a projection step to eliminate the nuisance nonparametric component and utilizes an empirical-likelihood-based technique, along with the Bartlett correction, to enhance the coverage probability of the confidence interval for the parameter of interest. This method demonstrates robustness in handling normally and non-normally distributed errors. The proposed empirical likelihood ratio statistic converges to a limiting chi-square distribution under certain regulations. Simulation studies demonstrate that this method provides better inference in terms of coverage probabilities compared to the conventional normal-approximation-based method. The proposed method is illustrated by analyzing the Boston housing data from a real study.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2227-7390
العلاقة: https://www.mdpi.com/2227-7390/12/1/162Test; https://doaj.org/toc/2227-7390Test; https://doaj.org/article/5795f1bfb8f94de888c23da294f4af7cTest
DOI: 10.3390/math12010162
الإتاحة: https://doi.org/10.3390/math12010162Test
https://doaj.org/article/5795f1bfb8f94de888c23da294f4af7cTest
رقم الانضمام: edsbas.3645BA45
قاعدة البيانات: BASE
الوصف
تدمد:22277390
DOI:10.3390/math12010162