TY - JOUR

T1 - Empirical likelihood-based inference in a partially linear model for longitudinal data

AU - Xue, Liugen

AU - ZHU, Lixing

N1 - Funding Information:
Received September 13, 2005; accepted July 28, 2006 DOI: 10.1007/s11425-008-0020-4 † Corresponding author The first author was supported by the National Natural Science Foundation of China (Grant No. 10571008), the Natural Science Foundation of Beijing (Grant No. 1072004) and the Science and Technology Development Project of Education Committee of Beijing City (Grant No. KM200510005009). The second author was supported by a grant of the Research Grant Council of Hong Kong (Grant No. HKBU7060/04P)

PY - 2008/1

Y1 - 2008/1

N2 - A partially linear model with longitudinal data is considered, empirical likelihood to inference for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the parameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.

AB - A partially linear model with longitudinal data is considered, empirical likelihood to inference for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the parameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.

KW - Confidence region

KW - Empirical likelihood

KW - Longitudinal data

KW - Partially linear model

UR - http://www.scopus.com/inward/record.url?scp=37649010853&partnerID=8YFLogxK

U2 - 10.1007/s11425-008-0020-4

DO - 10.1007/s11425-008-0020-4

M3 - Article

AN - SCOPUS:37649010853

VL - 51

SP - 115

EP - 130

JO - Science in China, Series A: Mathematics, Physics, Astronomy

JF - Science in China, Series A: Mathematics, Physics, Astronomy

SN - 1006-9283

IS - 1

ER -