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RISS 인기검색어
- 검색키워드
- 저자 : Sanying Feng (검색결과 4 건)
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Test for the covariance matrix in time-varying coefficients panel data models with fixed effects
Yuping Hu,Sanying Feng,Jing Zhao 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.2
This paper proposes tests for the null of sphericity and identity matrix for nonparametric time-varying coefficient panel data models with fixed effects. Firstly, based on the local linear smoothing technique, the estimators of the unknown coefficient functions and model residuals are obtained. Secondly, proper test statistics are proposed aiming at tests for sphericity or identity matrix with a large number of cross sectional units and time series observations. In addition, the limiting distributions of the proposed test statistics are derived based on random matrix theory. At last, some simulation studies are conducted to examine the finite sample performance for the proposed test statistics and a real data example is analyzed.
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Revisiting feature selection for linear models with FDR and power guarantees
Yuan Panxu,Feng Sanying,Li Gaorong 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.4
The problem of feature selection for linear models is re-examined by using the fixed-X knockoff procedure and incorporating the selection probability as variable importance scores. Unlike previous work that predominantly focused on false discovery rate (FDR) control, this paper aims to establish theoretical power guarantees for the fixed-X knockoff procedure in linear models. An intersection selection procedure is proposed to make better use of sample data for estimating the selection probability, which in practice results in increasing the selection power. In addition, a two-stage procedure by using the data splitting technique is further developed to explore related theoretical results under high dimensionality. The performance of the proposal over its main competitors is demonstrated through comprehensive simulation studies and real data analysis.
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Estimation in partially linear time-varying coefficients panel data models with fixed effects
Jing Zhao,Sanying Feng,Weihu Cheng 한국통계학회 2017 Journal of the Korean Statistical Society Vol.46 No.2
A partially time-varying coefficient time series panel data model with fixed effects is considered to characterize the nonlinearity and trending phenomenon in panel data model. To estimate the linear regression coefficient and the time-varying coefficient function, two methods are applied with the help of profile least squares. The first one is taking cross-sectional average to eliminate the fixed effects. The second one is local linear dummy variable approach. In each method we derive consistent estimates for both the parametric component and non-parametric trend function. The asymptotic distributions of the estimates are established when T and N tend to infinity simultaneously, where N is the cross section size, T is the time series length. The asymptotic results reveal that the parametric component (non-parametric coefficient function) estimate based on crosssectional average has a rate of convergence T)^−1/2 ((Th)^−1/2) that is slower than that based on local linear dummy variable approach, which is (NT))^−1/2((NTh)^−1/2), where h is the bandwidth. Furthermore, block bootstrap method is used to construct confidence interval for parametric and nonparametric components, respectively. At last, some simulation studies are conducted to examine the finite sample performance for the proposed methods and a real data example is analyzed.
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Testing for covariance matrices in time-varying coefficient panel datamodels with fixed effects
Ranran Chen,Gaorong Li,Sanying Feng 한국통계학회 2020 Journal of the Korean Statistical Society Vol.49 No.1
In this paper, we study the tests for sphericity and identity of covariance matrices in time-varying coefficient high-dimensional panel data models with fixed effects. In order to construct the effective test statistics and avoid the influence of the unknown fixed effects, we apply the difference method to eliminate the dependence of the residual sample, and further construct test statistics using the trace estimators of the covariance matrices. For the estimators of the coefficient functions, we use the local linear dummy variable method. Under some regularity conditions, we study the asymptotic property of the estimators and establish the asymptotic distributions of our proposed test statistics without specifying an explicit relationship between the cross-sectional and the time series dimensions.We further show that the test statistics are asymptotic distribution-free. Subsequently simulation studies are carried out to evaluate our proposed methods. In order to assess the performance of our proposed test method, we compare with the existing test methods in panel data linear models with fixed effects.
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