Adaptive estimation for varying coefficient models with nonstationary covariates
نویسندگان
چکیده
منابع مشابه
Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates
The empirical likelihood-based inference for varying coefficient models with missing covariates is investigated. An imputed empirical likelihood ratio function for the coefficient functions is proposed, and it is shown that iis limiting distribution is standard chi-squared. Then the corresponding confidence intervals for the regression coefficients are constructed. Some simulations show that th...
متن کاملFeature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates.
This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency...
متن کاملEstimation in nonstationary random coefficient autoregressive models
We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (φ+ bk)Xk−1 + ek, where (φ,ω 2, σ2) is the parameter of the process, Eb0 = ω2, Ee0 = σ 2. We consider a nonstationary RCA process satisfying E log |φ + b0| ≥ 0 and show that σ2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimat...
متن کاملAdaptive Varying-coefficient Linear Models
Varying-coefficient linear models arise from multivariate nonparametric regression, nonlinear time series modelling and forecasting, functional data analysis, longitudinal data analysis, and others. It has been a common practice to assume that the vary-coefficients are functions of a given variable which is often called an index. A frequently asked question is which variable should be used as t...
متن کاملSemiparametric Estimation of Partially Linear Varying Coefficient Models with Time Trend and Nonstationary Regressors
This paper extends the partially linear varying coefficient model to contain time trend and nonstationary variables as regressors. We use the profile likelihood method to estimate both time trend coefficient in the linear component and the functional coefficients in the nonlinear component and establish their asymptotic distributions. Monte Carlo simulations are shown to investigate the finite ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2018
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2018.1484483