scholarly journals PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2057
Author(s):  
Shuangshuang Li ◽  
Jianbao Chen ◽  
Danqing Chen
Keyword(s):  
Random Effects ◽  
Spatial Data ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Panel Model ◽  
Spatial Autoregressive ◽  
Quasi Maximum Likelihood

This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient spatial autoregressive panel model (PLVCSARPM) with random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the estimators were proved under some regular conditions. Monte Carlo simulations implied our estimators have good finite sample performance. Finally, a set of asymmetric real data applications was analyzed for illustrating the performance of the provided method.

scholarly journals Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinghua Zhang ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Linear Models ◽  
Selection Procedure ◽  
Real Data ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Quadratic Inference Function ◽  
Quadratic Inference Functions

Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.

scholarly journals Adjusted Empirical Likelihood for Varying Coefficient Partially Linear Models with Censored Data

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Peixin Zhao
Keyword(s):  
Censored Data ◽  
Empirical Likelihood ◽  
Linear Models ◽  
Real Data ◽  
Partially Linear Models ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Adjusted Empirical Likelihood ◽  
Chi Squared

By constructing an adjusted auxiliary vector ingeniously, we propose an adjusted empirical likelihood ratio function for the parametric components of varying coefficient partially linear models with censored data. It is shown that its limiting distribution is standard central chi-squared. Then the confidence intervals for the parametric components are constructed. A simulation study and a real data analysis are undertaken to assess the finite sample performance of the proposed method.

scholarly journals Bayesian Analysis of Partially Linear Additive Spatial Autoregressive Models with Free-Knot Splines

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1635
Author(s):  
Zhiyong Chen ◽  
Jianbao Chen
Keyword(s):  
Spatial Data ◽  
Generalized Method Of Moments ◽  
Bayesian Estimator ◽  
Posterior Distributions ◽  
Computationally Efficient ◽  
Partially Linear ◽  
Spatial Autoregressive ◽  
Method Of Moments Estimator ◽  
Spatial Autoregressive Models ◽  
Convergent Algorithm

This article deals with symmetrical data that can be modelled based on Gaussian distribution. We consider a class of partially linear additive spatial autoregressive (PLASAR) models for spatial data. We develop a Bayesian free-knot splines approach to approximate the nonparametric functions. It can be performed to facilitate efficient Markov chain Monte Carlo (MCMC) tools to design a Gibbs sampler to explore the full conditional posterior distributions and analyze the PLASAR models. In order to acquire a rapidly-convergent algorithm, a modified Bayesian free-knot splines approach incorporated with powerful MCMC techniques is employed. The Bayesian estimator (BE) method is more computationally efficient than the generalized method of moments estimator (GMME) and thus capable of handling large scales of spatial data. The performance of the PLASAR model and methodology is illustrated by a simulation, and the model is used to analyze a Sydney real estate dataset.

Testing Impact Measures in Spatial Autoregressive Models

2019 ◽  
Vol 43 (1-2) ◽  
pp. 40-75 ◽  
Author(s):  
Giuseppe Arbia ◽  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Linear Regression ◽  
Spatial Data ◽  
Regression Models ◽  
Delta Method ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Spatial Panel Data Models ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
The Impact

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.

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