Semiparametric model
Shrinkage Estimation of Semiparametric Model with Missing Responses for Cluster Data
... 1, model (1) reduces to semiparametric partially linear varying-coefficient ...0, model (1) becomes varying-coefficient model which has been widely studied by many authors such as Fan and ...
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Difference based M estimator of generalized semiparametric model with NSD errors
... the semiparametric partially linear ...in semiparametric re- gression ...linear model with correlated er- rors, but this paper just focuses on estimating the linear ...generalized ...
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Asymptotic properties of wavelet estimators in heteroscedastic semiparametric model based on negatively associated innovations
... In this paper, we aim to derive the least squares estimators, weighted least squares esti- mators of β, and their strong consistency for the wavelet estimators of f and g. At the same time, Berry–Esséen-type bounds of ...
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A Semiparametric Model for Bayesian Reader Identification
... flexible semiparametric models of eye movements during reading in which den- sities are inferred under a Gaussian process prior centered at a parametric distribution fam- ily that is expected to approximate the ...
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Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
... two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto- regressive ...a ...
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sim.7351.pdf
... to model event count data over the study ...joint semiparametric model for event count data and informative dropout time that allows for correlation through a Gamma ...the semiparametric ...
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Estimation and model specification testing in nonparametric and semiparametric econometric models
... regression model; Shively, Kohn and Ansley (1994) consider testing for linearity in a semiparametric regres- sion model based on the stochastic interpretation of spline smoothing given in Wahba ...
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Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models
... flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contex- tual variables to allow for potential ...
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Estimation in semiparametric models with missing data
... other semiparametric model with missing ...study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing ...
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Nonparametric and Semiparametric Models - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... and model the underlying ...The semiparametric model- ing technique compromises the two aims, flexibility and simplicity of statis- tical procedures, by introducing partial parametric ...to ...
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Topics in Longitudinal Studies with Coarsened Data
... a semiparametric model, the nuisance tangent space is the mean square closure of nui- sance tangent spaces of all parametric submodels, ...the semiparametric model and contain the ...
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Nonlinear time series: semiparametric and nonparametric methods
... various semiparametric methods and mod- els have been proposed and studied extensively in the economics and statistics ...on semiparametric modelling of either independent or depen- dent time series ...that ...
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A semiparametric spatial dynamic model
... a semiparametric spatial dynamic model, which extends the ordinary spatial au- toregressive models to accommodate the effects of some covariates associated with the house ...
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Semiparametric Efficient Adaptive Estimation of the PTTGARCH model
... a semiparametric efficient adaptive estimator of a conditional heteroskedasticity and asymmetric volatility GARCH-type model ...the semiparametric estimator is adaptive for parameters in- cluded in ...
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A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients
... First, semiparametric bivariate probit model allows for flexible functional dependence of the response variables on continuous covariates via the use of penalized regression ...flexibly model the ...
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Quantifying Regional-Scale Water Storage Using Models and Observations: Application For Drought Assessment In South Carolina
... above semiparametric estimating functions are step functions of the regression parameters with potentially multiple roots, and the corresponding estimators may not be well ...
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Association Study, Risk Assessment, and Prediction of Children's Growth Trajectories through Methylation Profiles using Functional Mixed Models.
... the model for predictive ...our model is nonlinear and combines standard functional methods with Gaussian process ...prediction model that is better suited to predicting the full growth curve of a ...
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Semiparametric estimation of a panel data proportional hazards model with fixed effects
... variance as compared to the uncensored counterpart. Therefore, it may be an interesting problem to develop a more efficient estimator than one proposed here. Furthermore, it may also be interesting to find the ...
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Spatial Modeling of Positive Definite Matrices and Its Applications to Diffusion Tensor Imaging.
... Incorporating spatial dependence is important for achieving efficient and valid inference in imaging data analysis (Spence et al. 2007; Wu et al. 2013; Xue et al. 2018). Recently, Lan et al. (2019) also reveal that ...
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Principal Longevity, Leadership Behaviors, and Student Academic Achievement
... PH model due to its good properties and straight- forward interpretation based on hazard ratios in the interval censored ...PH model when the data is interval cen- ...PH model with interval censored ...
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