[PDF] Top 20 Semiparametric Bayesian inference in smooth coefficient models
Has 10000 "Semiparametric Bayesian inference in smooth coefficient models" found on our website. Below are the top 20 most common "Semiparametric Bayesian inference in smooth coefficient models".
Semiparametric Bayesian inference in smooth coefficient models
... the smooth coefficient and parametric models when spousal income is set at $120,000 (approximately the 95th percentile of the spousal income distribution), and thus investigate the impact of ability ... See full document
33
Semiparametric Likelihood Ratio Inference
... tistical models, sometimes called nonparametric maximum likelihood estima- tors (NPMLE) or semiparametric maximum likelihood ...of smooth parameters of the ...for semiparametric models. ... See full document
39
Estimation of productivity in Korean electric power plants : a semiparametric smooth coefficient model
... and statistical significance. The results from simple and varying coefficient semiparametric models represent neutral and non-neutral shifts in the production function, respectively. The rate of ... See full document
33
Improved Simultaneous Estimation of Location and System Reliability via Shrinkage Ideas
... (LT) models are a broad class of regression models which take the PH, PO, and PB models as special ...cases. Semiparametric LT models presume that an unknown non-decreasing ... See full document
112
Bayesian Inference in Nonparanormal Graphical Models.
... for semiparametric copula estimation (Hoff, 2007) and for ROC curve estimation (Gu & Ghosal, 2009; Gu, Ghosal & Kleiner, ...graphical models are generally known as Gaussian copula graphical ... See full document
107
Bayesian analysis of random coefficient autoregressive models
... the Bayesian approach to analyze RCAR models. Bayesian data analysis is becoming more and more appealing because of its flexi- bility in handling complex models that typically involve many ... See full document
35
Semiparametric approaches to inference in joint models for longitudinal and time-to-event data
... ideal and SNP estimators, standard errors track the Monte Carlo standard deviations regardless of the distribution of the random effects, and coverage probabilities are close to the nominal level. The conditional score ... See full document
97
On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models*
... of Bayesian nonparametric statistics started slowly five decades ...nonparametric) models of increasing ...for Bayesian methods, the necessity to analyse priors on infinite or at least high ... See full document
13
Frequentist and Bayesian Analysis of Random Coefficient Autoregressive models
... (RCA) models are obtained by introducing random coefficients to an AR or more generally ARMA ...These models have second order properties similar to that of ARCH and GARCH ...and Bayesian approaches to ... See full document
146
Semiparametric Estimation and Inference for Censored Regression Models.
... The di ffi culty in variance estimation for censored quantile regression arises partly from the unsmoothness of the corresponding estimating function that involves indicator functions. The unsmoothness issue can be ... See full document
86
Efficient Bayesian inference for COM-Poisson regression models
... a Bayesian setting, using a data augmentation technique which requires sampling from the COM-Poisson distribution, and present an exact MCMC algorithm for the COM-Poisson regression ...the Bayesian ... See full document
15
BNSP: an R Package for fitting Bayesian semiparametric regression models and variable selection
... semi-parametric models, summarizing model fits, visualizing covariate effects and predicting new responses or their ...including models and priors specified, marginal posterior probabilities of term ... See full document
24
Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy
... The assumptions of heteroscedastic, correlated, and non- Gaussian residuals are accounted for using the method of Schoups and Vrugt (2010) in the following procedure: (i) the correlation is removed from the residuals ... See full document
24
Robust Inference for Time Varying Coefficient Models with Longitudinal Data
... Time-varying coefficient models are useful in longitudinal data ...the coefficient functions, based on the least squares ...the coefficient functions and develop a robustified generalized ... See full document
12
Essays on Bayesian semiparametric ordinal response models
... The coefficient of the lagged creditworthiness is still positive and signifi- cant in all versions of model 3; 0.132, 0.122, 0.133 and 0.203 in models 3a, 3b1, 3b2 and 3c respectively. Treating the initial ... See full document
163
Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility
... first semiparametric extension has already been applied in the context of standard stochastic volatility models (Jensen and Maheu, 2010; Delatola and Griffin, ...second semiparametric extension is ... See full document
30
Semiparametric Bayesian inference in multiple equation models
... In both the schooling and wage equations, we include the respondent’s Armed Forces Qualifying Test (AFQT) score which is standardized by age (denoted ABILITY), highest grade completed by the respondent’s mother (MOMED) ... See full document
29
Semiparametric smooth coefficient estimation of a production system
... Figure 1 illustrates Table 2 via kernel density plots. We can see that the distributions of the input elasticity estimates from the SPSC SUR model lie on the right-hand-side of the zero vertical line, and the dispersions ... See full document
27
Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models
... autoregressive semiparametric model with no relevant regressors as well as multiple partially linear ...autoregressive models in the case when all explanatory covariates are irrelevant in predicting the ... See full document
46
Semiparametric Bayesian inference in multiple equation models
... posterior inference. Thus, for the subjective Bayesian, prior information can be used to surmount the problem of insufficient ...empirical Bayesian methods could be used to estimate η from the ... See full document
28
Related subjects