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Showing 3 results for Gibbs Sampling
Sakineh Sadeghi, Iraj Kazemi,
Volume 3, Issue 1 (9-2009)
Abstract
Recently, dynamic panel data models are comprehensively used in social and economic studies. In fitting these models, a lagged response is incorrectly considered as an explanatory variable. This ad-hoc assumption produces unreliable results when using conventional estimation approaches. A principle issue in the analysis of panel data is to take into account the variability of experimental individual effects. These effects are usually assumed fixed in many studies, because of computational complexity. In this paper, we assume random individual effects to handle such variability and then compare the results with fixed effects. Furthermore, we obtain the model parameter estimates by implementing the maximum likelihood and Gibbs sampling methods. We also fit these models on a data set which contains assets and liabilities of banks in Iran.
Ali Aghamohammadi, Sakineh Mohammadi,
Volume 9, Issue 2 (2-2016)
Abstract
In many medical studies, in order to describe the course of illness and treatment effects, longitudinal studies are used. In longitudinal studies, responses are measured frequently over time, but sometimes these responses are discrete and with two-state. Recently Binary quantile regression methods to analyze this kind of data have been taken into consideration. In this paper, quantile regression model with Lasso and adaptive Lasso penalty for longitudinal data with dichotomous responses is provided. Since in both methods posteriori distributions of the parameters are not in explicit form, thus the full conditional posteriori distributions of parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed methods with the conventional methods, a simulation study was conducted and at the end, applications to a real data set are illustrated.
Hosein Bahrami Cheshme Ali, Arash Ardalan,
Volume 12, Issue 1 (9-2018)
Abstract
The nonparametric and semiparametric regression models have been improved extensively in the field of cross-sectional study and independent data, but their improvement in the field of longitudinal data is restricted to the recent years or decade. Since the common methods for correlated data have a much lower ability rather than for the independent data, we should use the models which consider the correlation among the data. The mixed and marginal models consider the correlation factor among the data, and so obtain a better fit for that. Furthermore, the semiparametric regression has more flexibility compared to the parametric and nonparametric regression. Consequently, based on the properties of the longitudinal data, the marginal longitudinal semiparametric regression with the penalized spline estimations, is a suitable choice for the analysis of the longitudinal data. In this article, the semiparametric regression with different coefficients which specifies the relationship between a response variable and an explanatory variable based on another explanatory variable is assessed. In addition, Bayesian inference on the nonparametric model for a simulated data and the marginal longitudinal semiparametric model for a real data have been done by standard software; and the results have good performance.
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