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A Hamiltonian Monte Carlo EM algorithm for generalized linear mixed models with spatial skew latent variables
Spatial generalized linear mixed models with skew latent variables are usually used to model discrete spatial responses that have some skewness....
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A new autoregressive process driven by explanatory variables and past observations: an application to PM 2.5
This paper uses the empirical likelihood (EL) method for a new random coefficient autoregressive process driven by explanatory variables and past...
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Modified Cumulative Extropies of Doubly Truncated Random Variables
In this paper, we introduce the modified doubly truncated cumulative residual and past extropies, which are generalizations of corresponding...
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A Comparison of Estimation Methods for Shared Gamma Frailty Models
This paper compares six different estimation methods for shared frailty models via a series of simulation studies. A shared frailty model is a...
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Divergence-based tests for the bivariate gamma distribution applied to polarimetric synthetic aperture radar
The use of polarimetric synthetic aperture radar (PolSAR) is one of the most successful tools for solving remote sensing problems. The...
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Generalized log-gamma additive partial linear models with P-spline smoothing
In this paper additive partial linear models with generalized log-gamma errors and P-spline smoothing are proposed for uncensored data. This class...
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Quantile-based control charts for poisson and gamma distributed data
In terms of statistical process control (SPC), the probability distributions of the quality characteristics are critical in detecting changes in the...
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Efficient importance sampling for large sums of independent and identically distributed random variables
We discuss estimating the probability that the sum of nonnegative independent and identically distributed random variables falls below a given...
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On the Baum–Katz theorem for randomly weighted sums of negatively associated random variables with general normalizing sequences and applications in some random design regression models
In this paper, we develop Jajte’s technique, which is used in the proof of strong laws of large numbers, to prove complete convergence for randomly...
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Some Bivariate and Multivariate Models Involving Independent Gamma Distributed Components
Several multivariate models involving independent gamma distributed components (three of which are new) are described. The flexible bivariate beta(2)...
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A Study of Lack-of-Fit Diagnostics for Models Fit to Cross-Classified Binary Variables
In this paper, an extended version of the GFfit statistic is compared to other lack-of-fit diagnostics for models fit to cross-classified binary...
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Parameter estimation for Logistic errors-in-variables regression under case–control studies
The article develops parameter estimation in the Logistic regression when the covariate is observed with measurement error. In Logistic regression...
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An R-Estimator in the Errors in Variables Linear Regression Model
This note develops an R estimator of the regression parameters in the errors in variables linear regression model, when the distributions of the...
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Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU
Bayesian approaches have been utilized to address the challenge of variable selection and statistical inference in high-dimensional survival...
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Stochastic monotonicity of dependent variables given their sum
Given a finite set of independent random variables, assume one can observe their sum, and denote with s its value. Efron in 1965, and Lehmann in...
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Bayesian Survival Analysis with the Extended Generalized Gamma Model: Application to Demographic and Health Survey Data
We extend the existing family of flexible survival models by assembling models scattered across the literature into a more knit form and under the...
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Simultaneous inference for Berkson errors-in-variables regression under fixed design
In various applications of regression analysis, in addition to errors in the dependent observations also errors in the predictor variables play a...
