44 citations · 71 across the 16 of their papers we have counts for
16 papers
Stochastic analysis of finite-temperature effects on cosmological parameters by artificial neural networks
Armin Hatefi, Ehsan Hatefi, I. Y. Park
We explore the impact of finite-temperature quantum gravity effects on cosmological parameters, particularly the effective vacuum-energy sector, by incorporating temperature-depend…
Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory
Armin Hatefi, Ehsan Hatefi, Roberto J. Lopez-Sastre
It is well-known that the critical gravitational collapse produces continuous self-similar solutions characterized by the Choptuik critical exponent, . We examine the solutions…
Liu-type Shrinkage Estimators for Mixture of Poisson Regressions with Experts: A Heart Disease Study
Elsayed Ghanem, Moein Yoosefi, Armin Hatefi
Count data play a critical role in medical research, such as heart disease. The Poisson regression model is a common technique for evaluating the impact of a set of covariates on t…
Sequential Monte Carlo with Cross-validated Neural Networks for Complexity of Hyperbolic Black Hole Solutions in 4D
Armin Hatefi, Ehsan Hatefi
This paper investigates the self-similar solutions of the Einstein-axion-dilaton configuration from type IIB string theory and the global SL(2,R) symmetry. We consider the Continuo…
Modeling the complexity of Elliptic Black Hole Solution In 4D Using Hamiltonian Monte Carlo with Stacked Neural Networks
Armin Hatefi, Ehsan Hatefi, Roberto J. López-Sastre
In this paper, we study the black hole solution of self-similar gravitational collapse in the Einstein-axion-dilaton system for the elliptic class in four dimensions. The solution…
Bayesian Mixture Modelling with Ranked Set Samples
Amirhossein Alvandi, Sedigheh Omidvar, Armin Hatefi +3
We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effe…
Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks
Ehsan Hatefi, Armin Hatefi, Roberto J. López-Sastre
In this paper, we introduce a numerical method based on Artificial Neural Networks (ANNs) for the analysis of black hole solutions to the Einstein-axion-dilaton system in a high di…
Unsupervised Liu-type Shrinkage Estimators for Mixture of Regression Models
Elsayed Ghanem, Armin Hatefi, Hamid Usefi
In many applications (e.g., medical studies), the population of interest (e.g., disease status) comprises heterogeneous subpopulations. The mixture of probabilistic regression mode…
Liu-type Shrinkage Estimators for Mixture of Logistic Regressions: An Osteoporosis Study
Elsayed Ghanem, Armin Hatefi, Hamid Usefi
The logistic regression model is one of the most powerful statistical methods for the analysis of binary data. The logistic regression allows to use a set of covariates to explain…
Nonlinear Statistical Spline Smoothers for Critical Spherical Black Hole Solutions in 4-dimension
Ehsan Hatefi, Armin Hatefi
This paper focuses on self-similar gravitational collapse solutions of the Einstein--axion-dilaton configuration for two conjugacy classes of SL(2, R) transformations. These soluti…
Multiple Observers Ranked Set Samples for Shrinkage Estimators
Andrew David Pearce, Armin Hatefi
Ranked set sampling (RSS) is used as a powerful data collection technique for situations where measuring the study variable requires a costly and/or tedious process while the sampl…
Estimation of Critical Collapse Solutions to Black Holes with Nonlinear Statistical Models
Ehsan Hatefi, Armin Hatefi
The self-similar gravitational collapse solutions to the Einstein-axion-dilaton system have already been found out. Those solutions become invariants after combining the spacetime…
Analysis of Ordinal Populations from Judgment Post-Stratification
Amirhossein Alvandi, Armin Hatefi
In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; However, we often have…
Statistical Inference, Learning and Models in Big Data
Beate Franke, Jean-François Plante, Ribana Roscher +11
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essenti…
Information content of partially rank-ordered set samples
Armin Hatefi, Mohammad Jafari Jozani
Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having m…
Proportion estimation based on a partially rank ordered set sample with multiple concomitants in a breast cancer study
Armin Hatefi, Mohammad Jafari Jozani
In this paper, we use partially rank-ordered set (PROS) sampling design with multiple concomitants in a breast cancer study and propose a method to estimate the proportion of patie…