In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson distribution are addressed whe...

We explore the viable $f(R)$ gravity models in FLRW backgrounds with a free spatial curvature parameter $\Omega_{K}$. In our numerical calculation, we concentrate on exponential model of $f(R) = R - \lambda R_{ch}(1-\exp{(-R/R_{ch}}))$, where $R_{ch}$ is characteristic scale, which independent $\Omega_K$, and $\lambda$ corresponds to parameter, while $R_{ch}\lambda=2\Lambda$ $\Lambda$ cosmologi...

We consider the problem of designing revenue-optimal auctions for selling two items and bidders’ valuations are independent among bidders but negatively correlated among items. Abstractly, this setting can be thought of as an instance with single-dimensional type space but multi-dimensional allocation space. Such setting has been extensively studied in the literature, but all under the assumpti...

We review the information approach to regularization parameter selection and its information complexity extension for the solution of discrete ill posed problems. An information criterion for regularization parameter selection was first proposed by Shibata in the context of ridge regression as an extension of Takeuchi’s information criterion. In the information approach, the regularization para...

Many regression procedures are affected by heteroskedasticity, or non-constant variance. A classic solution is to transform the response y and model h(y) instead. Common functions h require a direct relationship between the variance and the mean. Unless the transformation is known in advance, it can be found by applying a model for the variance to the squared residuals from a regression fit. Un...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

Parametric conditional copula models allow the copula parameters to vary with a set of covariates according to an unknown calibration function. In this paper we develop a flexible Bayesian method to estimate the calibration function of a bivariate conditional copula. We construct a prior distribution over the set of smooth calibration functions using a sparse Gaussian process (GP) prior for the...

ABSTRACT In this work, we investigate the MOdified Gravity (MOG) theory for dynamics of Universe and compare results with Lambda cold dark matter (?CDM) cosmology. We study background cosmological properties MOG model structure formation at linear perturbation level. two models currently available data by using statistical Bayesian analyses. After obtaining updated constraints on free parameter...

NuSTAR observed the black hole candidate XTE J1908+094 during its 2013 and 2019 outbursts. We use relativistic reflection to measure spin of through 19 different assumptions relxill flavors parameter combinations. The most favored model in terms Deviance Information Criterion (DIC) measures be , inclination degrees (1σ statistical errors). look at effects coronal geometry density accretion disk...

To understand the spatial pattern of dengue fever (DF) patients’ survival and investigated factors influencing DF survival. A Bayesian method via a conditional autoregressive approach was used to analyze that influence in 14 sub-districts from January 2015 May 2017 Makassar city, Indonesia. non-spatial model were compared by using deviance information criterion. The more suitable than model. Un...