In this paper, some new statistical methods are proposed, for making inferences about the parameter indexing a Cox proportional hazards marginal structural model for point exposure. Under the key assumption that unmeasured confounding is absent, we propose a new class of closed-form estimators that are doubly robust in the sense that they remain consistent and asymptotically normal for the e¤ec...

Erhardt and Czado (2008) suggest an approximative method for sampling highdimensional count random variables with a specified Pearson correlation. They utilize Gaussian copulae for the construction of multivariate discrete distributions. A major task is to determine the appropriate copula parameters for the achievement of a specified target correlation. Erhardt and Czado (2008) develop an optim...

Although detecting communities in networks has attracted considerable recent attention, estimating the number of is still an open problem. In this paper, we propose a model, which replicates generative process mixed-membership stochastic block model (MMSB) within generic allocation framework Bayesian (BAM) and BAM-MMSB. contrast to traditional blockmodels, BAM-MMSB considers observations as Poi...

Using data for the period 2000 to 2019, aim of this paper is profile and compare research publications in regions continents. The empirical evidence based on Poisson regression model, quantile counts panel negative binomial regression. findings can be summarised as follows: continuous linear increasing trends production knowledge are noted developing specifically Africa even if contribution con...

A fishing model of Starr, Wardrop, and Woodroofe is related to the sequential search model of Cozzolino. The latter is generalized to allow an arbitrary joint distribution of capture times and fish sizes. Implications to the foraging models of Oaten and Green and to debugging software are indicated. 0. Introduction. The central theme of this paper is a result in sequential analysis that has app...

• Count data are often modeled using a Poisson model. • If y ∼ Poisson(μ) then E(y) = var(y) = μ. • When counts are assumed exchangeable given μ and the rates μ can also be assumed to be exchangeable, a Gamma population model for the rates is often chosen. • The hierarchical model is then yi ∼ Poisson(μi) μi ∼ Gamma(α, β). • Priors for the hyperparameters are often taken to be Gamma (or exponen...

• Count data are often modeled using a Poisson model. • If y ∼ Poisson(μ) then E(y) = var(y) = μ. • When counts are assumed exchangeable given μ and the rates μ can also be assumed to be exchangeable, a Gamma population model for the rates is often chosen. • The hierarchical model is then yi ∼ Poisson(μi) μi ∼ Gamma(α, β). • Priors for the hyperparameters are often taken to be Gamma (or exponen...

A general-arrival, bulk service time queueing model is formulated for studying the distribution of random queues in signalized networks. The model is predicated on the occurrence of three traffic stream transformations: merging, splitting, and filtering. The model is applied to steady-state conditions (traffic intensity < 1.0) but can be eventually converted to a time-dependent form to account ...

In this paper, we set up a stochastic model for the dynamics of active Rab5 and Rab7 proteins on surface endosomes acidification process that govern virus–endosome fusion endosomal escape pH-responsive nanoparticles. We employ well-known cut-off switch to conversion consider two random terms: white Gaussian Poisson noises with zero mean. derive governing equations joint probability density func...