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In many applications, we assume that two random observations x and yare generated according to independent Poisson distributions x1d4ab;(λS) and x1d4ab;(μT) and we are interested in performing statistical inference on the ratio φ = λ / μ of the two incidence rates. In vaccine efficacy trials, x and y are typically the numbers of cases in the vaccine and the control groups respectively, φ is called the relative risk and the statistical model is called ‘partial immunity model’. In this paper we...
In many applications, we assume that two random observations x and
y are generated according to independent Poisson distributions
𝒫(λS)
and 𝒫(μT)
and we are interested in performing statistical inference on the ratio
φ = λ / μ of the two
incidence rates. In vaccine efficacy trials, x and y are
typically the numbers of cases in the vaccine and the control groups respectively,
φ is called the relative risk...
This paper presents a Bayesian significance test for a change in mean when observations are not independent. Using a noninformative prior, a unconditional test based on the highest posterior density credible set is determined. From a Gibbs sampler simulation study the effect of correlation on the performance of the Bayesian significance test derived under the assumption of no correlation is examined. This paper is a generalization of earlier studies by KIM (1991) to not independent observations.
A network of mobile cooperative sensors is considered. The following problems are studied: (1) the “optimal“deployment of the sensors on a given territory; (2) the detection of local anomalies in the noisy data measured by the sensors. In absence of an information fusion center in the network, from “local” interactions between sensors “global“solutions of these problems are found.
A network of mobile cooperative sensors is considered. The following
problems are studied:
(1) the “optimal" deployment of the sensors on a given territory;
(2) the detection of local anomalies in the noisy data measured by the
sensors.
In absence of an information fusion center in the network, from “local" interactions between sensors “global" solutions of these problems are found.
Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for a real data...
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