Assessment of vague and noninformative priors for Bayesian estimation of the realized random effects in random-effects meta-analysis

Random-effects meta-analysis has become a well-established tool applied in many areas, for example, when combining the results of several clinical studies on a treatment effect. Typically, the inference aims at the common mean and the amount of heterogeneity. In some applications, the laboratory effects are of interest, for example, when assessing uncertainties quoted by laboratories participating in an interlaboratory comparison in metrology. We consider the Bayesian estimation of the realized random effects in random-effects meta-analysis. Several vague and noninformative priors are examined as well as a proposed novel one. Conditions are established that ensure propriety of the posteriors for the realized random effects. We present extensive simulation results that assess the inference in dependence on the choice of prior as well as mis-specifications in the statistical model. Overall good performance is observed for all priors with the novel prior showing the most promising results. Finally, the uncertainties reported by eleven national metrology institutes and universities for their measurements on the Newtonian constant of gravitation are assessed.     

CUSUM control schemes for monitoring the covariance matrix of multivariate time series

Modified cumulative sum (CUSUM) control charts and CUSUM schemes for residuals are suggested to detect changes in the covariance matrix of multivariate time series. Several properties of these schemes are derived when the in-control process is a stationary Gaussian process. A Monte Carlo study reveals that the proposed approaches show similar or even better performance than the schemes based on the multivariate exponentially weighted moving average (MEWMA) recursion. We illustrate how the control procedures can be applied to monitor the covariance structure of developed stock market indices.     

Statistical inference procedure for the mean–variance efficient frontier with estimated parameters

We suggest finite sample tests for the location of the efficient frontier with the estimated parameters in mean–variance space. The exact densities of the test statistics are derived. We implement the introduced testing procedure empirically by considering monthly returns of ten developed stock markets. It is shown that ignoring the uncertainty about the estimated parameters leads to a more frequent reconstruction of the efficient frontier.     

The Role of Similarity Cues in the Development of Trustin Sources of Information About GM Food

In evaluating complex new technologies, people are usually dependent on information provided by others, for example, experts or journalists, and have to determine whether they can trust these information sources. This article focuses on similarity as the basis for trust. The first experiment ( N  = 261) confirmed that a journalist writing about genetically modified (GM) food was trusted more when his attitude was congruent with that of his readers. In addition, the experiment showed that this effect was mediated by the perceived similarity of the journalist. The second experiment ( N  = 172) revealed that trust in a journalist writing about the focal domain of GM food was even influenced by him expressing a congruent attitude in an unrelated domain. This result supports a general similarity account of the congruence effect on trust, as opposed to a confirmatory bias account.     

Multivariate control charts based on a projection approach

Summary: In this paper the projection approach of Runger (1996) is applied to construct control charts for a multivariate process. It is assumed that a shift in the mean might only occur in a known subspace of the parameter space. The projection method permits a reduction of the dimensionality of the control problem.Several control schemes based on projections are introduced. We consider CUSUM type charts as well as EWMA schemes. The underlying variables are assumed to be independent and normally distributed. Using the average run length all control charts are compared with each other. Moreover, it is analyzed how sensitive the charts react on a false choice of the subspace.     

CUSUM charts for monitoring the mean of a multivariate Gaussian process

In this paper control charts for the mean of a multivariate Gaussian process are considered. Using the generalized likelihood ratio approach and the sequential probability ratio test under an additional constraint on the magnitude of the change various types of CUSUM control charts are derived. It is analyzed under which conditions these schemes are directionally invariant. These charts are compared with several other control schemes proposed in literature. The performance of the charts is studied based on the maximum average delay.     

Application of the Generalized Likelihood Ratio Test for Detecting Changes in the Mean of Multivariate GARCH Processes

We derive several multivariate control charts to monitor the mean vector of multi-variate GARCH processes under the presence of changes, by means of maximizing the generalized likelihood ratio. This presentation is rounded up by a comparative performance study based on extensive Monte Carlo simulations. An empirical illustration shows how the obtained results can be applied to real data.