Bayesian inference for dynamic social network data

We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations.     

A Test to Detect Within-family Infectivity when the Whole Epidemic Process is Observed

An epidemic model for the spread of an infectious disease in a population of families is considered. The score test of the hypothesis that there is no higher infectivity between family members is constructed under the assumption that the epidemic process is observed continuously up to some time t . The score process is a martingale as a function of t and by letting the number of families tend to infinity, a central limit theorem for the process can be proved. The central limit theorem not only justifies a normal approximation of the test statistic—it also suggests a smaller variance estimator than expected.     

Bayesian point null hypothesis testing via the posterior likelihood ratio

This paper gives an exposition of the use of the posterior likelihood ratio for testing point null hypotheses in a fully Bayesian framework. Connections between the frequentist P-value and the posterior distribution of the likelihood ratio are used to interpret and calibrate P-values in a Bayesian context, and examples are given to show the use of simple posterior simulation methods to provide Bayesian tests of common hypotheses.     

Circular Questioning and Shifting Relationships

This article is a contribution to the necessary development of process evaluation in family therapy. A videotaped case, consisting of six sessions, is reviewed and the changes in the various family relationships are described. Attempts are made to look for correspondences between circular questioning in one of the sessions and shifts in relationships.     

The individual welfare function: A review

The Individual Welfare Function (IWF), introduced by Van Praag (1968), is a cardinal utility function. It can be measured by means of survey questions. Since its introduction, the IWF has been used extensively in both theoretical and empirical research. This research is reviewed, with an emphasis on policy applications.     

A New Look at Half-normal Plots for Assessing the Significance of Contrasts for Unreplicated Factorials

The visual inspection of a half-normal plot is a popular procedure for interpreting data from unreplicated factorial experiments. As this technique is somewhat subjective, the plot should preferably be supplemented with a more formal procedure. However, despite the improvements proposed by Zahn, the formal method using the half-normal plot suggested by Daniel seems to be rarely used. We investigate this procedure by using simulations and real data and suggest a modification to improve its acceptability. We also present corrected critical values for a procedure proposed by Lenth which was found to be miscalibrated.     

Bayesian sample-size determination for one and two Poisson rate parameters with applications to quality control

We formulate Bayesian approaches to the problems of determining the required sample size for Bayesian interval estimators of a predetermined length for a single Poisson rate, for the difference between two Poisson rates, and for the ratio of two Poisson rates. We demonstrate the efficacy of our Bayesian-based sample-size determination method with two real-data quality-control examples and compare the results to frequentist sample-size determination methods.