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Non-parametric probabilistic classification models are increasingly being investigated as an
alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...
Suppose we are given two independent strings of data from a known finite alphabet. We are interested in testing the null hypothesis that both the strings were drawn from the same distribution, assuming that the samples within each string are mutually indep ...
In this paper we give a preview of our system for automatically evaluating attention in the classroom. We demonstrate our current behaviour metrics and preliminary observations on how they reflect the reactions of people to the given lecture. We also intro ...
Water temperature is one of the most important abiotic variables in streams and strongly influences the distribution and abundance of freshwater organisms. It might be assumed that in natural streams home to heterogenous habitats also a wide range of therm ...
Most researchers want evidence for the direction of an effect, not evidence against a point null hypothesis. Such evidence is ideally on a scale that is easily interpretable, with an accompanying standard error. Further, the evidence from identical experim ...
In this paper, we investigate the construction of compromise estimators of location and scale, by averaging over several models selected among a specified large set of possible models. The weight given to each distribution is based on the profile likelihoo ...
Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...
Some equivalence tests are based on two one-sided tests, where in many applications the test statistics are approximately normal. We define and find evidence for equivalence in Z-tests and then one-and two-sample binomial tests as well as for t-tests. Mult ...
Variance stabilization is a simple device for normalizing a statistic. Even though its large sample properties are similar to those of studentizing, many simulation studies of confidence interval procedures show that variance stabilization works better for ...
