For over 50 years expected value and expected utility theory has been challenged by behavioral findings in repeated and single plays of risky gambles. The inherent long-term nature of these models has been found to be at odds with preferences indicating short-term maximization in single play situations. With the present study we provide further evidence on the distinction between long-term and ...

Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, ERGMs framework has been successfully employed for reconstructing networks, detecting statistically significant patterns in graphs, counting networked configurations with given properties. From a technical point of view, workflow is defined by two subsequent optimization st...

Often the probability of responding depends directly on the outcome value. This case can be treated by postulating a parametric model for the distribution of the outcomes before nonresponse and a model for the response mechanism. The two models define a parametric model for the joint distribution of the outcomes and response indicators, and therefore the parameters of these models can be estima...

Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a s...

A (Stochastic) EM in General Expectation-Maximization (EM) is an iterative method for finding the maximum likelihood or maximum a posteriori (MAP) estimates of the parameters in statistical models when data is only partially, or when model depends on unobserved latent variables. This section is inspired from lecture of Dr Namrata Vaswani available at http://www.ece.iastate.edu/∼namrata/EE527 Sp...

This paper addresses the estimation of fuzzy Gaussian distribution mixture with applications to unsupervised statistical fuzzy image segmentation. In a general way, the fuzzy approach enriches the current statistical models by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several them...

This chapter examines the estimation of multivariable linear models for which the parameters vary in a time-varying manner that depends in an affine fashion on a known or otherwise measured signal. These locally linear models which depend on a measurable operating point are known as linear parameter varying (LPV) models. The contribution here relative to previous work on the topic is that in th...