University of Cyprus

About: University of Cyprus is a education organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Large Hadron Collider & Context (language use). The organization has 3624 authors who have published 15157 publications receiving 412135 citations.

Making use of real options simple: an overview and applications in flexible/modular decision making

Abstract: This article focuses on how the use of real options can be made simple, providing an overview of the power of flexible and modular decision making and its use in various applications across industries. After common real options are discussed through a comprehensive example, the article reviews the key lessons and implications of real options thinking for flexible decision making. It then proceeds to propose a modular problem structuring approach that allows simplifying of complex real option problems by decomposing them into a few basic building-block option types (reviewed) connected by some basic decision operators. The resulting problem-structuring option map is depicted in a range of illustrative applications in various industries. Past areas of application of real options as well as research challenges ahead are also discussed.

Verification of $K$ -Step Opacity and Analysis of Its Complexity

Abstract: Motivated by security and privacy considerations in a variety of applications of discrete event systems, we describe and analyze the computational complexity required for verifying the notion of K -step opacity for systems that are modeled as nondeterministic finite automata with partial observation on their transitions. Specifically, a system is K-step opaque if, at any specific point within the last K observations, the entrance of the system state to a given set of secret states remains opaque (uncertain) to an intruder who has complete knowledge of the system model and observes system activity through some natural projection map. We provide two methods for verifying K -step opacity using two different state estimator constructions, and analyze the computational complexity of both.

Quasi‐likelihood inference for negative binomial time series models

Abstract: We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi-likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.