Ilia Vonta

This paper develops a competing risks model within the framework of Multi-State Systems (MSS) methodology, focusing on statistical inference when sojourn times (waiting times) are from a general closed-under-minima family of distributions. The proposed family includes a broad class of time-to-event distributions and thus is highly applicable to reliability theory, survival analysis and other fields of study. We set up a consistent statistical framework for inference and evaluate its performance through large-scale simulation studies. The findings validate the flexibility and accuracy of the proposed method, signifying its potential in modeling advanced failure mechanisms. The contribution of this study lies in presenting a new, holistic approach to competing risks analysis with its wide applications in various disciplines.

Log-concavity and log-convexity play a key role in various scientific fields, especially in those where the distinction between exponential and non-exponential distributions is necessary for inferential purposes. In the present study, we introduce a testing procedure for the tail part of a distribution which can be used for the distinction between exponential and non-exponential distributions. The conspiracy and catastrophe principles are initially used to establish a characterization of (the tail part of) the exponential distribution, which is one of the main contributions of the present work, leading the way for the construction of the new test of fit. The proposed test and its implementation are thoroughly discussed, and an extended simulation study has been undertaken to clarify issues related to its implementation and explore the extent of its capabilities. A real data case is also investigated.

This paper deals with the presentation and study of alternative coupling techniques for maximum and minimum values between data sets, namely the problem which is examined in this work is the possible appearance of maximum or minimum values between data sets in the same or neighboring time points. The data can be time-dependent (time series) or non-time-dependent. In this work, the analysis is focused on time series and novel indices are defined in order to measure whether the values of N sets of data display in terms of time, the maximum or minimum values at the same instances or at very close instances. For this purpose, two methods will be compared, one direct method and one indirect method. The indirect method is based on Matrices of dimensionless indicators which are denoted by [mu]([MKN]), and the direct method is based on a variance-type measure which is denoted by [V]([MKN]).