1,721,007 research outputs found
On dynamic mutual information for bivariate lifetimes
No full textWe consider dynamic versions of the mutual information of lifetime distributions, with focus on past lifetimes, residual lifetimes and mixed lifetimes evaluated at different instants. This allows to study multicomponent systems, by measuring the dependence in conditional lifetimes of two components having possibly different ages. We provide some bounds, and investigate the mutual information of residual lifetimes within the time-transformed exponential model (under both the assumptions of unbounded and truncated lifetimes). Moreover, with reference to the order statistics of a random sample, we evaluate explicitly the mutual information between the minimum and the maximum, conditional on inspection at different times, and show that it is distribution-free. Finally, we develop a copula-based approach aiming to express the dynamic mutual information for past and residual bivariate lifetimes in an alternative way
On the mutual information between residual lifetime distributions
No full textThe mutual information is a measure of the dependence of two random variables.
We propose an extension of this notion finalized to describe residual lifetime
distributions, i.e. to measure the dependence between the remaining lifetimes
of two systems, given that both systems have survived up to time t.
Some examples of application of such a measure are presented
Competing risks within shock models
No full textWe consider a competing risks model, in which system failures are due to one
out of two mutually exclusive causes, formulated within the framework of shock
models driven by bivariate Poisson process. We obtain the failure densities
and the survival functions as well as other related quantities under three
different schemes. Namely, system failures are assumed to occur at the first
instant in which a random constant threshold is reached by (a) the sum of
received shocks, (b) the minimum of shocks, (c) the maximum of shocks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
On cumulative entropies
No full textIn analogy with the cumulative residual entropy recently proposed by Wang et al.
(2003a) and (2003b), we introduce and study the cumulative entropy, which is a
new measure of information alternative to the classical differential entropy.
We show that the cumulative entropy of a random lifetime X can be expressed as
the expectation of its mean inactivity time evaluated at X. Hence, our measure
is particularly suitable to describe the information in problems related to ageing
properties of reliability theory based on the past and on the inactivity times.
Our results include various bounds to the cumulative entropy, its connection to
the proportional reversed hazards model, and the study of its dynamic version
that is shown to be increasing if the mean inactivity time is increasing. The empirical
cumulative entropy is finally proposed to estimate the new information measure
A Virtual Agent as a Commensal Companion
No full textPrevious work introduced the concept of artificial commensal companions, i.e., embodied agents capable of interacting with humans during meals. They are supposed to bring the benefits of eating together in settings where a human would be forced to eat alone (e.g., elderly, hospitalized patients, self-isolation, etc.). This paper presents an experiment with a virtual agent and a human eating together. We invited volunteers to bring a small meal and let them chat briefly with the agent, simulating eating behaviors during the conversation. After the experience, participants filled out a questionnaire, providing quantitative and qualitative feedback. While results are encouraging (i.e., participants showed interest in eating with an agent), further work is still needed to provide more convincing results
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