304 research outputs found
Latent demographic profile estimation in hard-to-reach groups
The sampling frame in most social science surveys excludes members of certain
groups, known as hard-to-reach groups. These groups, or subpopulations, may be
difficult to access (the homeless, e.g.), camouflaged by stigma (individuals
with HIV/AIDS), or both (commercial sex workers). Even basic demographic
information about these groups is typically unknown, especially in many
developing nations. We present statistical models which leverage social network
structure to estimate demographic characteristics of these subpopulations using
Aggregated relational data (ARD), or questions of the form "How many X's do you
know?" Unlike other network-based techniques for reaching these groups, ARD
require no special sampling strategy and are easily incorporated into standard
surveys. ARD also do not require respondents to reveal their own group
membership. We propose a Bayesian hierarchical model for estimating the
demographic characteristics of hard-to-reach groups, or latent demographic
profiles, using ARD. We propose two estimation techniques. First, we propose a
Markov-chain Monte Carlo algorithm for existing data or cases where the full
posterior distribution is of interest. For cases when new data can be
collected, we propose guidelines and, based on these guidelines, propose a
simple estimate motivated by a missing data approach. Using data from McCarty
et al. [Human Organization 60 (2001) 28-39], we estimate the age and gender
profiles of six hard-to-reach groups, such as individuals who have HIV, women
who were raped, and homeless persons. We also evaluate our simple estimates
using simulation studies.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS569 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An Efficient Design Representation of Conservative Reversible Logic Gates
Computers, which have become a ubiquitous staple of modern society, consume nearly 10% of all the energy produced worldwide. While computing implementation technology has been made more energy efficient over the years, the energy required to operate a gate logically has become an increasingly large proportion of the total energy required. A systematic improvement of this power use would result in significant power savings, which would grow even more appreciable as overall efficiency improves. One method for achieving these power savings would be the use of conservative reversible logic (CRL) gates for system design. However, to date, only a few designs using these types of gates have been developed. This sporadic development is primarily due to the lack of a systematic method for representing CRL gates. This work describes an accurate and compact design representation of CRL gates based on a known variation of a zero-suppressed binary decision diagram, called PiDD. Two methods of adapting PiDDs to represent CRL gates of any size are presented, along with examples of design manipulation and analysis
Reactive point processes: A new approach to predicting power failures in underground electrical systems
Reactive point processes (RPPs) are a new statistical model designed for
predicting discrete events in time based on past history. RPPs were developed
to handle an important problem within the domain of electrical grid
reliability: short-term prediction of electrical grid failures ("manhole
events"), including outages, fires, explosions and smoking manholes, which can
cause threats to public safety and reliability of electrical service in cities.
RPPs incorporate self-exciting, self-regulating and saturating components. The
self-excitement occurs as a result of a past event, which causes a temporary
rise in vulner ability to future events. The self-regulation occurs as a result
of an external inspection which temporarily lowers vulnerability to future
events. RPPs can saturate when too many events or inspections occur close
together, which ensures that the probability of an event stays within a
realistic range. Two of the operational challenges for power companies are (i)
making continuous-time failure predictions, and (ii) cost/benefit analysis for
decision making and proactive maintenance. RPPs are naturally suited for
handling both of these challenges. We use the model to predict power-grid
failures in Manhattan over a short-term horizon, and to provide a cost/benefit
analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Throwing the Flag on Copyright Warnings: How Professional Sports Organizations Systematically Overstate Copyright Protection
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