61 research outputs found
Approximation of supply curves
In this note, we illustrate the computation of the approximation of the
supply curves using a one-step basis. We derive the expression for the L2
approximation and propose a procedure for the selection of nodes of the
approximation. We illustrate the use of this approach with three large sets of
bid curves from European electricity markets
Bayesian Nested Latent Class Models for Cause-of-Death Assignment using Verbal Autopsies Across Multiple Domains
Understanding cause-specific mortality rates is crucial for monitoring
population health and designing public health interventions. Worldwide,
two-thirds of deaths do not have a cause assigned. Verbal autopsy (VA) is a
well-established tool to collect information describing deaths outside of
hospitals by conducting surveys to caregivers of a deceased person. It is
routinely implemented in many low- and middle-income countries. Statistical
algorithms to assign cause of death using VAs are typically vulnerable to the
distribution shift between the data used to train the model and the target
population. This presents a major challenge for analyzing VAs as labeled data
are usually unavailable in the target population. This article proposes a
Latent Class model framework for VA data (LCVA) that jointly models VAs
collected over multiple heterogeneous domains, assign cause of death for
out-of-domain observations, and estimate cause-specific mortality fractions for
a new domain. We introduce a parsimonious representation of the joint
distribution of the collected symptoms using nested latent class models and
develop an efficient algorithm for posterior inference. We demonstrate that
LCVA outperforms existing methods in predictive performance and scalability.
Supplementary materials for this article and the R package to implement the
model are available online.Comment: Main paper: 45 pages, 9 figures. Supplement: 20 pages, 16 figures, 2
table
Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies
Only about one-third of the deaths worldwide are assigned a
medically-certified cause, and understanding the causes of deaths occurring
outside of medical facilities is logistically and financially challenging.
Verbal autopsy (VA) is a routinely used tool to collect information on cause of
death in such settings. VA is a survey-based method where a structured
questionnaire is conducted to family members or caregivers of a recently
deceased person, and the collected information is used to infer the cause of
death. As VA becomes an increasingly routine tool for cause-of-death data
collection, the lengthy questionnaire has become a major challenge to the
implementation and scale-up of VAs. In this paper, we propose a novel active
questionnaire design approach that optimizes the order of the questions
dynamically to achieve accurate cause-of-death assignment with the smallest
number of questions. We propose a fully Bayesian strategy for adaptive question
selection that is compatible with any existing probabilistic cause-of-death
assignment methods. We also develop an early stopping criterion that fully
accounts for the uncertainty in the model parameters. We also propose a
penalized score to account for constraints and preferences of existing question
structures. We evaluate the performance of our active designs using both
synthetic and real data, demonstrating that the proposed strategy achieves
accurate cause-of-death assignment using considerably fewer questions than the
traditional static VA survey instruments
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