5,037 research outputs found
High-dimensional genome-wide association study and misspecified mixed model analysis
We study behavior of the restricted maximum likelihood (REML) estimator under
a misspecified linear mixed model (LMM) that has received much attention in
recent gnome-wide association studies. The asymptotic analysis establishes
consistency of the REML estimator of the variance of the errors in the LMM, and
convergence in probability of the REML estimator of the variance of the random
effects in the LMM to a certain limit, which is equal to the true variance of
the random effects multiplied by the limiting proportion of the nonzero random
effects present in the LMM. The aymptotic results also establish convergence
rate (in probability) of the REML estimators as well as a result regarding
convergence of the asymptotic conditional variance of the REML estimator. The
asymptotic results are fully supported by the results of empirical studies,
which include extensive simulation studies that compare the performance of the
REML estimator (under the misspecified LMM) with other existing methods.Comment: 3 figure
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which
explores the tremendous data collected by mobile smart devices with prominent
spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing
Systems, temporally recruited mobile users can provide agile, fine-grained, and
economical sensing labors, however their self-interest cannot guarantee the
quality of the sensing data, even when there is a fair return. Therefore, a
mechanism is required for the system server to recruit well-behaving users for
credible sensing, and to stimulate and reward more contributive users based on
sensing truth discovery to further increase credible reporting. In this paper,
we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile
Crowdsensing Systems, which achieves credibility-driven user recruitment and
payback maximization for honest users with quality data. Via theoretical
analysis, we demonstrate the correctness of our design. The performance of our
scheme is evaluated based on extensive realworld trace-driven simulations. Our
evaluation results show that our scheme is proven to be effective in terms of
both guaranteeing sensing accuracy and resisting potential cheating behaviors,
as demonstrated in practical scenarios, as well as those that are intentionally
harsher
A Penalized Multi-trait Mixed Model for Association Mapping in Pedigree-based GWAS
In genome-wide association studies (GWAS), penalization is an important
approach for identifying genetic markers associated with trait while mixed
model is successful in accounting for a complicated dependence structure among
samples. Therefore, penalized linear mixed model is a tool that combines the
advantages of penalization approach and linear mixed model. In this study, a
GWAS with multiple highly correlated traits is analyzed. For GWAS with multiple
quantitative traits that are highly correlated, the analysis using traits
marginally inevitably lose some essential information among multiple traits. We
propose a penalized-MTMM, a penalized multivariate linear mixed model that
allows both the within-trait and between-trait variance components
simultaneously for multiple traits. The proposed penalized-MTMM estimates
variance components using an AI-REML method and conducts variable selection and
point estimation simultaneously using group MCP and sparse group MCP. Best
linear unbiased predictor (BLUP) is used to find predictive values and the
Pearson's correlations between predictive values and their corresponding
observations are used to evaluate prediction performance. Both prediction and
selection performance of the proposed approach and its comparison with the
uni-trait penalized-LMM are evaluated through simulation studies. We apply the
proposed approach to a GWAS data from Genetic Analysis Workshop (GAW) 18
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
MODELING, ANALYSIS, AND IMPROVEMENT OF DOOR PRODUCTION LINE AT AN AUTOMOTIVE BODY SHOP
ABSTRACT In this paper, we introduce a case study of modeling, analysis, and continuous improvement of a door production line at an automotive body shop using production systems engineering methods. Analytical models have been developed, and recursive procedures have been derived to evaluate line production rate. An arrow-based bottleneck analysis method is introduced to identify the bottlenecks, whose improvement can lead to the largest improvement in system performance. Such methods provide a quantitative tool for plant engineers and managers to operate and improve door production line to achieve high productivity, and are also applicable to other large volume manufacturing systems
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