3,749 research outputs found
A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates (New Developments on Mathematical Decision Making Under Uncertainty)
The black-box approach based on stochastic software reliability models is a simple methodology with only software fault data in order to describe the temporal behavior of fault-detection processes, but fails to incorporate some significant development metrics data observed in the development process. In this paper we develop proportional intensity-based software reliability models with time-dependent metrics, and propose a statistical framework to assess the software reliability with the timedependent covariate as well as the software fault data. The resulting models are similar to the usual proportional hazard model, but possess somewhat different covariate structure from the existing one. We compare these metricsbased software reliability models with eleven well-known non-homogeneous Poisson process models, which are the special cases of our models, and evaluate quantitatively the goodness-of-fit and prediction. As an important result, the accuracy on reliability assessment strongly depends on the kind of software metrics used for analysis and can be improved by incorporating the time-dependent metrics data in modeling
Posterior accuracy and calibration under misspecification in Bayesian generalized linear models
Generalized linear models (GLMs) are popular for data-analysis in almost all
quantitative sciences, but the choice of likelihood family and link function is
often difficult. This motivates the search for likelihoods and links that
minimize the impact of potential misspecification. We perform a large-scale
simulation study on double-bounded and lower-bounded response data where we
systematically vary both true and assumed likelihoods and links. In contrast to
previous studies, we also study posterior calibration and uncertainty metrics
in addition to point-estimate accuracy. Our results indicate that certain
likelihoods and links can be remarkably robust to misspecification, performing
almost on par with their respective true counterparts. Additionally, normal
likelihood models with identity link (i.e., linear regression) often achieve
calibration comparable to the more structurally faithful alternatives, at least
in the studied scenarios. On the basis of our findings, we provide practical
suggestions for robust likelihood and link choices in GLMs
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions
There is an especially strong need in modern large-scale data analysis to
prioritize samples for manual inspection. For example, the inspection could
target important mislabeled samples or key vulnerabilities exploitable by an
adversarial attack. In order to solve the "needle in the haystack" problem of
which samples to inspect, we develop a new scalable version of Cook's distance,
a classical statistical technique for identifying samples which unusually
strongly impact the fit of a regression model (and its downstream predictions).
In order to scale this technique up to very large and high-dimensional
datasets, we introduce a new algorithm which we call "influence sketching."
Influence sketching embeds random projections within the influence computation;
in particular, the influence score is calculated using the randomly projected
pseudo-dataset from the post-convergence Generalized Linear Model (GLM). We
validate that influence sketching can reliably and successfully discover
influential samples by applying the technique to a malware detection dataset of
over 2 million executable files, each represented with almost 100,000 features.
For example, we find that randomly deleting approximately 10% of training
samples reduces predictive accuracy only slightly from 99.47% to 99.45%,
whereas deleting the same number of samples with high influence sketch scores
reduces predictive accuracy all the way down to 90.24%. Moreover, we find that
influential samples are especially likely to be mislabeled. In the case study,
we manually inspect the most influential samples, and find that influence
sketching pointed us to new, previously unidentified pieces of malware.Comment: fixed additional typo
A Predictive Modeling Approach for Assessing Seismic Soil Liquefaction Potential Using CPT Data
Soil liquefaction, or loss of strength due to excess pore water pressures generated during dynamic loading, is a main cause of damage during earthquakes. When a soil liquefies (referred to as triggering), it may lose its ability to support overlying structures, deform vertically or laterally, or cause buoyant uplift of buried utilities. Empirical liquefaction models, used to predict liquefaction potential based upon in-situ soil index property measurements and anticipated level of seismic loading, are the standard of practice for assessing liquefaction triggering. However, many current models do not incorporate predictor variable uncertainty or do so in a limited fashion. Additionally, past model creation and validation lacks the same rigor found in predictive modeling in other fields.
This study examines the details of creating and validating an empirical liquefaction model, using the existing worldwide cone penetration test liquefaction database. Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic interpretation of model parameters. Our model is built using a hierarchal approach account for intra-event correlation in loading variables and differences in event sample sizes that mirrors the random/mixed effects models used in ground motion prediction equation development. The model is tested using an independent set of case histories from recent New Zealand earthquakes, and performance metrics are reported.
We found that a Bayesian measurement error model considering two predictor variables, qc,1 and CSR, decreases model uncertainty while maintaining predictive utility for new data. Two forms of model uncertainty were considered – the spread of probabilities predicted by mean values of regression coefficients (apparent uncertainty) and the standard deviations of the predictive distributions from fully probabilistic inference. Additionally, we found models considering friction ratio as a predictor variable performed worse than the two variable case and will require more data or informative priors to be adequately estimated
Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions
Face-to-face social contacts are potentially important transmission routes
for acute respiratory infections, and understanding the contact network can
improve our ability to predict, contain, and control epidemics. Although
workplaces are important settings for infectious disease transmission, few
studies have collected workplace contact data and estimated workplace contact
networks. We use contact diaries, architectural distance measures, and
institutional structures to estimate social contact networks within a Swiss
research institute. Some contact reports were inconsistent, indicating
reporting errors. We adjust for this with a latent variable model, jointly
estimating the true (unobserved) network of contacts and duration-specific
reporting probabilities. We find that contact probability decreases with
distance, and research group membership, role, and shared projects are strongly
predictive of contact patterns. Estimated reporting probabilities were low only
for 0-5 minute contacts. Adjusting for reporting error changed the estimate of
the duration distribution, but did not change the estimates of covariate
effects and had little effect on epidemic predictions. Our epidemic simulation
study indicates that inclusion of network structure based on architectural and
organizational structure data can improve the accuracy of epidemic forecasting
models.Comment: 36 pages, 4 figure
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Phylogenetic comparative analysis is an approach to inferring evolutionary
process from a combination of phylogenetic and phenotypic data. The last few
years have seen increasingly sophisticated models employed in the evaluation of
more and more detailed evolutionary hypotheses, including adaptive hypotheses
with multiple selective optima and hypotheses with rate variation within and
across lineages. The statistical performance of these sophisticated models has
received relatively little systematic attention, however. We conducted an
extensive simulation study to quantify the statistical properties of a class of
models toward the simpler end of the spectrum that model phenotypic evolution
using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and
why these methods break down so that users can apply them with greater
understanding of their strengths and weaknesses. Our analysis identifies three
key determinants of performance: a discriminability ratio, a signal-to-noise
ratio, and the number of taxa sampled. Interestingly, we find that
model-selection power can be high even in regions that were previously thought
to be difficult, such as when tree size is small. On the other hand, we find
that model parameters are in many circumstances difficult to estimate
accurately, indicating a relative paucity of information in the data relative
to these parameters. Nevertheless, we note that accurate model selection is
often possible when parameters are only weakly identified. Our results have
implications for more sophisticated methods inasmuch as the latter are
generalizations of the case we study.Comment: 38 pages, in press at Systematic Biolog
- …