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Results for “probit regression”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effect of thermal conditioning on the initiation threshold of secondary high‐explosives

While most performance metrics of high–explosive (HE) based devices like detonation velocity, detonation pressure, and energy output are expected to degrade over time, the evolution of initiation threshold appears less clear, with claims of both increasing and decreasing trends in threshold having been made in the literature. This work analyzes D–optimally designed sequential binary test data for a few thermally conditioned porous–powder and polymer–bonded HE initiator systems using a Bayesian likelihood method employing the probit regression model. Here we find that in most cases the initiation threshold decreases (i.e., sensitivity increases) upon accelerated thermal conditioning. However, such results are nuanced and influenced by factors like the contact area of initiating stimulus, HE characteristics like density and specific surface area, as well as possible thermally induced changes to other materials and interfaces involved.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Comparing the reliability of related populations with the probability of agreement

Combining information from different populations to improve precision, simplify future predictions, or improve underlying understanding of relationships can be advantageous when considering the reliability of several related sets of systems. Using the probability of agreement to help quantify the similarities of populations can help to give a realistic assessment of whether the systems have reliability that are sufficiently similar for practical purposes to be treated as a homogeneous population. In addition, the new method is described and illustrated with an example involving two generations of a complex system where the reliability is modeled using either a logistic or probit regression model. Note that supplementary materials including code, datasets, and added discussion are available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Parametric and Nonparametric Models of U.S. Cost Overruns for Nuclear Power Plants

This study presents new data-driven models to estimate the effect of capacity on the percentage of cost overruns in the United States for nuclear power plant construction projects before and after the Three Mile Island accident. Parametric and nonparametric models have been developed that describe the significant shifts in nuclear energy costs during the dynamic environment. Employing a contemporary descriptive methodology and a quantitative analysis, we furnish a comprehensive overview of the alterations in cost overrun distribution and show the changes observed in other pivotal metrics alongside cost overruns. Our emphasis lies in documenting the fluctuations in cost overruns alongside nuclear reactor capacity levels and the increase of the overnight capital costs to build nuclear reactors. Our results show that increasing the size of nuclear reactors is not a factor statistically significant to decrease the percentage of cost overruns, and the probit model results provide evidence that an increase in size increases the probability of having cost overruns larger than 100% (double the estimated cost). We also compare our findings to two other regions: Asia and Europe.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Panning for gold: Enhancing the precision of sensitivity test data

Sensitivity tests apply a range of stimulus values to experimental subjects and record binary responses in order to estimate the distribution of threshold values in the subject population, where thresholds delineate responses from nonresponses. In many applications, such as explosives engineering, individual tests are expensive and are conducted in small runs. Scarcity of data results in nonexistence of estimates, or estimates with low precision. Here, we discuss various methods, such as combining test runs, covariate analysis, and penalized maximum likelihood, for enhancing precision and “mining more gold” from expensive test results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Single toxin dose-response models revisited

The goal of this paper is to offer a rigorous analysis of the sigmoid shape single toxin dose-response relationship. The toxin efficacy function is introduced and four special points, including maximum toxin efficacy and inflection points, on the dose-response curve are defined. The special points define three phases of the toxin effect on mortality: (1) toxin concentrations smaller than the first inflection point or (2) larger then the second inflection point imply low mortality rate, and (3) concentrations between the first and the second inflection points imply high mortality rate. Probabilistic interpretation and mathematical analysis for each of the four models, Hill, logit, probit, and Weibull is provided. Two general model extensions are introduced: (1) the multi-target hit model that accounts for the existence of several vital receptors affected by the toxin, and (2) model with a nonzero mortality at zero concentration to account for natural mortality. Special attention is given to statistical estimation in the framework of the generalized linear model with the binomial dependent variable as the mortality count in each experiment, contrary to the widespread nonlinear regression treating the mortality rate as continuous variable. The models are illustrated using standard EPA Daphnia acute (48 h) toxicity tests with mortality as a function of NiCl or CuSO{sub 4} toxin. - Highlights: • The paper offers a rigorous study of a sigmoid dose-response relationship. • The concentration with highest mortality rate is rigorously defined. • A table with four special points for five morality curves is presented. • Two new sigmoid dose-response models have been introduced. • The generalized linear model is advocated for estimation of sigmoid dose-response relationship.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

97 MATHEMATICS AND COMPUTING↗