DATAWorks Sessions Archive

No set of experimental data is perfect and researchers are aware that data from experimental studies invariably contain some margin of error. This is particularly true of studies with human subjects since human behavior is vulnerable to a range of intrinsic and extrinsic influences beyond the variables being manipulated in a controlled experimental setting. Potential sources of error may lead to wide variations in the interpretation of results and the formulation of subsequent implications. This talk will discuss specific sources of error and bias in the design of experiments and present systematic ways to overcome these effects. First, some of the basic errors in general experimental design will be discussed, including human errors, systematic errors and random errors. Second, we will explore specific types of experimental error that appear in human subjects research. Lastly, we will discuss the role of bias in experiments with human subjects. Bias is a type of systematic error that is introduced into the sampling or testing phase and encourages one outcome over another. Often, bias is the result of the intentional or unintentional influence that an experimenter may exert on the outcomes of a study. We will discuss some common sources of bias in research with human subjects, including biases in sampling, selection, response, performance execution, and measurement. The talk will conclude with a discussion of how errors and bias influence the validity of human subjects research and will explore some strategies for controlling these errors and biases

In recent years, software testing techniques based on formal methods have made their way into industrial practice as a supplement to system and unit testing. I will discuss three core techniques that have proven particularly amenable to transition: 1) Concolic execution, which enables the automatic generation of high-coverage test suites; 2) Property-based randomized testing, which automatically checks sequences of API calls to ensure that expected high-level behavior occurs; and 3) Bounded model checking, which enables systematic exploration of both concrete systems and high-level models to check temporal properties, including ordering of events and timing requirements.

Many software reliability models characterize the number of faults detected during the testing process as a function of testing time, which is performed over multiple stages. Typically, the later stages are progressively more expensive because of the increased number of personnel and equipment required to support testing as the system nears completion. Such transitions from one stage of testing to the next change in the operational environment. One statistical approach to combine software reliability growth models in a manner capable of characterizing multi-stage testing is the concept of a change-point process, where the intensity of a process experiences a distinct change at one or more discrete times during testing. Thus, change-point processes can be used to model change in the failure rate of software due to changes in the testing strategy and environment, integration testing, and resource allocation as it proceeds through multiple stages of testing. This presentation generalizes change-point models to the heterogeneous case, where fault detection before and after a change-point can be characterized by distinct nonhomogeneous Poisson processes (NHPP). Experimental results suggest that heterogeneous change-point models better characterize some failure data sets, which can improve the applicability of software reliability models to large-scale software systems that are tested over multiple stages.

Software reliability models enable several quantitative predictions such as the number of faults remaining, failure rate, and reliability (probability of failure free operation for a specified period of time in a specified environment). This talk will describe recent efforts in collaboration with NASA, including (1) the development of an automated script for the SFRAT (Software Failure and Reliability Assessment Tool) to streamline application of software reliability methods to ongoing programs, (2) application to a NASA program, (3) lessons learned, (4) and future directions for model and tool development to support the practical needs of the software reliability and security assessment frameworks.

A crucial step in the design and development of a fight vehicle, such as NASA’s Space Launch System (SLS), is understanding its vibration behavior while in fight. Vehicle designers rely on low-cost finite element analysis (FEA) to predict the vibration behavior of the vehicle. During ground and flight tests, sensors are strategically placed at predefined locations that contribute the most vibration information under the assumption that FEA is accurate, producing points to validate the FEA models. This collaborative work focused on developing optimal sensor placement algorithms to validate FEA models against test data, and to characterize the vehicles vibration characteristics.

The concept of sequential testing has many disparate meanings. Often, for statisticians it takes on a purely mathematical context while possibly meaning multiple disconnected test events for some practitioners. Here we present a pedagogical approach to creating test designs involving constrained factors using JMP software. Recent experiences testing one of the U.S. military’s fast jet life support systems (LSS) serves as a case study and back drop to support the presentation. The case study discusses several lessons learned during LSS testing, applicable to all practitioners of scientific test and analysis techniques (STAT) and design of experiments (DOE). We conduct a short analysis to specifically determine a test region with a set of factors pertinent to modeling human breathing and the use of breathing machines as part of the laboratory setup. A comparison of several government and industry laboratory test points and regions with governing documentation is made, along with the our proposal for determining a necessary and sufficient test region for tests involving human breathing as a factor.

Binary response experiments are common in epidemiology, biostatistics as well as in military applications. The Up and Down method, Langlie’s Method, Neyer’s method, K in a Row method and 3 Phase Optimal Design are methods used for sequential experimental design when there is a single continuous variable and a binary response. During this talk, we will discuss a new sequential experimental design approach called the Break Separation Method (BSM). BSM provides an algorithm for determining sequential experimental trials that will be used to find a median quantile and fit a logistic regression model using Maximum Likelihood estimation. BSM results in a small sample size and is designed to efficiently compute the median quantile.

Binary response experiments are common in epidemiology, biostatistics as well as in military applications. The Up and Down method, Langlie’s Method, Neyer’s method, K in a Row method and 3 Phase Optimal Design are methods used for sequential experimental design when there is a single continuous variable and a binary response. During this talk, we will discuss a new sequential experimental design approach called the Break Separation Method (BSM). BSM provides an algorithm for determining sequential experimental trials that will be used to find a median quantile and fit a logistic regression model using Maximum Likelihood estimation. BSM results in a small sample size and is designed to efficiently compute the median quantile.

Small screening designs are frequently used in the initial stages of experimentation with the goal of identifying important main effects as well as to gain insight on potentially important two-factor interactions. Commonly utilized experimental designs for screening (e.g., resolution III or IV two-level fractional factorials, Plackett-Burman designs, etc.) are unreplicated and as such, provide no unbiased estimate of experimental error. However, if statistical inference is considered an integral part of the experimental analysis, one view is that inferential procedures should be performed using the unbiased pure error estimate. As full replication of an experiment may be quite costly, partial replication offers an alternative for obtaining a model independent error estimate. Gilmour and Trinca (2012, Applied Statistics) introduce criteria for the design of optimal experiments for statistical inference (providing for the optimal selection of replicated design points). We begin with an extension of their work by proposing a Bayesian criterion for the construction of partially replicated screening designs with less dependence on an assumed model. We then consider the use of the proposed criterion within the context of multi-criteria design selection where estimation and protection against model misspecification are considered. Insights for analysis and model selection in light of partial replication will be provided.

Abstract: In applications where modeling and simulation runs are quick and cheap, space filling designs will give the tester all the information they need to make decisions about their system. In some applications however, this luxury does not exist, and each M&S run can be time consuming and expensive. In these scenarios, a sequential test approach provides an efficient solution where an initial screening is conducted, followed by an augmentation to fit specified models of interest. Until this point, no dedicated screening designs for UQ applications in resource constrained situations existed. Due to the Army’s frequent exposure to this type of situation, the need sparked a collaboration between Picatinny’s Statistical Methods and Analysis group and Professor V. Roshan Joseph of Georgie Tech, where a new type of UQ screening design was created. This paper provides a brief introduction to the design, its intended use, and a case study in which this new methodology was applied.

The Satellite Affordability in LEO (SAL) model identifies the cheapest constellation capable of providing a desired level of performance within certain constraints. SAL achieves this using a combination of analytical models, statistical emulators, and geometric relationships. SAL is flexible and modular, allowing users to customize certain components while retaining default behavior in other cases. This is desirable if users wish to consider an alternative cost formulation or different types of payload. Uses for SAL include examining cost tradeoffs with respect to factors like constellation size and desired performance level, evaluating the sensitivity of constellation costs to different assumptions about cost behavior, and providing a first-pass look at what proliferated smallsats might be capable of. At this point, SAL is limited to Walker constellations with sun-synchronous, polar orbits.

NASA is investigating the dose-response relationship between quiet sonic boom exposure and community noise perceptions. This relationship is the key to possible future regulations that would replace the ban on commercial supersonic flights with a noise limit. We have built several Bayesian statistical models using pilot community study data. Using goodness of fit measures, we downselected to a subset of models which are the most appropriate for the data. From this subset of models we demonstrate how to calculate sample size requirements for a simplified example without any missing data. We also suggest how to modify the sample size calculation to account for missing data.

Sample size drives the resources and supports the conclusions of operational test. Power analysis is a common statistical methodology used in planning efforts to justify the number of samples. Power analysis is sensitive to extreme performance (e.g. 0.1% correct responses or 99.999% correct responses) relative to a threshold value, extremes in response variable variability, numbers of factors and levels, system complexity, and a myriad of other design- and system-specific criteria. This discussion will describe considerations (correlation/aliasing, operational significance, thresholds, etc.) and relationships (design, difference to detect, noise, etc.) associated with power. The contribution of power to design selection or adequacy must often be tempered when significant uncertainty or test resources constraints exist. In these situations, other measures of merit and alternative analytical approaches become at least as important as power in the development of designs that achieve the desired technical adequacy. In conclusion, one must understand what power is, what factors influence the calculation, and when to leverage alternative measures of merit.

Sample size drives the resources and supports the conclusions of operational test. Power analysis is a common statistical methodology used in planning efforts to justify the number of samples. Power analysis is sensitive to extreme performance (e.g. 0.1% correct responses or 99.999% correct responses) relative to a threshold value, extremes in response variable variability, numbers of factors and levels, system complexity, and a myriad of other design- and system-specific criteria. This discussion will describe considerations (correlation/aliasing, operational significance, thresholds, etc.) and relationships (design, difference to detect, noise, etc.) associated with power. The contribution of power to design selection or adequacy must often be tempered when significant uncertainty or test resources constraints exist. In these situations, other measures of merit and alternative analytical approaches become at least as important as power in the development of designs that achieve the desired technical adequacy. In conclusion, one must understand what power is, what factors influence the calculation, and when to leverage alternative measures of merit.

The Stratospheric Aerosol and Gas Experiment (SAGE III) aboard the International Space Station (ISS) was experiencing a series of anomalies called Single Event Upsets (SEUs). Booz Allen Hamilton was tasked with conducting a statistical analysis to model the incidence of SEUs in the SAGE III equipment aboard the ISS. The team identified factors correlated with SEU incidences, set up a model to track degradation of Sage III, and showed current and past probabilities as a function of the space environment. The space environment of SAGE III was studied to identify possible causes of SEUs. The analysis revealed variables most correlated with the anomalies, including solar wind strength, solar and geomagnetic field behavior, and location/orientation of the ISS, sun, and moon. The data was gathered from a variety of sources including US government agencies, foreign and domestic academic centers, and state-of-the-art simulation algorithms and programs. Logistic regression was used to analyze SEUs and gain preliminary results. The data was divided into small time intervals to approximate independence and allow logistic regression. Due to the rarity of events the initial model results were based on few SEUs. The team set up a Graphical User Interface (GUI) program to automatically analyze new data as it became available to the SAGE III team. A GUI was built to allow the addition of more data over the life of the SAGE III mission. As more SEU incidents occur and are entered into the model, its predictive power will grow significantly. The GUI enables the user to easily rerun the regression analysis and visualize its results to inform operational decision making.

This paper presents the results of two hypervelocity impact failure probabilistic risk assessments for critical wire bundles exposed aboard the Joint Polar Satellite System (JPSS-1) to an increased orbital debris environment at its 824 km, 98.8 deg inclination orbit. The first “generic” approach predicted the number of wires broken by orbital debris ejecta emerging from normal impact with multi-layer insulation (MLI) covering 36-, 18-, and 6-strand wire bundles at a 5 cm standoff using the Smooth Particle Hydrodynamic (SPH) code. This approach also included a mathematical approach for computing the probability that redundant wires were impacted then severed within the bundle. Based in part on the high computed risk of a critical wire bundle failure from the generic approach, an enhanced orbital debris protection design was examined, consisting of betacloth-reinforced MLI suspended at a 5 cm standoff over a seven layer betacloth and Kevlar blanket, draped over the exposed wire bundles. A second SPH-based risk assessment was conducted that also included the beneficial effects from the high (75 degree) obliquity of orbital debris impact and shadowing by other spacecraft components, and resulted in a considerably reduced likelihood of critical wire bundle failure compared to the original baseline design.

Resampling Methods: This tutorial presents widely used resampling methods to include bootstrapping, cross-validation, and permutation tests. Underlying theories will be presented briefly, but the primary focus will be on applications. A new graph-theoretic approach to change detection will be discussed as a specific application of permutation testing. Examples will be demonstrated in R; participants are encouraged to bring their own portable computers to follow along using datasets provided by the instructor.

Several optimization models are described for allocating resources to different testing activities in a system’s reliability growth program. These models assume availability of an underlying reliability growth model for the system, and capture the tradeoffs associated with focusing testing resources at various levels (e.g., system, subsystem, component) and/or how to divide resources within a given level. In order to demonstrate insights generated by solving the model, we apply the optimization models to an example series-parallel system in which reliability growth is assumed to follow the Crow/AMSAA reliability growth model. We then demonstrate how the optimization models can be extended to incorporate uncertainty in Crow/AMSAA parameters.