Daniel R. Jeske

Mean Squared Error of Estimation or Prediction under a General Linear Model

Abstract The problem considered is that of predicting a linear combination of the fixed and random effects of a mixed-effects linear model. More generally, the problem considered is that of predicting an unobservable random variable from a set of observable random variables. The best linear-unbiased predictor depends on parameters which generally are unknown. Various exact or approximate expressions are given for the mean squared error (MSE) of the predictor obtained by replacing the unknown parameters with estimates. Several estimators of the MSE are investigated.

Baseline and stress-induced plasma corticosterone concentrations of mice selectively bred for high voluntary wheel running.

The hypothalamic‐pituitary‐adrenal (HPA) axis is important in regulating energy metabolism and in mediating responses to stressors, including increasing energy availability during physical exercise. In addition, glucocorticoids act directly on the central nervous system and influence behavior, including locomotor activity. To explore potential changes in the HPA axis as animals evolve higher voluntary activity levels, we characterized plasma corticosterone (CORT) concentrations and adrenal mass in four replicate lines of house mice that had been selectively bred for high voluntary wheel running (HR lines) for 34 generations and in four nonselected control (C) lines. We determined CORT concentrations under baseline conditions and immediately after exposure to a novel stressor (40 min of physical restraint) in mice that were housed without access to wheels. Resting daytime CORT concentrations were approximately twice as high in HR as in C mice for both sexes. Physical restraint increased CORT to similar concentrations in HR and C mice; consequently, the proportional response to restraint was smaller in HR than in C animals. Adrenal mass did not significantly differ between HR and C mice. Females had significantly higher baseline and postrestraint CORT concentrations and significantly larger adrenal glands than males in both HR and C lines. Replicate lines showed significant variation in body mass, length, baseline CORT concentrations, and postrestraint CORT concentrations in one or both sexes. Among lines, both body mass and length were significantly negatively correlated with baseline CORT concentrations, suggesting that CORT suppresses growth. Our results suggest that selection for increased locomotor activity has caused correlated changes in the HPA axis, resulting in higher baseline CORT concentrations and, possibly, reduced stress responsiveness and a lower growth rate.

On maximum-likelihood estimation of clock offset

Delay measurements from timing message exchanges between two clocks produce a maximum-likelihood estimator (MLE) of the clock offset when fixed delays in each direction are equal and unknown, and variable delays in each direction have an exponential distribution with an unknown mean. It is shown that the MLE corresponds to a previously proposed estimator of clock offset. The ML interpretation of the estimator provides further insight and motivation for its use.

Some successful approaches to software reliability modeling in industry

Over the past three years, we have been actively engaged in both software reliability growth modeling and architecture-based software reliability modeling for projects at Lucent Technologies. Our goal has been to include software into the overall reliability evaluation of a product design using either or both of these two fundamentally different approaches. During the course of our application efforts to real projects, we have identified practical difficulties with each approach. The application of software reliability growth models, for example, is plagued by widespread use of ad hoc test environments, and the use of architecture-based software reliability models is plagued by a large number of unknown parameters. In this paper, we discuss our methods for overcoming these and other practical difficulties. In particular, we show how calibration factors can be defined and used to adjust for the mismatch between the test and operational profiles of the software. We also present two useful ways to do sensitivity analyses that help alleviate the problem of so many uncertainties in the architecture-based modeling approach. We illustrate our methods with case studies, and offer comments on further work that is required to more satisfactorily bridge the gap between theory and applications in this research area.

A Bayesian method to improve mobile geolocation accuracy

We present a geolocation method for GSM/CDMA/UMTS networks. A cell area is partitioned into a grid, and a sequential Bayesian updating scheme is proposed to identify the grid point within the circular belt, defined by the one-way delay between the mobile and the base station, that is the most likely position of the user. Using GSM as case study, we employ an RF simulation model to study the accuracy of the algorithm compared to existing methods. We show that the accuracy of the Bayesian update method is relatively insensitive to cell size and robust to parameter settings.

Host–microbe relationships in inflammatory bowel disease detected by bacterial and metaproteomic analysis of the mucosal–luminal interface

Background: Host–microbe interactions at the intestinal mucosal–luminal interface (MLI) are critical factors in the biology of inflammatory bowel disease (IBD). Methods: To address this issue, we performed a series of investigations integrating analysis of the bacteria and metaproteome at the MLI of Crohns disease, ulcerative colitis, and healthy human subjects. After quantifying these variables in mucosal specimens from a first sample set, we searched for bacteria exhibiting strong correlations with host proteins. This assessment identified a small subset of bacterial phylotypes possessing this host interaction property. Using a second and independent sample set, we tested the association of disease state with levels of these 14 “host interaction” bacterial phylotypes. Results: A high frequency of these bacteria (35%) significantly differentiated human subjects by disease type. Analysis of the MLI metaproteomes also yielded disease classification with exceptional confidence levels. Examination of the relationships between the bacteria and proteins, using regularized canonical correlation analysis (RCCA), sorted most subjects by disease type, supporting the concept that host–microbe interactions are involved in the biology underlying IBD. Moreover, this correlation analysis identified bacteria and proteins that were undetected by standard means‐based methods such as analysis of variance, and identified associations of specific bacterial phylotypes with particular protein features of the innate immune response, some of which have been documented in model systems. Conclusions: These findings suggest that computational mining of mucosa‐associated bacteria for host interaction provides an unsupervised strategy to uncover networks of bacterial taxa and host processes relevant to normal and disease states. (Inflamm Bowel Dis 2012;)

Adjusting software failure rates that are estimated from test data

Software test environments are often different from field environments. Using test data exclusively to estimate a field failure rate will not usually give a very accurate estimate. In this paper, we extend an empirical calibration methodology for adjusting the failure rate estimate obtained from analysing test data. In addition to scaling the estimated failure rate of a fault, we propose scaling the estimated number of residual faults as well. We also derive likelihood ratio tests to formally determine (from previous releases of the software) if test, and field environments are significantly different. We illustrate our new results with two telecommunications case studies. The combination of the likelihood ratio test, and the calibration methodology offers a practical way to extend the application of software reliability growth models to less formal test environments.

Development of a Synthetic Data Set Generator for Building and Testing Information Discovery Systems

Data mining research has yielded many significant and useful results such as discovering consumer-spending habits, detecting credit card fraud, and identifying anomalous social behavior. Information discovery and analysis systems (IDAS) extract information from multiple sources of data and use data mining methodologies to identify potential significant events and relationships. This research designed and developed a tool called IDAS data and scenario generator (IDSG) to facilitate the creation, testing and training of IDAS. IDSG focuses on building a synthetic data generation engine powerful and flexible enough to generate synthetic data based on complex semantic graphs

On the maximum likelihood estimates for the Goel-Okumoto software reliability model

We show that the maximum likelihood (ML) estimates of the parameters of a well-known software reliability model are not consistent as the observation period for observed software failures extends to infinity. Properties of the ML estimators as the observation period gets long are particularly important when the observation period corresponds to the test interval, since extending the test interval is the most natural way to improve the reliability of the software prior to its release. In addition to providing insight on how to interpret the ML estimators in actual applications, our result also has pedagogical value as an illustration that asymptotic properties of ML estimators cannot be taken for granted.

Reliability Modeling of Hardware and Software Interactions, and Its Applications

We classify system failures into three categories: hardware failures, software failures, and hardware-software interaction failures. We develop a unified reliability model that accounts for failures in all three categories. Hardware, and software failures are accounted for with well-known modeling approaches. In this paper, we propose a modeling methodology using Markov processes to capture hardware-software interaction failures. We illustrate the combined hardware & software modeling approach by applying it to a real telecommunication system