**MAY 27 ^{th} 2010 – New date!**

**California State University East Bay**

**Hayward, CA**

Refreshments: 4:00 – 4:30 p.m.

Student presentations: 4:30 – 5:30 p.m.

Where: South Science 143

Award Recipients (Abstracts follow)

Marian Farah

Department of Applied Mathematics and Statistics

University of California, Santa Cruz

__Title__

An Application of Semiparametric Bayesian Isotonic Regression to the Study of Radiation Effects in Spaceborne Microelectronics

Karen McKeown

Division of Biostatistics

University of California, Berkeley

__Title__

Misclassification of Current Status Data

Ming Zhong

Department of Statistics

University of California, Davis

__Title__

Segmenting Nonstationary Time Series Via Quantile Autoregressions

Driving directions to CSU EB, Hayward Campus can be found at:

http://www20.csueastbay.edu/about/visitor-information/maps-campus-locations/hayward-campus-map/

Lots E1 and E2 are closest to the Science Buildings. There are parking permit dispensers in many of the parking lots including E2. Parking fee is $7/day. There are a very few metered spots at $1.50/hour.

**ABSTRACTS**

**Student:** Marian Farah, Dept of Applied Mathematics and Statistics

University of California, Santa Cruz

**Title:** An Application of Semiparametric
Bayesian Isotonic Regression to the Study of Radiation Effects in Spaceborne Microelectronics

**Abstract:**
This work is concerned with the vulnerability of spaceborne
microelectronics to single event upset, a change of state caused by high energy charged particles in the solar wind or the
cosmic ray environment striking a sensitive node. To measure the susceptibility
of a semiconductor device to single event upsets, testing is conducted by
exposing it to high-energy heavy ions or protons produced in a particle
accelerator. The number of upsets depends on the linear energy transfer (LET),
the cross-section of interaction, and the fluence.
The interaction cross-section is assumed to be monotonically increasing with
LET. The prediction of the on-orbit upset rate, one of the main scientific
goals of particle accelerator experiments, is made by combining the device
geometry and the cross-section vs. LET curve for the device with the model for
the orbit-specific radiation environment. Standard practice in the device
testing literature is to assume a Weibull or sometimes a Lognormal
parametric form for the cross-section vs. LET curve. The choice of either
parametric model is conventional and has little physical justification. We work
with a Poisson model for the upset counts and propose a semiparametric
isotonic regression method for count responses. Our approach is based on a Dirichlet process prior for the cross-section vs. LET
curve, which allows the data to drive the shape of the cross-section vs. LET
relationship, and can thus result in more accurate predictive inference for the
on-orbit upset rate. We illustrate the practical utility of the proposed
methodology with data obtained from two particle accelerator experiments
corresponding to different experimental scenarios.

**Student:** Karen McKeown

Division of Biostatistics, University of California, Berkeley

**Title:** Misclassification of Current Status Data

**Abstract:** Current status data provides information on the survival
status of individuals at various times rather than standard observation,
possibly right-censored, of failure times. In many current status data applications, ascertainment of an
individualÕs current status is based on a screening test
which may not have perfect sensitivity and specificity. For example, tests for the infection
status of a viral disease like HIV or HPV are designed to detect antibodies and
may be subject to error particularly when a test is performed soon after an
infection.

We describe a simple method for nonparametric estimation of a distribution function based on current status data where observations of current status information are subject to misclassification. Nonparametric maximum likelihood techniques lead to use of a straightforward set of adjustments to the familiar pool-adjacent-violators estimator used when misclassification is assumed absent. The methods consider alternative misclassification models and are extended to regression models for the underlying survival time. The ideas are motivated by and applied to an example on human papilloma virus (HPV) infection status of a sample of women examined in San Francisco.

**Student:** Ming Zhong

Department of Statistics, University of California, Davis

**Title:** Segmenting
Nonstationary Time Series Via Quantile Autoregressions

**Abstract:** Many time series observed in practice
display non-stationarities, especially if data is
collected over long time spans. Non-stationarities
can arise in various ways such as in the trend or in the variance-covariance
structure. Since parameter estimates as well as forecasts can be severely biased
if non-stationarities are not taken into account,
identifying and locating structural breaks has become an important issue in the
analysis of time series.

Since in practice innovation sequences cannot always be modeled conveniently with a Gaussian process, parameter estimates obtained from an application of the Gaussian likelihood may be inefficient. In fact, one commonly observes error distributions with longer tails than that of the Gaussian distribution in many financial time series. To incorporate skewed and possibly heavy-tailed innovations into the model building process, we propose the use of quantile autoregression models with Asymmetric Laplace innovations. As a member of the class of random-coefficient models, quantile autoregression models allow the autoregressive coefficients to take distinct values over different quantiles of the innovation process, and may thus expand the modeling options for non-stationary and asymmetric time series.

In this setting, we try to detect structural breaks with the minimum description length principle selecting the number and locations of break points for non-stationary time series. The estimated segments with piecewise quantile autoregression structures are expected to minimize a convex objective function, and a genetic algorithm is implemented to solve this optimization problem. We will present large sample properties and theoretical justifications for the consistency of this method. Numerical results from simulations and economic data applications show that our method consistently estimates the number and locations of the breaks.

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