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    Kimberly F. Sellers

Curriculum Vita [.pdf]

    Contact Information 306 St. Mary's Hall37th and O Streets, NW; Washington, DC 20057Phone: 202-687-8829Fax: 202-687-6067kfs7@georgetown.edu

Research Areas & Select Publications

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  • Don't Count on Poisson: Flexible Alternatives for Modeling Count Data
    While the Poisson distribution is a popular distribution for modeling count data, it is constrained by the "equi-dispersion assumption" (i.e. that the variance equals the mean). Real data oftentimes do not adhere to this restriction in that the data are found to be either over- or under-dispersed. The Conway-Maxwell-Poisson (COM-Poisson) distribution is a flexible, two-parameter alternative that not only generalizes the Poisson distribution, but also captures the Bernoulli and geometric distributions as special case distributions. Various works center around the development of related distributions and statistical methods that are motivated by the COM-Poisson distribution.

    Bivariate Conway-Maxwell-Poisson distribution: Formulation, properties, and inference
    Sellers et al. (2016)

    A flexible zero-inflated model to address data dispersion
    Sellers and Raim (2016)

    Data Dispersion: Now You See It... Now You Don't
    Sellers and Shmueli (2013)

    The COM-Poisson model for count data: a survey of methods and applications (with discussion)
    Sellers et al. (2012) (with discussion)

    A Generalized Statistical Control Chart for Over- or Under-Dispersed Data
    Sellers (2012)
  • usgs
    Proteomic Data Analysis
    Proteomic data analysis describes the collaborative study of protein change via differential expression and modification. Two-dimensional gel electrophoresis methods separate the proteins by pH and molecular weight to better study protein change on a large scale. Statistical methods provide scientists with the ability to quantitatively study these proteins and detect statistically significant protein change through two-dimensional polyacrylamide gel electroresis (2D-PAGE) and two-dimensional difference gel electrophoresis (2D-DIGE) images, where the proteins appear as spots in the images. The number and size of the images are a nice "Big Data" example.

    Statistical Analysis of Gel Electrophoresis Data
    Sellers and Miecznikowski (2012)

    Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets
    Miecznikowski, Sellers, and Eddy (2011)

    A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data
    Miecznikowski et al. (2010)

    Feature Detection Techniques for Preprocessing Proteomic Data
    Sellers and Miecznikowski (2010)

    Lights, Camera, Action! Systematic variation in 2-D difference gel electrophoresis images
    Sellers et al. (2007)
Other Projects

Race matters: analyzing the relationship between colorectal cancer mortality rates and various factors within respective racial groups
Veach et al. (2014)

Many-valued Logic in Vague and Multi-state Stochastic Systems
Sellers and Singpurwalla (2008)
Simulating Discrete Spatially Correlated Poisson Data on a Lattice
Jackson and Sellers (2008)

Kimberly F. Sellers306 St. Mary's Hall37th and O Streets, NW; Washington, DC 20057Phone: 202-687-8829Fax: 202-687-6067kfs7@georgetown.edu