@article{kundu_mazzuchi_sellers_soyer_2025, title={Foreword Special Issue on New Frontiers in Reliability and Risk Analysis: A Tribute to Nozer Darabsha Singpurwalla}, volume={41}, ISSN={["1526-4025"]}, url={https://doi.org/10.1002/asmb.70000}, DOI={10.1002/asmb.70000}, number={1}, journal={APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY}, author={Kundu, Subrata and Mazzuchi, Thomas and Sellers, Kimberly F. and Soyer, Refik}, year={2025}, month={Jan} }
 @article{sellers_booker_2025, title={Probability and Fuzzy Working in Concert-Honoring the Reliability Contributions of Nozer D. Singpurwalla}, volume={41}, ISSN={["1526-4025"]}, url={https://doi.org/10.1002/asmb.2918}, DOI={10.1002/asmb.2918}, abstractNote={ABSTRACT Since Lotfi Zadeh introduced fuzzy logic and fuzzy sets, this theory characterizing the uncertainty of classification has a proven record in fields of computation and engineering. These successful applications, however, have been falsely interpreted as competition or replacement of probability theory by those in many statistical and mathematical communities. Such misconceptions are the result of a lack of understanding about types of uncertainties, and anchored attitudes clinging to the past. Nozer Singpurwalla, among other statisticians, came to the realization that probability and fuzzy set theory can and should work in concert (i.e., not in competition) to accommodate two different types of uncertainty present within a problem or system. The authors had the honor to collaborate with Nozer; those works are featured as successful applications of the probability measure of fuzzy sets in reliability where respective uncertainties of the outcome of events and of classification exist. This paper features those works which embody the use of Bayesian analysis and the subjective interpretation of probability.}, number={1}, journal={APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY}, author={Sellers, Kimberly F. and Booker, Jane M.}, year={2025}, month={Jan} }
 @article{de jesus_sullivan_hopman_martinez_glenn_msopa_milligan_doney_howell_sellers_et al._2023, title={Examining the Role of Quality of Institutionalized Healthcare on Maternal Mortality in the Dominican Republic}, volume={20}, ISSN={1660-4601}, url={http://dx.doi.org/10.3390/ijerph20146413}, DOI={10.3390/ijerph20146413}, abstractNote={The main study objective was to determine the extent to which the quality of institutionalized healthcare, sociodemographic factors of obstetric patients, and institutional factors affect maternal mortality in the Dominican Republic. COM-Poisson distribution and the Pearson correlation coefficient were used to determine the relationship of predictor factors (i.e., hospital bed rate, vaginal birth rate, teenage mother birth rate, single mother birth rate, unemployment rate, infant mortality rate, and sex of child rate) in influencing maternal mortality rate. The factors hospital bed rate, teenage mother birth rate, and unemployment rate were not correlated with maternal mortality. Maternal mortality increased as vaginal birth rates and infant death rates increased whereas it decreased as single mother birth rates increased. Further research to explore alternate response variables, such as maternal near-misses or severe maternal morbidity is warranted. Additionally, the link found between infant death and maternal mortality presents an opportunity for collaboration among medical specialists to develop multi-faceted solutions to combat adverse maternal and infant health outcomes in the DR.}, number={14}, journal={International Journal of Environmental Research and Public Health}, publisher={MDPI AG}, author={De Jesus, Maria and Sullivan, Nora and Hopman, William and Martinez, Alex and Glenn, Paul David, II and Msopa, Saviour and Milligan, Brooke and Doney, Noah and Howell, William and Sellers, Kimberly and et al.}, year={2023}, month={Jul}, pages={6413} }
 @book{meyer_graye_sellers_2024, title={On Non- and Weakly-Informative Priors for the Conway-Maxwell-Poisson (COM-Poisson) Distribution}, url={https://arxiv.org/abs/2311.18053}, DOI={10.48550/arXiv.2311.18053}, abstractNote={Previous Bayesian evaluations of the Conway-Maxwell-Poisson (COM-Poisson) distribution have little discussion of non- and weakly-informative priors for the model. While only considering priors with such limited information restricts potential analyses, these priors serve an important first step in the modeling process and are useful when performing sensitivity analyses. We develop and derive several weakly- and non-informative priors using both the established conjugate prior and Jeffreys' prior. Our evaluation of each prior involves an empirical study under varying dispersion types and sample sizes. In general, we find the weakly informative priors tend to perform better than the non-informative priors. We also consider several data examples for illustration and provide code for implementation of each resulting posterior.}, number={2311.180532311.18053}, author={Meyer, M.J. and Graye, A. and Sellers, K.F.}, year={2024} }
 @misc{sellers_2023, title={The Conway–Maxwell–Poisson Distribution}, ISBN={9781108646437 9781108481106}, url={http://dx.doi.org/10.1017/9781108646437}, DOI={10.1017/9781108646437}, abstractNote={While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.}, publisher={Cambridge University Press}, author={Sellers, Kimberly F.}, year={2023}, month={Feb} }
 @article{morris_sellers_2022, title={A Flexible Mixed Model for Clustered Count Data}, volume={5}, ISSN={2571-905X}, url={http://dx.doi.org/10.3390/stats5010004}, DOI={10.3390/stats5010004}, abstractNote={Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson random intercept model, including choice of the random effect distribution, via simulated and real data examples. We find that COM-Poisson mixed models provide comparable model fit to well-known mixed models for associated special cases of clustered discrete data, and result in improved model fit for data with intermediate levels of over- or underdispersion in the count mechanism. Accordingly, the proposed models are useful for capturing dispersion not consistent with commonly used statistical models, and also serve as a practical diagnostic tool.}, number={1}, journal={Stats}, publisher={MDPI AG}, author={Morris, Darcy Steeg and Sellers, Kimberly F.}, year={2022}, month={Jan}, pages={52–69} }
 @article{gamerman_kolassa_li_natanegara_sellers_talwai_zou_2022, title={A Recap of an ICSA 2021 Panel: Statistics and Data Science Partnerships and Collaborations across Sectors"}, journal={Amstat News}, author={Gamerman, V. and Kolassa, J. and Li, J.Z. and Natanegara, F. and Sellers, K. and Talwai, A. and Zou, K.H.}, year={2022}, month={Feb} }
 @article{gamerman_kolassa_li_natanegara_sellers_talwai_zou_2022, title={A Recap of an ICSA 2021 Panel: Statistics and Data Science Partnerships and Collaborations across Sectors"}, journal={Amstat News}, author={Gamerman, V. and Kolassa, J. and Li, J.Z. and Natanegara, F. and Sellers, K. and Talwai, A. and Zou, K.H.}, year={2022}, month={Jan} }
 @article{sellers_li_wu_balakrishnan_2021, title={A Flexible Multivariate Distribution for Correlated Count Data}, volume={4}, ISSN={2571-905X}, url={http://dx.doi.org/10.3390/stats4020021}, DOI={10.3390/stats4020021}, abstractNote={Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean). Real data are oftentimes over-dispersed and, as such, consider various advancements of a negative binomial structure. While data over-dispersion is more prevalent than under-dispersion in real data, however, examples containing under-dispersed data are surfacing with greater frequency. Thus, there is a demonstrated need for a flexible model that can accommodate both data types. We develop a multivariate Conway–Maxwell–Poisson (MCMP) distribution to serve as a flexible alternative for correlated count data that contain data dispersion. This structure contains the multivariate Poisson, multivariate geometric, and the multivariate Bernoulli distributions as special cases, and serves as a bridge distribution across these three classical models to address other levels of over- or under-dispersion. In this work, we not only derive the distributional form and statistical properties of this model, but we further address parameter estimation, establish informative hypothesis tests to detect statistically significant data dispersion and aid in model parsimony, and illustrate the distribution’s flexibility through several simulated and real-world data examples. These examples demonstrate that the MCMP distribution performs on par with the multivariate negative binomial distribution for over-dispersed data, and proves particularly beneficial in effectively representing under-dispersed data. Thus, the MCMP distribution offers an effective, unifying framework for modeling over- or under-dispersed multivariate correlated count data that do not necessarily adhere to Poisson assumptions.}, number={2}, journal={Stats}, publisher={MDPI AG}, author={Sellers, Kimberly F. and Li, Tong and Wu, Yixuan and Balakrishnan, Narayanaswamy}, year={2021}, month={Apr}, pages={308–326} }
 @article{weems_sellers_li_2021, title={A flexible bivariate distribution for count data expressing data dispersion}, volume={52}, ISSN={0361-0926 1532-415X}, url={http://dx.doi.org/10.1080/03610926.2021.1999474}, DOI={10.1080/03610926.2021.1999474}, abstractNote={The bivariate Poisson distribution is a natural choice for modeling bivariate count data. Its constraining assumption, however, limits model flexibility in some contexts. This work considers the trivariate reduction method to construct a Bivariate Conway-Maxwell-Poisson (BCMP) distribution, which accommodates over- and under-dispersed data. The approach produces marginals that have a flexible form which includes several special case distributions for certain parameters. Moreover, this BCMP model performs well relative to other bivariate models for count data, including BCMP models based on different methods of construction. As a result, the trivariate-reduced BCMP distribution is a flexible alternative for modeling bivariate count data containing data dispersion.}, number={13}, journal={Communications in Statistics - Theory and Methods}, publisher={Informa UK Limited}, author={Weems, Kimberly S. and Sellers, Kimberly F. and Li, Tong}, year={2021}, month={Nov}, pages={4692–4718} }
 @article{arora_rao chaganty_sellers_2021, title={A flexible regression model for zero- and<i>k</i>-inflated count data}, volume={91}, ISSN={0094-9655 1563-5163}, url={http://dx.doi.org/10.1080/00949655.2021.1872077}, DOI={10.1080/00949655.2021.1872077}, abstractNote={Count data with inflated zeros commonly occur in numerous research studies. Accordingly, there is substantive literature regarding zero-inflated Poisson and analogous generalizable count regression models that account for data dispersion via excess zeros. Scenarios exist, however, where another count k>0 tends to be inflated, thus there remains the need to develop a flexible regression model that can accommodate both inflated frequencies and any inherent data dispersion. This work achieves this goal by employing the Conway–Maxwell–Poisson (CMP) distribution. We develop a zero- and k-inflated Conway–Maxwell–Poisson (ZkICMP) distribution and corresponding regression that addresses over- and under-dispersed count data. We further discuss parameter estimation and other diagnostics by analytical and numerical methods, and illustrate superior performance of the ZkICMP regression via real data examples.}, number={9}, journal={Journal of Statistical Computation and Simulation}, publisher={Informa UK Limited}, author={Arora, Monika and Rao Chaganty, N. and Sellers, Kimberly F.}, year={2021}, month={Jan}, pages={1815–1845} }
 @article{sellers_arab_melville_cui_2021, title={A flexible univariate moving average time-series model for dispersed count data}, volume={8}, ISSN={2195-5832}, url={http://dx.doi.org/10.1186/s40488-021-00115-2}, DOI={10.1186/s40488-021-00115-2}, abstractNote={Abstract Al-Osh and Alzaid (1988) consider a Poisson moving average (PMA) model to describe the relation among integer-valued time series data; this model, however, is constrained by the underlying equi-dispersion assumption for count data (i.e., that the variance and the mean equal). This work instead introduces a flexible integer-valued moving average model for count data that contain over- or under-dispersion via the Conway-Maxwell-Poisson (CMP) distribution and related distributions. This first-order sum-of-Conway-Maxwell-Poissons moving average (SCMPMA(1)) model offers a generalizable construct that includes the PMA (among others) as a special case. We highlight the SCMPMA model properties and illustrate its flexibility via simulated data examples.}, number={1}, journal={Journal of Statistical Distributions and Applications}, publisher={Springer Science and Business Media LLC}, author={Sellers, Kimberly F. and Arab, Ali and Melville, Sean and Cui, Fanyu}, year={2021}, month={Feb} }
 @article{sellers_2021, title={The JEDI Corner: How to Help Advocate for Justice, Equity, Diversity, and Inclusion"}, journal={Amstat News}, author={Sellers, K.F.}, year={2021}, month={Oct} }
 @article{morris_raim_sellers_2020, title={A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data}, volume={179}, ISSN={0047-259X}, url={http://dx.doi.org/10.1016/j.jmva.2020.104651}, DOI={10.1016/j.jmva.2020.104651}, journal={Journal of Multivariate Analysis}, publisher={Elsevier BV}, author={Morris, Darcy Steeg and Raim, Andrew M. and Sellers, Kimberly F.}, year={2020}, month={Sep}, pages={104651} }
 @article{sellers_premeaux_2020, title={Conway–Maxwell–Poisson</scp>regression models for dispersed count data}, volume={13}, ISSN={1939-5108 1939-0068}, url={http://dx.doi.org/10.1002/wics.1533}, DOI={10.1002/wics.1533}, abstractNote={Abstract While Poisson regression serves as a standard tool for modeling the association between a count response variable and explanatory variables, it is well‐documented that this approach is limited by the Poisson model's assumption of data equi‐dispersion. The Conway–Maxwell–Poisson (COM‐Poisson) distribution has demonstrated itself as a viable alternative for real count data that express data over‐ or under‐dispersion, and thus the COM‐Poisson regression can flexibly model associations involving a discrete count response variable and covariates. This work overviews the ongoing developmental knowledge and advancement of COM‐Poisson regression, introducing the reader to the underlying model (and its considered reparametrizations) and related regression constructs, including zero‐inflated models, and longitudinal studies. This manuscript further introduces readers to associated computing tools available to perform COM‐Poisson and related regressions. This article is categorized under: Statistical Models > Linear Models Statistical Models > Generalized Linear Models}, number={6}, journal={WIREs Computational Statistics}, publisher={Wiley}, author={Sellers, Kimberly F. and Premeaux, Bailey}, year={2020}, month={Sep} }
 @article{sellers_peng_arab_2020, title={A Flexible Univariate Autoregressive Time‐Series Model for Dispersed Count Data}, url={https://doi.org/10.1111/jtsa.12516}, DOI={10.1111/jtsa.12516}, abstractNote={Integer‐valued time series data have an ever‐increasing presence in various applications (e.g., the number of purchases made in response to a marketing strategy, or the number of employees at a business) and need to be analyzed properly. While a Poisson autoregressive (PAR) model would seem like a natural choice to model such data, it is constrained by the equi‐dispersion assumption (i.e., that the variance and the mean equal). Hence, data that are over‐ or under‐dispersed (i.e., have the variance greater or less than the mean respectively) are improperly modeled, resulting in biased estimates and inaccurate forecasts. This work instead develops a flexible integer‐valued autoregressive model for count data that contain over‐ or under‐dispersion. Using the Conway–Maxwell–Poisson (CMP) distribution and related distributions as motivation, we develop a first‐order sum‐of‐CMP's autoregressive (SCMPAR(1)) model that will instead offer a generalizable construct that captures the PAR, and versions of what we refer to as a negative binomial AR model, and binomial AR model respectively as special cases, and serve as an overarching representation connecting these three special cases through the dispersion parameter. We illustrate the SCMPAR model's flexibility and ability to effectively model count time series data containing data dispersion through simulated and real data examples.}, journal={Journal of Time Series Analysis}, author={Sellers, Kimberly F. and Peng, Stephen J. and Arab, Ali}, year={2020}, month={May} }
 @article{sellers_young_2019, title={Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression}, volume={89}, url={https://doi.org/10.1080/00949655.2019.1590580}, DOI={10.1080/00949655.2019.1590580}, abstractNote={While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks.}, number={9}, journal={Journal of Statistical Computation and Simulation}, publisher={Informa UK Limited}, author={Sellers, Kimberly F. and Young, Derek S.}, year={2019}, month={Jun}, pages={1649–1673} }
 @article{sellers_swift_weems_2017, title={A flexible distribution class for count data}, volume={4}, url={https://doi.org/10.1186/s40488-017-0077-0}, DOI={10.1186/s40488-017-0077-0}, abstractNote={The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-parameter generalization of the Poisson distribution that can accommodate data over- or under-dispersion. This work further generalizes the ideas of the CMP distribution by considering sums of CMP random variables to establish a flexible class of distributions that encompasses the Poisson, negative binomial, and binomial distributions as special cases. This sum-of-Conway-Maxwell-Poissons (sCMP) class captures the CMP and its special cases, as well as the classical negative binomial and binomial distributions. Through simulated and real data examples, we demonstrate this model's flexibility, encompassing several classical distributions as well as other count data distributions containing significant data dispersion.}, number={1}, journal={Journal of Statistical Distributions and Applications}, publisher={Springer Science and Business Media LLC}, author={Sellers, Kimberly F. and Swift, Andrew W. and Weems, Kimberly S.}, year={2017}, month={Dec} }
 @article{sellers_benn_garcia_kellam_2017, title={Addressing Implicit Bias Among Women Statisticians and Data Scientists}, volume={30}, ISSN={0933-2480 1867-2280}, url={http://dx.doi.org/10.1080/09332480.2017.1320477}, DOI={10.1080/09332480.2017.1320477}, abstractNote={The Kirwan Institute for the Study of Race and Ethnicity at the Ohio State University defines implicit bias as:The attitudes or stereotypes that affect our understanding, actions, and decisions in ...}, number={2}, journal={CHANCE}, publisher={Informa UK Limited}, author={Sellers, Kimberly F. and Benn, Emma K. T. and Garcia, Maria and Kellam, Meghan}, year={2017}, month={Apr}, pages={38–41} }
 @article{sellers_morris_2017, title={Underdispersion models: Models that are “under the radar”}, volume={46}, ISSN={0361-0926 1532-415X}, url={http://dx.doi.org/10.1080/03610926.2017.1291976}, DOI={10.1080/03610926.2017.1291976}, abstractNote={The Poisson distribution is a benchmark for modeling count data. Its equidispersion constraint, however, does not accurately represent real data. Most real datasets express overdispersion; hence attention in the statistics community focuses on associated issues. More examples are surfacing, however, that display underdispersion, warranting the need to highlight this phenomenon and bring more attention to those models that can better describe such data structures. This work addresses various sources of data underdispersion and surveys several distributions that can model underdispersed data, comparing their performance on applied datasets.}, number={24}, journal={Communications in Statistics - Theory and Methods}, publisher={Informa UK Limited}, author={Sellers, Kimberly F. and Morris, Darcy S.}, year={2017}, month={Feb}, pages={12075–12086} }
 @article{sellers_raim_2016, title={A flexible zero-inflated model to address data dispersion}, volume={99}, url={https://doi.org/10.1016/j.csda.2016.01.007}, DOI={10.1016/j.csda.2016.01.007}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={Sellers, Kimberly F. and Raim, Andrew}, year={2016}, month={Jul}, pages={68–80} }
 @article{sellers_morris_balakrishnan_2016, title={Bivariate Conway–Maxwell–Poisson distribution: Formulation, properties, and inference}, volume={150}, url={https://doi.org/10.1016/j.jmva.2016.04.007}, DOI={10.1016/j.jmva.2016.04.007}, journal={Journal of Multivariate Analysis}, publisher={Elsevier BV}, author={Sellers, Kimberly F. and Morris, Darcy Steeg and Balakrishnan, Narayanaswamy}, year={2016}, month={Sep}, pages={152–168} }
 @article{veach_xique_johnson_lyle_almodovar_sellers_moore_jackson_2014, title={Race Matters: Analyzing the Relationship between Colorectal Cancer Mortality Rates and Various Factors within Respective Racial Groups}, volume={2}, ISSN={2296-2565}, url={http://dx.doi.org/10.3389/fpubh.2014.00239}, DOI={10.3389/fpubh.2014.00239}, abstractNote={Colorectal cancer (CRC) is the third leading cause of mortality due to cancer (with over 50,000 deaths annually), representing 9% of all cancer deaths in the United States (1). In particular, the African-American CRC mortality rate is among the highest reported for any race/ethnic group. Meanwhile, the CRC mortality rate for Hispanics is 15-19% lower than that for non-Hispanic Caucasians (2). While factors such as obesity, age, and socio-economic status are known to associate with CRC mortality, do these and other potential factors correlate with CRC death in the same way across races? This research linked CRC mortality data obtained from the National Cancer Institute with data from the United States Census Bureau, the Centers for Disease Control and Prevention, and the National Solar Radiation Database to examine geographic and racial/ethnic differences, and develop a spatial regression model that adjusted for several factors that may attribute to health disparities among ethnic/racial groups. This analysis showed that sunlight, obesity, and socio-economic status were significant predictors of CRC mortality. The study is significant because it not only verifies known factors associated with the risk of CRC death but, more importantly, demonstrates how these factors vary within different racial groups. Accordingly, education on reducing risk factors for CRC should be directed at specific racial groups above and beyond creating a generalized education plan.}, journal={Frontiers in Public Health}, publisher={Frontiers Media SA}, author={Veach, Emma and Xique, Ismael and Johnson, Jada and Lyle, Jessica and Almodovar, Israel and Sellers, Kimberly F. and Moore, Calandra T. and Jackson, Monica C.}, year={2014}, month={Nov} }
 @article{miecznikowski_sellers_eddy_2012, title={Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets}, volume={02}, ISSN={2161-122X 2161-1238}, url={http://dx.doi.org/10.4236/jst.2012.21005}, DOI={10.4236/jst.2012.21005}, abstractNote={Feature detection in chemical sensors images falls under the general topic of mathematical morphology, where the goal is to detect “image objects” e.g. peaks or spots in an image. Here, we propose a novel method for object detection that can be generalized for a k-dimensional object obtained from an analogous higher-dimensional technology source. Our method is based on the smoothing decomposition, Data = Smooth + Rough, where the “rough” (i.e. residual) object from a k-dimensional cross-shaped smoother provides information for object detection. We demonstrate properties of this procedure with chemical sensor applications from various biological fields, including genetic and proteomic data analysis.}, number={01}, journal={Journal of Sensor Technology}, publisher={Scientific Research Publishing, Inc.}, author={Miecznikowski, Jeffrey C. and Sellers, Kimberly F. and Eddy, William F.}, year={2012}, pages={23–37} }
 @article{sellers_borle_shmueli_2012, title={Rejoinder: The COM‐Poisson Model for count data: A survey of methods and applications}, volume={28}, ISSN={1524-1904 1526-4025}, url={http://dx.doi.org/10.1002/asmb.1923}, DOI={10.1002/asmb.1923}, number={2}, journal={Applied Stochastic Models in Business and Industry}, publisher={Wiley}, author={Sellers, Kimberly F. and Borle, Sharad and Shmueli, Galit}, year={2012}, month={Mar}, pages={128–129} }
 @article{sellers_borle_shmueli_2011, title={The COM‐Poisson model for count data: a survey of methods and applications}, volume={28}, ISSN={1524-1904 1526-4025}, url={http://dx.doi.org/10.1002/asmb.918}, DOI={10.1002/asmb.918}, abstractNote={The Poisson distribution is a popular distribution for modeling count data, yet it is constrained by its equidispersion assumption, making it less than ideal for modeling real data that often exhibit over‐dispersion or under‐dispersion. The COM‐Poisson distribution is a two‐parameter generalization of the Poisson distribution that allows for a wide range of over‐dispersion and under‐dispersion. It not only generalizes the Poisson distribution but also contains the Bernoulli and geometric distributions as special cases. This distribution's flexibility and special properties have prompted a fast growth of methodological and applied research in various fields. This paper surveys the different COM‐Poisson models that have been published thus far and their applications in areas including marketing, transportation, and biology, among others. Copyright © 2011 John Wiley & Sons, Ltd.}, number={2}, journal={Applied Stochastic Models in Business and Industry}, publisher={Wiley}, author={Sellers, Kimberly F. and Borle, Sharad and Shmueli, Galit}, year={2011}, month={Sep}, pages={104–116} }
 @article{jackson_trotman_stephens_sellers_2011, title={The effect of latency variables on repeated measures inference applied to the measurement of risk-taking as a function of psychopathy}, volume={47}, ISSN={0033-5177 1573-7845}, url={http://dx.doi.org/10.1007/s11135-011-9475-4}, DOI={10.1007/s11135-011-9475-4}, number={1}, journal={Quality & Quantity}, publisher={Springer Science and Business Media LLC}, author={Jackson, Monica C. and Trotman, Adria and Stephens, Melissa and Sellers, Kimberly F.}, year={2011}, month={Mar}, pages={15–26} }
 @article{miecznikowski_damodaran_sellers_rabin_2010, title={A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data}, volume={8}, ISSN={1477-5956}, url={http://dx.doi.org/10.1186/1477-5956-8-66}, DOI={10.1186/1477-5956-8-66}, abstractNote={Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots. This work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error. In summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.}, number={1}, journal={Proteome Science}, publisher={Springer Science and Business Media LLC}, author={Miecznikowski, Jeffrey C and Damodaran, Senthilkumar and Sellers, Kimberly F and Rabin, Richard A}, year={2010}, pages={66} }
 @article{sellers_miecznikowski_2010, title={Feature Detection Techniques for Preprocessing Proteomic Data}, volume={2010}, ISSN={1687-4188 1687-4196}, url={http://dx.doi.org/10.1155/2010/896718}, DOI={10.1155/2010/896718}, abstractNote={Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.}, number={1}, journal={International Journal of Biomedical Imaging}, publisher={Wiley}, author={Sellers, Kimberly F. and Miecznikowski, Jeffrey C.}, editor={Zhao, ShanEditor}, year={2010}, month={Jan}, pages={1–9} }
 @article{jackson_johansen_furlong_colson_sellers_2010, title={Modelling the effect of climate change on prevalence of malaria in western Africa}, volume={64}, ISSN={0039-0402}, url={http://dx.doi.org/10.1111/j.1467-9574.2010.00453.x}, DOI={10.1111/j.1467-9574.2010.00453.x}, abstractNote={Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector-borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmission of malaria in several ways, affecting all stages of the disease's development. Using various weather-related factors that influence climate change, this study utilizes statistical analysis to determine the effect of climate change on reported malaria rates in an African region with endemic malaria. It examines the relationship between malaria prevalence and climate in western Africa using spatial regression modeling and tests for correlation. Our analysis suggests that minimal correlation exists between reported malaria rates and climate in western Africa. This analysis further contradicts the prevailing theory that climate and malaria prevalence are closely linked and negates the idea that climate change will increase malaria transmission in this region.}, number={4}, journal={Statistica Neerlandica}, publisher={Wiley}, author={Jackson, Monica C. and Johansen, Laura and Furlong, Cathy and Colson, Abigail and Sellers, Kimberly F.}, year={2010}, month={Jun}, pages={388–400} }
 @book{paez_zangerl_sellers_acland_aguirre_2008, title={Characterization of Gene Expression Profiles of Normal Canine Retina and Brain Using a Retinal cDNA Microarray}, ISBN={9780387749020 9780387749044}, ISSN={0065-2598}, url={http://dx.doi.org/10.1007/978-0-387-74904-4_20}, DOI={10.1007/978-0-387-74904-4_20}, journal={Advances in Experimental Medicine and Biology}, publisher={Springer New York}, author={Paez, Gerardo L. and Zangerl, Barbara and Sellers, Kimberly and Acland, Gregory M. and Aguirre, Gustavo D.}, year={2008}, pages={179–184} }