Monica Jackson

Monica Jackson
American University Washington D.C. | AU · Department of Mathematics and Statistics

About

19
Publications
3,392
Reads
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319
Citations
Citations since 2016
3 Research Items
178 Citations
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530
2016201720182019202020212022051015202530

Publications

Publications (19)
Chapter
Blunted: The Effects of Cannabis on Cognition and Motivation The effects of cannabis on cognition and motivation are key considerations both for clinicians and policy makers alike. The medical research on this topic shows some inconsistency in their conclusions but overall suggests that cannabis can indeed lead to reduced cognitive and motivational...
Article
Genetic counseling and testing (GCT) for hereditary breast and ovarian cancers (HBOC) can inform treatment decisions in survivors. Black women at risk of HBOC have lower GCT engagement. There is limited data about Black survivors’ experiences. The goals of this study were to: 1) examine the factors associated with HBOC knowledge and 2) assess the i...
Article
Full-text available
Background: A best practice goal to reduce surgical site infection includes administration of antibiotics in the ideal preoperative window. This article evaluates an office surgical suite antibiotic administration rate and compares it with the timing of a local hospital treating a similar patient population. The hypothesis was that similar or bette...
Article
Full-text available
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% low...
Article
Full-text available
Malaria is a leading cause of mortality worldwide. There is currently conflicting data and interpretation on how variability in climate factors affects the incidence of malaria. This study presents a hierarchical Bayesian modelling framework for the analysis of malaria versus climate factors in West Africa. The hierarchical Bayesian framework takes...
Article
In numerous psychology studies, subjects are asked to perform some task a number of times, T. The effect of the choice of T on the associated inference, however, is usually not assessed. We investigate the appropriate choice of T empirically by using data collected in a study on the relationship between psychopathy and risk-taking in 90 inner city...
Article
Full-text available
The significance of statistical software such as Splus/R, SAS, and SUDDAN in data analysis is the reason most statistics graduate pro-grams require statistical software courses. Therefore, we experimented with the idea of incorporating an industrial certificate program in a Statistical Software course at a private university. While we are not promo...
Article
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 influenc...
Article
Lower access and/or utilization of colorectal screening are thought to be major contributors to the higher proportion of cancers among African Americans and Hispanics that are diagnosed at advanced stages of disease and the poorer outcomes observed among Hispanics and African Americans compared with non-Hispanic whites. We examine rates of initiati...
Article
Full-text available
Investigation of global clustering patterns across regions is very important in spatial data analysis. Moran's I is a widely used spatial statistic for detecting global spatial patterns such as an east-west trend or an unusually large cluster. Here, we intend to improve Moran's I for evaluating global clustering patterns by including the weight fun...
Article
The objective of a spatial data analysis may be to capture the trends apparent in the data set. In spatial data, we do not have the unidirectional flow of time that occurs with time series data. Instead, spatial models are built on nearest neighbors. For discrete data, there are several models including generalized linear mixed models and condition...
Article
Full-text available
The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clusterin...
Article
Full-text available
The small proportion of cancers diagnosed at the local disease stage, resectable at the time diagnosis, and responsive to chemotherapy contribute to poor survival making pancreatic cancer the fourth leading cause of cancer death among Americans. This emphasizes the importance of receiving appropriate palliative care. Racial/ethnic cancer treatment...
Article
Despite its benefit, about 30% of women report that they did not have a recent mammogram. We examine impact of distance, rural-urban residence, and other characteristics on mammography screening rates. We linked data on 33,938 women aged 40-84 years from the 2003 and 2005 California Health Interview Survey with FDA data on the location of mammograp...
Article
Full-text available
Spatial data analyses assess patterns in data and present results as graphs, maps, and model parameter estimates. Several spatial models are avail-able, but there are poor guidelines for choosing among them. We simulate data under two methods for discrete Poisson data on a lattice. A Gibbs sampling algorithm is used to generate conditionally-specif...
Chapter
Permutation tests are a class of nonparametric methods for hypothesis testing. These methods are related to randomization tests.
Article
The ability to detect anomalies such as spatial clustering in data sets plays an important role in spatial data analysis, leading to interest in test statistics identifying patterns exhibiting significant levels of clustering. Toward this end, Tango (1995) proposed a statistic (and its associated distribution under a null hypothesis of no clusterin...
Article
Spatial data analysis uses the notion that data closer together in space are usually more correlated than those farther apart. The data is defined from distributions that are dependent on location. This spatial dependency is modeled through a special neighborhood structure depending on the model used. We present spatial models along with their bene...

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