@article{giffin_gong_majumder_rappold_reich_yang_2022, title={Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread}, volume={52}, ISSN={["2211-6753"]}, DOI={10.1016/j.spasta.2022.100711}, abstractNote={Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.}, journal={SPATIAL STATISTICS}, author={Giffin, Andrew and Gong, Wenlong and Majumder, Suman and Rappold, Ana G. and Reich, Brian J. and Yang, Shu}, year={2022}, month={Dec} }
 @article{biswas_basu_majumder_niyogi_2021, title={A Weighted Likelihood Approach to Problems in Survival Data}, volume={83}, ISSN={["0976-8394"]}, DOI={10.1007/s13571-019-00214-w}, number={2}, journal={SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS}, author={Biswas, Adhidev and Basu, Ayanendranath and Majumder, Suman and Niyogi, Pratim Guha}, year={2021}, month={Nov}, pages={466–492} }
 @article{dorman_hopperstad_reich_majumder_kennedy_reisig_greene_reay‐jones_collins_bacheler_et al._2021, title={Landscape‐level variation in
            <i>Bt
            crops predict
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              <i>Helicoverpa ze
            
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            (
            <scp>Lepidoptera: Noctuidae
            ) resistance in cotton agroecosystems}, volume={77}, ISSN={1526-498X 1526-4998}, url={http://dx.doi.org/10.1002/ps.6585}, DOI={10.1002/ps.6585}, abstractNote={Abstract}, number={12}, journal={Pest Management Science}, publisher={Wiley}, author={Dorman, Seth J and Hopperstad, Kristen A and Reich, Brian J and Majumder, Suman and Kennedy, George and Reisig, Dominic D and Greene, Jeremy K and Reay‐Jones, Francis PF and Collins, Guy and Bacheler, Jack S and et al.}, year={2021}, month={Aug}, pages={5454–5462} }
 @article{majumder_guan_reich_o'neill_rappold_2021, title={Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting}, volume={26}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-020-00420-4}, abstractNote={Fine particulate matter, PM2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM2.5 air pollution in the US. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.}, number={1}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Majumder, Suman and Guan, Yawen and Reich, Brian J. and O'Neill, Susan and Rappold, Ana G.}, year={2021}, month={Mar}, pages={23–44} }
 @article{majumder_biswas_roy_bhandari_basu_2021, title={Statistical inference based on a new weighted likelihood approach}, volume={84}, ISSN={["1435-926X"]}, DOI={10.1007/s00184-020-00778-y}, abstractNote={We discuss a new weighted likelihood method for robust parametric estimation. The method is motivated by the need for generating a simple estimation strategy which provides a robust solution that is simultaneously fully efficient when the model is correctly specified. This is achieved by appropriately weighting the score function at each observation in the maximum likelihood score equation. The weight function determines the compatibility of each observation with the model in relation to the remaining observations and applies a downweighting only if it is necessary, rather than automatically downweighting a proportion of the observations all the time. This allows the estimators to retain full asymptotic efficiency at the model. We establish all the theoretical properties of the proposed estimators and substantiate the theory developed through simulation and real data examples. Our approach provides an alternative to the weighted likelihood method of Markatou et al. (J Stat Plan Inference 57(2):215–232, 1997; J Am Stat Assoc 93(442):740–750, 1998).}, number={1}, journal={METRIKA}, author={Majumder, Suman and Biswas, Adhidev and Roy, Tania and Bhandari, Subir Kumar and Basu, Ayanendranath}, year={2021}, month={Jan}, pages={97–120} }
 @article{haskett_majumder_kotter-gruhn_gutierrez_2021, title={The role of university students' wellness in links between homelessness, food insecurity, and academic success}, volume={30}, ISSN={["1573-658X"]}, url={http://dx.doi.org/10.1080/10530789.2020.1733815}, DOI={10.1080/10530789.2020.1733815}, abstractNote={ABSTRACT In recent years there has been a steep increase in attention among higher education professionals to the prevalence of unmet basic needs among college students. There is also growing acknowledgement that food insecurity and homelessness among students is associated with incredible distress and a host of negative consequences for students’ success. Understanding the mechanisms that link basic needs insecurity to academic success could identify critical points of intervention to ensure educational equity among students. Our aim was to determine whether university students’ wellness mediated the association between unmet basic needs (i.e. homelessness and food insecurity) and academic success. Of the 1,330 students at a large southeastern U.S. university who completed an online survey (28% response rate), 15.49% were food insecure and 9.47% had been homeless in the past 12 months. Results of multinomial logistic regression analyses indicated that psychological well-being and sleep mediated the link between food security and GPA. Homelessness was not associated with GPA, so mediation was not examined. Implications for addressing homelessness and food insecurity among college students are offered.}, number={1}, journal={JOURNAL OF SOCIAL DISTRESS AND THE HOMELESS}, publisher={Informa UK Limited}, author={Haskett, Mary E. and Majumder, Suman and Kotter-Gruhn, Dana and Gutierrez, Indira}, year={2021}, month={Jan}, pages={59–65} }
 @article{haskett_kotter-gruhn_majumder_2020, title={Prevalence and Correlates of Food Insecurity and Homelessness Among University Students}, volume={61}, ISSN={["1543-3382"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85079158854&partnerID=MN8TOARS}, DOI={10.1353/csd.2020.0007}, abstractNote={Prevalence and Correlates of Food Insecurity and Homelessness Among University Students Mary E. Haskett (bio), Dana Kotter-Grühn (bio), and Suman Majumder (bio) Recently, there has been a steep increase in attention to insecurity among college students for basic needs (e.g., Miles, McBeath, Brockett, & Sorenson, 2017; Morris, Smith, Davis, & Null, 2016); however, published research on student food insecurity and housing insecurity remains sparse. It is critical to understand the prevalence of these challenges because they are associated with mental health functioning, academic success, and graduation rates (see Goldrick-Rab, Richardson, Schneider, Hernandez, & Cady, 2018). Prior studies (Miles et al., 2017; Tsui et al., 2011) point to high co-occurrence of food insecurity and homelessness, so the intersection of these challenges should be explored. To prevent basic needs insecurity and offer appropriate support for those who are affected, we must understand correlates of college food insecurity and student homelessness. Thus, the purpose of this investigation was to answer questions of prevalence, co-occurrence, and correlates of food insecurity and homelessness. A novel feature of this study was our examination of the degree to which cumulative marginalized/ minoritized characteristics predicted food insecurity and homelessness. Cumulative risk models, in which risks that tend to cluster together are equally weighted, are widely used in developmental psychology and have advantages over studies of single predictors of outcomes (Ashworth & Humphrey, 2019; Evans, Li, & Whipple, 2013); to date, such models have not been applied to studies of student food insecurity or student homelessness, even though predictors tend to be correlated (Crutchfield & McGuire, 2019). METHOD The research office at our large public university in the Southeastern US randomly selected 7,000 students; 1,923 completed our online survey (27.5% response, much higher [End Page 109] than most prior studies). They represented the student body in race (4.7% African American; 5.3% biracial or multiracial; 14.9% Asian; 71.3% White; 3.8% other), ethnicity (6.0% Hispanic), and degree sought (71.0% undergraduate, 27.8% graduate, 1.1% associate's). Mean age was 21.74 years (SD = 4.4; 17—61 years). The vast majority, 94.7%, were enrolled full time. The sample slightly overrepresented women, 51.3% compared to the university population with 45.6%; 39.6% were men; and the remainder used a different term when asked about their gender identity (e.g., genderfluid, transMale). Most, 78.8%, considered themselves to be heterosexual, 9.0% did not reply to the item, 5.7% were bisexual, 2.7% were gay/lesbian, and the remainder were asexual, questioning, or used a different term. An e-mail invitation to voluntarily complete the online survey was sent to the 7,000 students in Fall 2017. The survey was a modification of the one developed by the California State University system (Crutchfield & Maguire, 2017) and the Hope Center (Goldrick-Rab, Richardson, & Kinsley, 2017). Our IRB approved the study. To measure food insecurity, the USDA Household/Individual Food Security Survey Module (FSSM) 10-item version was used with a 30-day time frame (Bickel, Nord, Price, Hamilton, & Cook, 2000). Two of the 10 questions asked participants to report the number of days various food situations occurred in the past 30 days. The response format might have been confusing, because almost no participant answered those 2 questions; therefore, we adjusted the coding for the 10-item survey using a conservative approach to defining very low food security. Those who provided affirmative responses to none (0) of the FSSM items were food secure, participants with scores of 1—2 were marginally food secure, those with scores of 3—5 were low food secure, and those with scores of 6—8 were very low food secure. We generated a food insecurity variable by combining students who were low food secure or very low food secure. The U.S. Department of Education (2016) definition of homelessness includes individuals who lack a fixed, regular, and adequate nighttime residence. To assess homelessness using this definition, we asked students to indicate all the places they had stayed in the past 12 months, with 9 items indicative of homelessness (e.g., at a shelter, temporarily staying with friends, outdoor location). The items were recommended by Crutchfield...}, number={1}, journal={JOURNAL OF COLLEGE STUDENT DEVELOPMENT}, author={Haskett, Mary E. and Kotter-Gruhn, Dana and Majumder, Suman}, year={2020}, pages={109–114} }