Scholarly Works, Statistics

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Local knowledge reconstructs historical resource use
Castello, Leandro; Martins, Eduardo G.; Sorice, Michael G.; Smith, Eric P.; Almedia, Morgana; Bastos, Gastao C.C.; Gardoso, Luis G.; Clauzet, Mariana; Dopona, Alisson P.; Ferreira, Beatrice; Haimovic, Manuel; Jorge, Marcelo; Mendonça, Jocemar; Ávila-da- Silva, Antonio O.; Roman, Ana P.O.; Ramires, Milena; de Miranda, Laura V.; Lopes, Priscila F.M. (Wiley, 2024-03-07)
Information on natural resource exploitation is vital for conservation but scarce in developing nations, which encompass most of the world and often lack the capacity to produce it. A growing approach to generate information about resource use in the context of developing nations relies on surveys of resource users about their recollections (recall) of past harvests. However, the reliability of harvest recalls remains unclear. Here, we show that harvest recalls can be as accurate to data collected by standardized protocols, despite that recalls are variable and affected by the age of the recollecting person and the length of time elapsed since the event. Samples of harvest recalls permit relatively reliable reconstruction of harvests for up to 39 years in the past. Harvest recalls therefore have strong potential to inform data-poor resource systems and curb shifting baselines around the world at a fraction of the cost of conventional approaches.
Learning Common Knowledge Networks Via Exponential Random Graph Models
Liu, Xueying; Hu, Zhihao; Deng, Xinwei; Kuhlman, Chris (ACM, 2023-11-06)
Common knowledge (CK) is a phenomenon where each individual within a group knows the same information and everyone knows that everyone knows the information, infinitely recursively. CK spreads information as a contagion through social networks in ways different from other models like susceptibleinfectious- recovered (SIR) model. In a model of CK on Facebook, the biclique serves as the characterizing graph substructure for generating CK, as all nodes within a biclique share CK through their walls. To understand the effects of network structure on CKbased contagion, it is necessary to control the numbers and sizes of bicliques in networks. Thus, learning how to generate these CK networks (CKNs) is important. Consequently, we develop an exponential random graph model (ERGM) that constructs networks while controlling for bicliques. Our method offers powerful prediction and inference, reduces computational costs significantly, and has proven its merit in contagion dynamics through numerical experiments.
Antibiotic exposure is associated with decreased risk of psychiatric disorders
Kerman, Ilan A.; Glover, Matthew E.; Lin, Yezhe; West, Jennifer L.; Hanlon, Alexandra L.; Kablinger, Anita S.; Clinton, Sarah M. (Frontiers, 2024-01-08)
Objective: This study sought to investigate the relationship between antibiotic exposure and subsequent risk of psychiatric disorders. Methods: This retrospective cohort study used a national database of 69 million patients from 54 large healthcare organizations. We identified a cohort of 20,214 (42.5% male; 57.9 ± 15.1 years old [mean ± SD]) adults without prior neuropsychiatric diagnoses who received antibiotics during hospitalization. Matched controls included 41,555 (39.6% male; 57.3 ± 15.5 years old) hospitalized adults without antibiotic exposure. The two cohorts were balanced for potential confounders, including demographics and variables with potential to affect: the microbiome, mental health, medical comorbidity, and overall health status. Data were stratified by age and by sex, and outcome measures were assessed starting 6 months after hospital discharge. Results: Antibiotic exposure was consistently associated with a significant decrease in the risk of novel mood disorders and anxiety and stressor-related disorders in: men (mood (OR 0.84, 95% CI 0.77, 0.91), anxiety (OR 0.88, 95% CI 0.82, 0.95), women (mood (OR 0.94, 95% CI 0.89,1.00), anxiety (OR 0.93, 95% CI 0.88, 0.98), those who are 26–49 years old (mood (OR 0.87, 95% CI 0.80, 0.94), anxiety (OR 0.90, 95% CI 0.84, 0.97)), and in those ≥50 years old (mood (OR 0.91, 95% CI 0.86, 0.97), anxiety (OR 0.92, 95% CI 0.87, 0.97). Risk of intentional harm and suicidality was decreased in men (OR 0.73, 95% CI 0.55, 0.98) and in those ≥50 years old (OR 0.67, 95% CI 0.49, 0.92). Risk of psychotic disorders was also decreased in subjects ≥50 years old (OR 0.83, 95 CI: 0.69, 0.99). Conclusion: Use of antibiotics in the inpatient setting is associated with protective effects against multiple psychiatric outcomes in an age- and sex-dependent manner.
Effects of establishment fertilization on Landsat-assessed leaf area development of loblolly pine stands
House, Matthew N.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Rachel L.; Carter, David R.; Van Mullekom, Jennifer H.; Rakestraw, Jim; Schroeder, Todd A. (Elsevier, 2024-03-15)
Loblolly pine (Pinus taeda L.) plantations in the southeastern United States are among the world's most intensively managed forest plantations. Under intensive management, a common practice is fertilizing at establishment. The objective of this study was to investigate the effect of establishment fertilization on leaf area development of loblolly pine plantation stands (n = 3997) over 16 years compared to stands that did not receive nutrient additions at planting. Leaf area index (LAI) is a meaningful biophysical indicator of vigor and an important functional and structural element of a planted stand. The study area was stratified by plant hardiness zone to account for climatic differences and soil type (texture and drainage class), using the Cooperative Research in Forest Fertilization (CRIFF) groupings. LAI was estimated from Landsat imagery to create trajectories of mean stand LAI over 16 years. Establishment fertilization, on average, (1) increased stand LAI beginning at year two, with a peak at years six and seven, and (2) decreased the time required for a stand to reach a winter LAI of 1.5 by almost two years. Fertilization responses varied by climate zone and soil drainage class, where the warmest zones benefited the most, particularly in poorly drained soils. Past year 10, the differences in LAI between fertilized and unfertilized stands were not practically important. Using Landsat data in a cloud-computing environment, we demonstrated the benefits of establishment fertilization to stand LAI development using a large sample over the native range of loblolly pine.
Age-dependent ventilator-induced lung injury: Mathematical modeling, experimental data, and statistical analysis
Hay, Quintessa; Grubb, Christopher; Minucci, Sarah; Valentine, Michael S.; Van Mullekom, Jennifer H.; Heise, Rebecca L.; Reynolds, Angela M. (PLOS, 2024-02-22)
A variety of pulmonary insults can prompt the need for life-saving mechanical ventilation; however, misuse, prolonged use, or an excessive inflammatory response, can result in ventilator-induced lung injury. Past research has observed an increased instance of respiratory distress in older patients and differences in the inflammatory response. To address this, we performed high pressure ventilation on young (2-3 months) and old (20-25 months) mice for 2 hours and collected data for macrophage phenotypes and lung tissue integrity. Large differences in macrophage activation at baseline and airspace enlargement after ventilation were observed in the old mice. The experimental data was used to determine plausible trajectories for a mathematical model of the inflammatory response to lung injury which includes variables for the innate inflammatory cells and mediators, epithelial cells in varying states, and repair mediators. Classification methods were used to identify influential parameters separating the parameter sets associated with the young or old data and separating the response to ventilation, which was measured by changes in the epithelial state variables. Classification methods ranked parameters involved in repair and damage to the epithelial cells and those associated with classically activated macrophages to be influential. Sensitivity results were used to determine candidate in-silico interventions and these interventions were most impact for transients associated with the old data, specifically those with poorer lung health prior to ventilation. Model results identified dynamics involved in M1 macrophages as a focus for further research, potentially driving the age-dependent differences in all macrophage phenotypes. The model also supported the pro-inflammatory response as a potential indicator of age-dependent differences in response to ventilation. This mathematical model can serve as a baseline model for incorporating other pulmonary injuries.
Nonparametric Bayesian Functional Clustering with Applications to Racial Disparities in Breast Cancer
Gao, Wenyu; Kim, Inyoung; Nam, Wonil; Ren, Xiang; Zhou, Wei; Agah, Masoud (Wiley, 2024-01)
As we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM-VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.
Public and industry knowledge and perceptions of US swine industry castration practices
Neary, Jessica M.; Guthrie, Adeline P.; Jacobs, Leonie (Cambridge University Press, 2023-12-22)
In the United States (US), surgical castration of male piglets is typically performed without any form of analgesia. This may raise concerns with the public; however, there is no information regarding current public knowledge on swine industry practices in the US. In this study we gained insight into public knowledge and perception on castration with and without analgesia in comparison to knowledge of industry stakeholders on these same topics. Through an online survey, 119 respondents were asked four questions about castration in the US swine industry. Industry respondents were contacted via social media and networking. The general public sample was accessed through Mechanical Turk. Survey responses were categorised by experience (industry vs public). Industry respondents were more aware of practices compared to the general public. Most public respondents were unaware of castration practices and the lack of analgesia use. Respondents from rural communities were more aware of castration practices than (sub)urban communities and more aware of analgesia use than those from urban communities. Those with more education had greater awareness of castration practices (occurrence not frequency). Based on the results from this first US sample, knowledge on industry practices was especially lacking for public respondents, but also for a minority of industry respondents, indicating opportunities for education and further research on the topic.
Merging Two Cultures: Deep and Statistical Learning
Bhadra, Anindya; Datta, Jyotishka; Polson, Nick; Sokolov, Vadim; Xu, Jianeng (2021-10-21)
Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for structured tabular data. Deep learning can be viewed through the lens of generalized linear models (GLMs) with composite link functions. Sufficient dimensionality reduction (SDR) and sparsity performs nonlinear feature engineering. We show that prediction, interpolation and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model. Thus a general framework for machine learning arises that first generates nonlinear features (a.k.a factors) via sparse regularization and stochastic gradient optimisation and second uses a stochastic output layer for predictive uncertainty. Rather than using shallow additive architectures as in many statistical models, deep learning uses layers of semi affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (a.k.a features) to which predictive statistical methods can be applied. Thus we achieve the best of both worlds: scalability and fast predictive rule construction together with uncertainty quantification. Sparse regularisation with un-supervised or supervised learning finds the features. We clarify the duality between shallow and wide models such as PCA, PPR, RRR and deep but skinny architectures such as autoencoders, MLPs, CNN, and LSTM. The connection with data transformations is of practical importance for finding good network architectures. By incorporating probabilistic components at the output level we allow for predictive uncertainty. For interpolation we use deep Gaussian process and ReLU trees for classification. We provide applications to regression, classification and interpolation. Finally, we conclude with directions for future research.
Nonparametric Bayes multiresolution testing for high-dimensional rare events
Datta, Jyotishka; Banerjee, Sayantan; Dunson, David B. (2024-01)
In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting data on rare variants, the focus is on assessing whether and how the variant profile changes with the disease subtype. Motivated by this application, we develop multiresolution nonparametric Bayes tests for differential mutation rates across groups. The multiresolution approach yields fast and accurate detection of spatial clusters of rare variants, and our nonparametric Bayes framework provides great flexibility for modeling the intensities of rare variants. Some theoretical properties are also assessed, including weak consistency of our Dirichlet Process-Poisson-Gamma mixture over multiple resolutions. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to detect rare variants related to common variable immunodeficiency from whole exome sequencing data on 215 patients and over 60,027 control subjects.
Ultra-Fast Approximate Inference Using Variational Functional Mixed Models
Huo, Shuning; Morris, Jeffrey S.; Zhu, Hongxiao (Taylor & Francis, 2023-04-03)
While Bayesian functional mixed models have been shown effective to model functional data with various complex structures, their application to extremely high-dimensional data is limited due to computational challenges involved in posterior sampling. We introduce a new computational framework that enables ultra-fast approximate inference for high-dimensional data in functional form. This framework adopts parsimonious basis to represent functional observations, which facilitates efficient compression and parallel computing in basis space. Instead of performing expensive Markov chain Monte Carlo sampling, we approximate the posterior distribution using variational Bayes and adopt a fast iterative algorithm to estimate parameters of the approximate distribution. Our approach facilitates a fast multiple testing procedure in basis space, which can be used to identify significant local regions that reflect differences across groups of samples. We perform two simulation studies to assess the performance of approximate inference, and demonstrate applications of the proposed approach by using a proteomic mass spectrometry dataset and a brain imaging dataset. Supplementary materials for this article are available online.
COVID-19 related messaging, beliefs, information sources, and mitigation behaviors in Virginia: A cross-sectional survey in the summer of 2020
Silverman, Rachel; Short, Danielle; Wenzel, Sophie; Friesen, Mary Ann; Cook, Natalie (PeerJ, 2024-01-08)
Background. Conflicting messages and misleading information related to the coronavirus (COVID-19) pandemic (SARS-CoV-2) have hindered mitigation efforts. It is important that trust in evidence-based public health information be maintained to effectively continue pandemic mitigation strategies. Officials, researchers, and the public can benefit from exploring how people receive information they believe and trust, and how their beliefs influence their behaviors. Methods. To gain insight and inform effective evidence-based public health messaging, we distributed an anonymous online cross-sectional survey from May to July, 2020 to Virginia residents, 18 years of age or older. Participants were surveyed about their perceptions of COVID-19, risk mitigation behaviors, messages and events they felt influenced their beliefs and behaviors, and where they obtained information that they trust. The survey also collected socio-demographic information, including gender, age, race, ethnicity, level of education, income, employment status, occupation, changes in employment due to the pandemic, political affiliation, sexual orientation, and zip code. Analyses included specific focus on the most effective behavioral measures: wearing a face mask and distancing in public. Results. Among 3,488 respondents, systematic differences were observed in information sources that people trust, events that impacted beliefs and behaviors, and how behaviors changed by socio-demographics, political identity, and geography within Virginia. Characteristics significantly associated (p < 0.025) with not wearing a mask in public included identifying as non-Hispanic white, male, Republican political identity, younger age, lower income, not trusting national science and health organizations, believing one or more non-evidence-based messages, and residing in Southwest Virginia in logistic regression. Similar, lesser in magnitude correlations, were observed for distancing in public. Conclusions. This study describes how information sources considered trustworthy vary across different populations and identities, and how these differentially correspond to beliefs and behaviors. This study can assist decision makers and the public to improve and effectively target public health messaging related to the ongoing COVID19 pandemic and future public health challenges in Virginia and similar jurisdictions.
Parameterizing Lognormal state space models using moment matching
Smith, John W.; Thomas, R. Quinn; Johnson, Leah R. (Springer, 2023-09)
In ecology, it is common for processes to be bounded based on physical constraints of the system. One common example is the positivity constraint, which applies to phenomena such as duration times, population sizes, and total stock of a system’s commodity. In this paper, we propose a novel method for parameterizing Lognormal state space models using an approach based on moment matching. Our method enforces the positivity constraint, allows for arbitrary mean evolution and variance structure, and has a closed-form Markov transition density which allows for more flexibility in fitting techniques. We discuss two existing Lognormal state space models and examine how they differ from the method presented here. We use 180 synthetic datasets to compare the forecasting performance under model misspecification and assess the estimation of precision parameters between our method and existing methods. We find that our models perform well under misspecification, and that fixing the observation variance both helps to improve estimation of the process variance and improves forecast performance. To test our method on a difficult problem, we compare the predictive performance of two Lognormal state space models in predicting the Leaf Area Index over a 151 day horizon by using a process-based ecosystem model to describe the temporal dynamics. We find that our moment matching model performs better than its competitor, and is better suited for intermediate predictive horizons. Overall, our study helps to inform practitioners about the importance of incorporating sensible dynamics when using models of complex systems to predict out-of-sample.
Semiparametric Integrated and Additive Spatio-Temporal Single-Index Models
Mahmoud, Hamdy F. F.; Kim, Inyoung (MDPI, 2023-11-13)
In this paper, we introduce two semiparametric single-index models for spatially and temporally correlated data. Our first model has spatially and temporally correlated random effects that are additive to the nonparametric function, which we refer to as the “semiparametric spatio-temporal single-index model (ST-SIM)”. The second model integrates the spatially correlated effects into the nonparametric function, and the time random effects are additive to the single-index function. We refer to our second model as the “semiparametric integrated spatio-temporal single-index model (IST-SIM)”. Two algorithms based on a Markov chain expectation maximization are introduced to simultaneously estimate the model parameters, spatial effects, and time effects of the two models. We compare the performance of our models using several simulation studies. The proposed models are then applied to mortality data from six major cities in South Korea. Our results suggest that IST-SIM (1) is more flexible than ST-SIM because the former can estimate various nonparametric functions for different locations, while ST-SIM enforces the mortality functions having the same shape over locations; (2) provides better estimation and prediction, and (3) does not need restrictions for the single-index coefficients to fix the identifiability problem.
National assessment of obstetrics and gynecology and family medicine residents' experiences with CenteringPregnancy group prenatal care
Place, Jean Marie; Van De Griend, Kristin; Zhang, Mengxi; Schreiner, Melanie; Munroe, Tanya; Crockett, Amy; Ji, Wenyan; Hanlon, Alexandra L. (2023-11-21)
Objective To examine family medicine (FM) and obstetrician-gynecologist (OB/GYN) residents’ experiences with CenteringPregnancy (CP) group prenatal care (GPNC) as a correlate to perceived likelihood of implementing CP in future practice, as well as knowledge, level of support, and perceived barriers to implementation. Methods We conducted a repeated cross-sectional study annually from 2017 to 2019 with FM and OB/GYN residents from residency programs in the United States licensed to operate CP. We applied adjusted logistic regression models to identify predictors of intentions to engage with CP in future practice. Results Of 212 FM and 176 OB/GYN residents included in analysis, 67.01% of respondents intended to participate as a facilitator in CP in future practice and 51.80% of respondents were willing to talk to decision makers about establishing CP. Both FM and OB/GYN residents who spent more than 15 h engaged with CP and who expressed support towards CP were more likely to participate as a facilitator. FM residents who received residency-based training on CP and who were more familiar with CP reported higher intention to participate as a facilitator, while OB/GYN residents who had higher levels of engagement with CP were more likely to report an intention to participate as a facilitator. Conclusion Engagement with and support towards CP during residency are key factors in residents’ intention to practice CP in the future. To encourage future adoption of CP among residents, consider maximizing resident engagement with the model in hours of exposure and level of engagement, including hosting residency-based trainings on CP for FM residents.
Alternative approaches for creating a wealth index: the case of Mozambique
Xie, Kexin; Marathe, Achla; Deng, Xinwei; Ruiz-Castillo, Paula; Imputiua, Saimado; Elobolobo, Eldo; Mutepa, Victor; Sale, Mussa; Nicolas, Patricia; Montana, Julia; Jamisse, Edgar; Munguambe, Humberto; Materrula, Felisbela; Casellas, Aina; Rabinovich, Regina; Saute, Francisco; Chaccour, Carlos J.; Sacoor, Charfudin; Rist, Cassidy (BMJ, 2023-08)
Introduction: The wealth index is widely used as a proxy for a household's socioeconomic position (SEP) and living standard. This work constructs a wealth index for the Mopeia district in Mozambique using data collected in year 2021 under the BOHEMIA (Broad One Health Endectocide-based Malaria Intervention in Africa) project. Methods: We evaluate the performance of three alternative approaches against the Demographic and Health Survey (DHS) method based wealth index: feature selection principal components analysis (PCA), sparse PCA and robust PCA. The internal coherence between four wealth indices is investigated through statistical testing. Validation and an evaluation of the stability of the wealth index are performed with additional household income data from the BOHEMIA Health Economics Survey and the 2018 Malaria Indicator Survey data in Mozambique. Results: The Spearman's rank correlation between wealth index ventiles from four methods is over 0.98, indicating a high consistency in results across methods. Wealth rankings and households' income show a strong concordance with the area under the curve value of ∼0.7 in the receiver operating characteristic analysis. The agreement between the alternative wealth indices and the DHS wealth index demonstrates the stability in rankings from the alternative methods. Conclusions: This study creates a wealth index for Mopeia, Mozambique, and shows that DHS method based wealth index is an appropriate proxy for the SEP in low-income regions. However, this research recommends feature selection PCA over the DHS method since it uses fewer asset indicators and constructs a high-quality wealth index.
Assessing Ecosystem State Space Models: Identifiability and Estimation
Smith, John W.; Johnson, Leah R.; Thomas, R. Quinn (Springer, 2023-03)
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
Differences in Sleep Quality and Sleepiness among Veterinary Medical Students at Multiple Institutions before and after the Pandemic Induced Transition to Online Learning
Nappier, Michael T.; Alvarez, Elizabeth E.; Bartl-Wilson, Lara; Boynton, Elizabeth P.; Hanlon, Alexandra L.; Lozano, Alicia J.; Ng, Zenithson; Ogunmayowa, Oluwatosin; Shoop, Tiffany; Welborn, Nancy D.; Wuerz, Julia (University of Toronto Press)
Poor sleep health has been previously documented in veterinary medical students. However, it is not known how universal or widespread this problem is. This study evaluated Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) scores to measure sleep health among students at seven colleges of veterinary medicine in the United States (US). Inadvertently, the transition to online only learning due to the global COVID-19 pandemic was also captured. Veterinary students were found to have universally poor sleep quality and high daytime sleepiness. The transition to online only learning appeared to have little impact on sleep quality, but improved daytime sleepiness scores were observed. The findings suggest poor sleep health is common among veterinary medical students at multiple institutions in the US and that further investigation is necessary.
BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data
Xu, Shuangshuang; Williams, Jacob; Ferreira, Marco A. R. (2023-09-15)
Background Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data. Results We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data).
Design of adaptive EWMA control charts using the conditional false alarm rate
Aytaçoğlu, Burcu; Driscoll, Anne R.; Woodall, William H. (Wiley, 2023-04)
Dynamic control limits can be useful in designing control charts, especially when sample sizes, risk scores, or other covariate values change over time. Computer simulation can be used to control the conditional false alarm rate and thus the in-control run length properties. We show that this approach can be useful in designing adaptive exponentially weighted moving average (AEWMA) control charts for which the control chart smoothing parameter at a given time point depends on the observed value at that time point. We use AEWMA charts as examples, but the approach can be applied to the adaptive cumulative sum (CUSUM) chart and other types of adaptive charts.
Review: Is design data collection still relevant in the big data era? With extensions to machine learning
Freeman, Laura J. (Wiley, 2023-06)

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