Related papers: A Conversation with Seymour Geisser
After completing their undergraduate studies, many computer science (CS) students apply for competitive graduate programs in North America. Their long-term goal is often to be hired by one of the big five tech companies or to become a…
This is an exchange between Jerome Sacks and Donald Ylvisaker covering their career paths along with some related history and philosophy of Statistics.
The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain…
The primary sourcebook for developments based on the data of the world components "Theory of Intellectualities and Mathematical Statistics" (TIMS) collections of the Department of Mathematics, Physics and Astronomy of Odessky National…
Leveraging health administrative data (HAD) datasets for predicting the risk of chronic diseases including diabetes has gained a lot of attention in the machine learning community recently. In this paper, we use the largest health records…
Moore introduced a method for graduate mathematics instruction that consisted primarily of individual student work on challenging proofs (Jones, 1977). Cohen (1982) described an adaptation with less explicit competition suitable for…
We have examined the success rates of 19 American, Canadian, Australian, and Dutch graduate programs in producing astronomers. A 20-year baseline was considered (1975-1994), incorporating 897 PhD recipients. The major conclusion from our…
This review is the updated and enlarged version of a talk delivered by J. S. on the occasion of the 1982 meeting of Nobel laureates at Lindau, and of talks given by B.-G. E. at several West German universities and Max Planck institutes in…
Deterministic compartmental models are predominantly used in the modeling of infectious diseases, though stochastic models are considered more realistic, yet are complicated to estimate due to missing data. In this paper we present a novel…
This study examined the teaching practices of 227 college instructors of introductory statistics (from the health and behavioral sciences). Using primarily multidimensional scaling (MDS) techniques, a two-dimensional, 10-item teaching…
In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes…
Advances in sensing technology have made it possible to collect large volumes of high-dimensional time-series data. In fields like genetics and neuroscience, key questions concern whether directed relationships between variables can be…
Understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions. We extend the classical SIR model by incorporating additional complexities such as the introduction of a cure and…
As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly…
Complex survey designs are commonly employed in many medical cohorts. In such scenarios, developing case-specific predictive risk score models that reflect the unique characteristics of the study design is essential for minimizing selective…
Molecular HIV Surveillance (MHS) has been described as key to enabling rapid responses to HIV outbreaks. It operates by linking individuals with genetically similar viral sequences, which forms a network. A major limitation of MHS is that…
Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in…
Following the recent publication of our book on Exploring the Health State of a Population by Dynamic Modeling Methods in The Springer Series on Demographic Methods and Population Analysis (DOI 10.1007/978-3-319-65142-2) we provide this…
The Sustainable Development Goals (SDGs) offer a lens for tracking societal change, yet contributions from the social and behavioral sciences have rarely been integrated into policy agendas. To take stock and create a baseline and benchmark…
These lecture notes were prepared for a special topics course in the Department of Statistics at the University of Washington, Seattle. They comprise the first eight chapters of a book currently in progress.