Shrinkage Function And Its Applications In Matrix Approximation

The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are demonstrated in solving several well-known problems, together with a new result in matrix approximation

ECONOMIC REPLACEMENT OF A HETEROGENEOUS HERD

A model was developed and used to determine the optimal slaughter weights of pigs with heterogeneous growth raised in a 1,000 head barn and marketed in truckload groups. Explicitly recognizing the heterogeneity of pig weights and marketing the herd over time in truckload batches can substantially increase profit.Marketing,

The Charitable Habits of Blood Donors

Introduction: There is a need for a constant supply of blood and blood products (e.g. plasma and platelets) in the American health care system. Common recipients of blood include: patients at risk for major hemorrhage, patients with sickle cell anemia, patients undergoing surgery, and thrombocytopenia in neonatal patients. This demand is met through nationwide blood banks, such as the American Red Cross, and their blood donation programs. The American Red Cross relies solely on volunteer donors; thus, one of the most pressing issues facing this institution is getting donors in the door. Through our survey questions we hope to uncover more factors that guide individuals in their philanthropic ways. The overall goal of this research is focused on unveiling new information that will supply the American Red Cross with valuable insight into their donor population and possible opportunities for joint publicity. We investigated the similarities and difference between how and why individuals undertake certain charitable activities.https://scholarworks.uvm.edu/comphp_gallery/1206/thumbnail.jp

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative

BACKGROUND: The Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources. METHODS: DEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes. RESULTS: In the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8). CONCLUSION: Most C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs