A modified slacks-based measure of efficiency in data envelopment analysis

https://onlinelibrary.wiley.com/doi/pdf/10.1111/itor.12468

A review of dynamic data envelopment analysis: state of the art and applications

We develop a heterogeneous autoregressive model of downside volatility (HAR‐DV) model with structural changes (HAR‐DV‐SC) model to investigate the effects of structural changes on predicting downside volatility. Then we employ HAR‐DV and HAR‐DV‐SC models to forecast downside volatilities in S&P 500 index, crude oil, gold, copper, and soybean futures markets. The in‐sample analysis shows that structural changes contain in‐sample information for predicting downside volatility. The out‐of‐sample analysis indicates that the HAR‐DV‐SC model outperforms the HAR‐DV model, suggesting that structural changes contain incremental out‐of‐sample information on future downside volatility. These results are robust and have important implications for risk management of stakeholders.

Effects of structural changes on the prediction of downside volatility in futures markets

Performance prediction is an issue of vital importance in many real managerial applications. This paper will propose a prediction approach for sports team performance based on data envelopment analysis (DEA) methodology and data-driven technique. The proposed approach includes two steps: The first one conducts a multivariate logistic regression analysis to examine the relationship between the winning probability and game outcomes at the team-level. The other one addresses a DEA-based player portfolio efficiency analysis to optimally choose players and plan the playing time among players in the court. The second step aims to use players’ and team's historical data to train the future and obtain the most promising outcomes in terms of their average inefficiency status. Finally, we apply the proposed performance prediction approach to National Basketball Association and take Golden State Warriors as an example to illustrate its usefulness and efficacy. We obtain the prediction results for the 2015–16 regular season based on a four-season dataset from the 2011–12 season to the 2014–15 season. Further, we carry out multiple experiments to provide deeper discussion and analysis on according prediction results. It shows that the DEA-based data-driven approach can predict the sports team performance very well and can also provide interesting insights into the performance prediction problem.

A data-driven prediction approach for sports team performance and its application to National Basketball Association

Energy and AQI performance of 31 cities in China

Handling negative data in slacks-based measure data envelopment analysis models

It has been well recognized that to thoroughly evaluate a firm’s performance, the evaluator must assess not only its past and present records but also future potential. However, to the best of our knowledge, there are no data envelopment analysis (DEA)-type models proposed in the literature that simultaneously take past, present and, especially, future performance indicators into account. Hence, this research aims at developing a new type of DEA model referred to as Intertemporal DEA models that can be used to fully measure a firm’s efficiency by explicitly considering its key inputs and outputs involving the past-present-future time span. In this research, the proposed Intertemporal DEA models are applied to the performance evaluation of high-tech Integrated Circuit design companies in Taiwan to demonstrate their advantages over other DEA models that ignore intertemporal efficiency.

https://papers.ssrn.com/sol3/Delivery.cfm/jors_2013_139.pdf?abstractid=2534374&mirid=1

Past-present-future Intertemporal DEA models

DEA models incorporating uncertain future performance

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation.

Introduction Macrophages are components of the innate immune system and can play an anti-tumor or pro-tumor role in the tumor microenvironment owing to their high heterogeneity and plasticity. Meanwhile, prostate cancer (PCa) is an immune-sensitive tumor, making it essential to investigate the value of macrophage-associated networks in its prognosis and treatment. Methods Macrophage-related marker genes (MRMGs) were identified through the comprehensive analysis of single-cell sequencing data from GSE141445 and the impact of macrophages on PCa was evaluated using consensus clustering of MRMGs in the TCGA database. Subsequently, a macrophage-related marker gene prognostic signature (MRMGPS) was constructed by LASSO-Cox regression analysis and grouped based on the median risk score. The predictive ability of MRMGPS was verified by experiments, survival analysis, and nomogram in the TCGA cohort and GEO-Merged cohort. Additionally, immune landscape, genomic heterogeneity, tumor stemness, drug sensitivity, and molecular docking were conducted to explore the relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection. Results We identified 307 MRMGs and verified that macrophages had a strong influence on the development and progression of PCa. Furthermore, we showed that the MRMGPS constructed with 9 genes and the predictive nomogram had excellent predictive ability in both the TCGA and GEO-Merged cohorts. More importantly, we also found the close relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection by multi-omics analysis. Discussion Our study reveals the application value of MRMGPS in predicting the prognosis of PCa patients. It also provides a novel perspective and theoretical basis for immune research and drug choices for PCa.

/pdf/multi-omics-analysis-reveals-a-macrophage-related-marker-1k143ghi.pdf

Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer

Chen Hui Wu

Papers

Handling negative data in slacks-based measure data envelopment analysis models

Past-present-future Intertemporal DEA models

DEA models incorporating uncertain future performance

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation.

Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer