Semblance
Víctor Gustavo Tercero Gómez is a professor at the School of Engineering and Sciences at Tecnológico de Monterrey. He received his Ph.D. in Systems and Engineering Management from Texas Tech University and a second Ph.D. in Engineering Sciences from Tecnológico de Monterrey. He holds Black Belt and Master Black Belt certifications in Six Sigma, with years of experience leading and advising continuous improvement projects in manufacturing and service organizations. As a researcher, he has authored numerous scientific papers in the areas of statistical process monitoring, non-parametric statistics, and statistical engineering. He is currently a member of the National System of Researchers in Mexico with level 2 status.
Expertise and Skills
- Statistical Process Monitoring
- Non-Parametric Statistics
- Six Sigma
- Quality Engineering
- Statistical Quality Control
- Statistical Process Monitoring
- Non-Parametric Statistics
Academic Publications
- On the power and robustness of phase I nonparametric Shewhart-type charts using sequential normal scores
- A CUSUM control chart for gamma distribution with guaranteed performance
- Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization
- Multivariate Control Chart with Guaranteed IC performance and Cautious Parameter Learning I
- Statistical Analysis of Minsky’s Financial Instability Hypothesis for the 1945–2023 Era
- Sensitizing Rules for Change Point Detection in Phase I Analysis with Nonparametric Shewhart-type Control Chart
- A review on statistical process control in healthcare: data-driven monitoring schemes
- A distribution-free control chart for joint monitoring of location and scale in finite horizon productions
- Evaluation of economic hyperinflation through the lens of statistical process methodologies: A preliminary analysis and discussion
- Letter on statistical process monitoring research: Misdirections and recommendations.
- Nonparametric multivariate processes monitoring with guaranteed in-control performance for changes in location
- The case against generally weighted moving average (GWMA) control charts
- The impracticality of homogeneously weighted moving average and progressive mean control chart approaches
- Using statistical process monitoring to identify us business cycle change points and turning points
- Quantifying Risk Perception: The Entropy Decision Risk Model Utility (EDRM-U)
- A distribution‐free CUSUM chart for joint monitoring of location and scale based on the combination of Wilcoxon and Mood statistics
- A system dynamics-based technological archetype for the economics of leasing capital-intensive industrial robots