Bayesian inference has emerged as a powerful tool in the analysis of queueing systems, blending probability theory with statistical estimation to update beliefs about system parameters as new data ...
Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...
The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
A Perspective published in Volume 3 of the journal Psychoradiology, researchers from Shanghai Jiao Tong University confronted these challenges and advocates for more clarity and transparency in causal ...
At the core of science is a commitment to rigorous reasoning, method, and the use of evidence. The final session of the workshop was designed to take a step back from the specific issues of how ...
Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the ...
Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized? The following represents disclosure information provided by authors ...
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