Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...
Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 76, No. 1 (JANUARY 2014), pp. 197-215 (19 pages) Multiphased designs and biased sampling designs are two of the ...
Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data ...
Stochastic dynamical systems arise in many scientific fields, such as asset prices in financial markets, neural activity in ...
Information on Earth's biodiversity is increasingly collected using DNA-, image- and audio-based sampling. At the same time, ...
Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease.
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 ...
Recently, a research team from Dankook University in South Korea proposed a new method that utilizes principles of quantum mechanics to solve causal inference problems. This breakthrough provides a ...
Information on Earth's biodiversity is increasingly collected using DNA-, image- and audio-based sampling. At the same time, ...
This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results
Feedback