3 citations · 4 across the 7 of their papers we have counts for
7 papers
A totally empirical basis of science
Orestis Loukas, Ho-Ryun Chung
Statistical hypothesis testing is the central method to demarcate scientific theories in both exploratory and inferential analyses. However, whether this method befits such purpose…
On the estimation and interpretation of effect size metrics
Orestis Loukas, Ho Ryun Chung
Effect size estimates are thought to capture the collective, two-way response to an intervention or exposure in a three-way problem among the intervention/exposure, various confoun…
Eliminating confounder-induced bias in the statistics of intervention
Orestis Loukas, Ho Ryun Chung
Experimental and observational studies often lead to spurious association between the outcome and independent variables describing the intervention, because of confounding to third…
Total Empiricism: Learning from Data
Orestis Loukas, Ho Ryun Chung
Statistical analysis is an important tool to distinguish systematic from chance findings. Current statistical analyses rely on distributional assumptions reflecting the structure o…
Demographic Parity: Mitigating Biases in Real-World Data
Orestis Loukas, Ho-Ryun Chung
Computer-based decision systems are widely used to automate decisions in many aspects of everyday life, which include sensitive areas like hiring, loaning and even criminal sentenc…
Entropy-based Characterization of Modeling Constraints
Orestis Loukas, Ho Ryun Chung
In most data-scientific approaches, the principle of Maximum Entropy (MaxEnt) is used to a posteriori justify some parametric model which has been already chosen based on experienc…
Categorical Distributions of Maximum Entropy under Marginal Constraints
Orestis Loukas, Ho Ryun Chung
The estimation of categorical distributions under marginal constraints summarizing some sample from a population in the most-generalizable way is key for many machine-learning and…