Zobrazeno 1 - 10
of 991
pro vyhledávání: '"Song, Peter"'
To evaluate a classification algorithm, it is common practice to plot the ROC curve using test data. However, the inherent randomness in the test data can undermine our confidence in the conclusions drawn from the ROC curve, necessitating uncertainty
Externí odkaz:
http://arxiv.org/abs/2405.12953
We propose a unified class of generalized structural equation models (GSEMs) with data of mixed types in mediation analysis, including continuous, categorical, and count variables. Such models extend substantially the classical linear structural equa
Externí odkaz:
http://arxiv.org/abs/2311.17867
In many practical studies, learning directionality between a pair of variables is of great interest while notoriously hard when their underlying relation is nonlinear. This paper presents a method that examines asymmetry in exposure-outcome pairs whe
Externí odkaz:
http://arxiv.org/abs/2311.04696
Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific field
Externí odkaz:
http://arxiv.org/abs/2310.11630
Autor:
Giddens, Spencer, Zhou, Yiwang, Krull, Kevin R., Brinkman, Tara M., Song, Peter X. K., Liu, Fang
It is commonplace to use data containing personal information to build predictive models in the framework of empirical risk minimization (ERM). While these models can be highly accurate in prediction, results obtained from these models with the use o
Externí odkaz:
http://arxiv.org/abs/2307.13127
Autor:
Purkayastha, Soumik, Song, Peter X. K.
As a fundamental concept in information theory, mutual information ($MI$) has been commonly applied to quantify association between random vectors. Most existing nonparametric estimators of $MI$ have unstable statistical performance since they involv
Externí odkaz:
http://arxiv.org/abs/2212.10268
Autor:
Purkayastha, Soumik, Song, Peter X. K.
Causal investigations in observational studies pose a great challenge in research where randomized trials or intervention-based studies are not feasible. We develop an information geometric causal discovery and inference framework of "predictive asym
Externí odkaz:
http://arxiv.org/abs/2210.14455
Data sharing barriers are paramount challenges arising from multicenter clinical trials where multiple data sources are stored in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a central
Externí odkaz:
http://arxiv.org/abs/2204.00857
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic dimension of the parameter space and unleashes sharper statistical accur
Externí odkaz:
http://arxiv.org/abs/2201.01036