A linked Tucker tensor factorization enables a joint individualized hurdle-ordinal regression model that uncovers spatially heterogeneous effects of fluoride and diet on paired caries and fluorosis outcomes.
Journal of the royal statistical society: Series B (Methodolog- ical) 36, 111–133
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
Develops a unified framework representing performance metrics as smooth functionals of confusion-matrix probabilities to enable cluster-robust sandwich variance estimation for asymptotically valid confidence intervals and tests under clustered data.
Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
Parameters from statistical reconstruction of Itô process coefficients via normal mixture separation improve autoregressive time series prediction.
citing papers explorer
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Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes
A linked Tucker tensor factorization enables a joint individualized hurdle-ordinal regression model that uncovers spatially heterogeneous effects of fluoride and diet on paired caries and fluorosis outcomes.
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Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
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Beyond Point Estimates: Reliable Evaluation of Prediction Performance Metrics under Clustered Data
Develops a unified framework representing performance metrics as smooth functionals of confusion-matrix probabilities to enable cluster-robust sandwich variance estimation for asymptotically valid confidence intervals and tests under clustered data.
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Optimal sequential two-stage Bayes Factor Design for two-arm clinical Phase II Trials with binary Endpoints
Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.
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Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
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Extraction of informative statistical features in the problem of forecasting time series generated by It{\^{o}}-type processes
Parameters from statistical reconstruction of Itô process coefficients via normal mixture separation improve autoregressive time series prediction.