no code implementations • 13 Jan 2021 • Joris Mulder, Peter D. Hoff
In this paper a multiplicative latent factor model is proposed to analyze such relational data.
Methodology
1 code implementation • 28 Apr 2020 • Anna K. Yanchenko, Peter D. Hoff
In this article, we develop a statistical methodology for identifying and quantifying systematic stylistic differences among artists that are consistent across audio recordings of a common set of pieces, in terms of several musical features.
Applications
no code implementations • 29 Jul 2019 • Peter D. Hoff
Importantly, the linking model does not need to be correct to maintain the uniformity of the $p$-values under their null hypotheses.
Methodology 62F03 62F03 62F03
1 code implementation • 20 Jul 2018 • Peter D. Hoff
Network datasets typically exhibit certain types of statistical dependencies, such as within-dyad correlation, row and column heterogeneity, and third-order dependence patterns such as transitivity and clustering.
Methodology 62H25, 62F15
no code implementations • 23 May 2017 • Peter D. Hoff, Chaoyu Yu
We describe an adaptive approach for estimating the prior distribution from the data so that exact non-asymptotic $1-\alpha$ coverage is maintained.
Methodology 62J05
no code implementations • 25 Dec 2016 • Chaoyu Yu, Peter D. Hoff
In this article we construct confidence intervals that have a constant frequentist coverage rate and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard $t$-intervals on average across groups.
Methodology 62C12
1 code implementation • 4 Oct 2014 • David C. Gerard, Peter D. Hoff
We develop a higher order generalization of the LQ decomposition and show that this decomposition plays an important role in likelihood-based estimation and testing for separable, or Kronecker structured, covariance models, such as the multilinear normal model.
Statistics Theory Statistics Theory 15A69, 62H12, 62H15, 65F99
no code implementations • 7 Nov 2007 • Peter D. Hoff
This article discusses a latent variable model for inference and prediction of symmetric relational data.
Methodology
