Dataset Information

MILFM: Multiple index latent factor model based on high-dimensional features.

ABSTRACT: The aim of this article is to develop a multiple-index latent factor modeling (MILFM) framework to build an accurate prediction model for clinical outcomes based on a massive number of features. We develop a three-stage estimation procedure to build the prediction model. MILFM uses an independent screening method to select a set of informative features, which may have a complex nonlinear relationship with the outcome variables. Moreover, we develop a latent factor model to project all informative predictors onto a small number of local subspaces, which lead to a few key features that capture reliable and informative covariate information. Finally, we fit the regularized empirical estimate to those key features in order to accurately predict clinical outcomes. We systematically investigate the theoretical properties of MILFM, such as risk bounds and selection consistency. Our simulation results and real data analysis show that MILFM outperforms many state-of-the-art methods in terms of prediction accuracy.

SUBMITTER: Yang H

PROVIDER: S-EPMC6158073 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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MILFM: Multiple index latent factor model based on high-dimensional features.

Yang Hojin H Zhu Hongtu H Ibrahim Joseph G JG

Biometrics 20180417 3

The aim of this article is to develop a multiple-index latent factor modeling (MILFM) framework to build an accurate prediction model for clinical outcomes based on a massive number of features. We develop a three-stage estimation procedure to build the prediction model. MILFM uses an independent screening method to select a set of informative features, which may have a complex nonlinear relationship with the outcome variables. Moreover, we develop a latent factor model to project all informativ ...[more]

PMID: 29665616

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Project description:The ability to quantitatively assess ecological health is of great interest to those tasked with monitoring and conserving ecosystems. For decades, biomonitoring research and policies have relied on multimetric health indices of various forms. Although indices are numbers, many are constructed based on qualitative procedures, thus limiting the quantitative rigor of the practical interpretations of such indices. The statistical modeling approach to construct the latent health factor index (LHFI) was recently developed. With ecological data that otherwise are used to construct conventional multimetric indices, the LHFI framework expresses such data in a rigorous quantitative model, integrating qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modeling approach allows unified statistical inference of health for observed sites (along with prediction of health for partially observed sites, if desired) and of the relevance of ecological drivers, all accompanied by formal uncertainty statements from a single, integrated analysis. Thus far, the LHFI approach has been demonstrated and validated in a freshwater context. We adapt this approach to modeling estuarine health, and illustrate it on the previously unassessed system in Richibucto in New Brunswick, Canada, where active oyster farming is a potential stressor through its effects on sediment properties. Field data correspond to health metrics that constitute the popular AZTI marine biotic index and the infaunal trophic index, as well as abiotic predictors preconceived to influence biota. Our paper is the first to construct a scientifically sensible model that rigorously identifies the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content-all regarded a priori as qualitatively important abiotic drivers-towards site health in the Richibucto ecosystem. This suggests the potential effectiveness of the LHFI approach for assessing not only freshwater systems but aquatic ecosystems in general.

Dataset Information

MILFM: Multiple index latent factor model based on high-dimensional features.

Publications

MILFM: Multiple index latent factor model based on high-dimensional features.

Similar Datasets

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