26,021 research outputs found
Projection predictive model selection for Gaussian processes
We propose a new method for simplification of Gaussian process (GP) models by
projecting the information contained in the full encompassing model and
selecting a reduced number of variables based on their predictive relevance.
Our results on synthetic and real world datasets show that the proposed method
improves the assessment of variable relevance compared to the automatic
relevance determination (ARD) via the length-scale parameters. We expect the
method to be useful for improving explainability of the models, reducing the
future measurement costs and reducing the computation time for making new
predictions.Comment: A few minor changes in tex
Modified Linear Projection for Large Spatial Data Sets
Recent developments in engineering techniques for spatial data collection
such as geographic information systems have resulted in an increasing need for
methods to analyze large spatial data sets. These sorts of data sets can be
found in various fields of the natural and social sciences. However, model
fitting and spatial prediction using these large spatial data sets are
impractically time-consuming, because of the necessary matrix inversions.
Various methods have been developed to deal with this problem, including a
reduced rank approach and a sparse matrix approximation. In this paper, we
propose a modification to an existing reduced rank approach to capture both the
large- and small-scale spatial variations effectively. We have used simulated
examples and an empirical data analysis to demonstrate that our proposed
approach consistently performs well when compared with other methods. In
particular, the performance of our new method does not depend on the dependence
properties of the spatial covariance functions.Comment: 29 pages, 5 figures, 4 table
A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
Inference for spatial generalized linear mixed models (SGLMMs) for
high-dimensional non-Gaussian spatial data is computationally intensive. The
computational challenge is due to the high-dimensional random effects and
because Markov chain Monte Carlo (MCMC) algorithms for these models tend to be
slow mixing. Moreover, spatial confounding inflates the variance of fixed
effect (regression coefficient) estimates. Our approach addresses both the
computational and confounding issues by replacing the high-dimensional spatial
random effects with a reduced-dimensional representation based on random
projections. Standard MCMC algorithms mix well and the reduced-dimensional
setting speeds up computations per iteration. We show, via simulated examples,
that Bayesian inference for this reduced-dimensional approach works well both
in terms of inference as well as prediction, our methods also compare favorably
to existing "reduced-rank" approaches. We also apply our methods to two real
world data examples, one on bird count data and the other classifying rock
types
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