Hilbert space methods for reduced-rank Gaussian process regression
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DOI: 10.1007/s11222-019-09886-w
Abstract: This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in… read more here.
Keywords: reduced rank; gaussian process; covariance function;
Low-Rank Covariance Function Estimation for Multidimensional Functional Data
Sign Up to like & getrecommendations! Published in 2020 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2020.1820344
Abstract: Multidimensional function data arise from many fields nowadays. The covariance function plays an important role in the analysis of such increasingly common data. In this article, we propose a novel... read more here.
Keywords: rank covariance; covariance function; function; low rank ... See more keywords
Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2023.2200522
Abstract: Covariance function estimation is a fundamental task in multivariate functional data analysis and arises in many applications. In this paper, we consider estimating sparse covariance functions for high-dimensional functional data, where the number of random… read more here.
Keywords: functional thresholding; thresholding; adaptive functional; covariance function ... See more keywords
Construction of a criterion for testing hypothesis about covariance function of a stationary Gaussian stochastic process with unknown mean
Sign Up to like & getrecommendations! Published in 2018 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2017.1377253
Abstract: ABSTRACT In this paper, a new criterion is constructed for testing hypothesis about covariance function of Gaussian stationary stochastic process with an unknown mean. This criterion is based on the fact that we can estimate… read more here.
Keywords: testing hypothesis; hypothesis covariance; criterion; covariance function ... See more keywords
Efficient semiparametric regression for longitudinal data with regularised estimation of error covariance function
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2019.1651853
Abstract: ABSTRACT Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of errors. But challenges arise in estimating the covariance matrix of longitudinal… read more here.
Keywords: longitudinal data; estimation; estimating covariance; covariance function ... See more keywords
Basis expansions for functional snippets
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DOI: 10.1093/biomet/asaa088
Abstract: Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of… read more here.
Keywords: basis expansions; mean covariance; functional snippets; covariance ... See more keywords