Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models

Stringer, Alex - Nama Orang; Brown, Patrick - Nama Orang; Stafford, Jamie - Nama Orang;

We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the additive predictor and mean response. The new class covers a broad range of interesting models including multi-resolution spatial processes, partial likelihood-based survival models, and multivariate measurement error models. Because computation of the exact posterior distribution is infeasible, we develop a fast, scalable approximate Bayesian inference methodology for this class based on nested Gaussian, Laplace, and adaptive quadrature approximations. We prove that the error in these approximate posteriors is op(1) under standard conditions, and provide numerical evidence suggesting that our method runs faster and scales to larger datasets than methods based on Integrated Nested Laplace Approximations and Markov chain Monte Carlo, with comparable accuracy. We apply the new method to the mapping of malaria incidence rates in continuous space using aggregated data, mapping leukemia survival hazards using a Cox Proportional-Hazards model with a continuously-varying spatial process, and estimating the mass of the Milky Way Galaxy using noisy multivariate measurements of the positions and velocities of star clusters in its orbit. Supplementary materials for this article are available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art144993	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.32 No.1 March 2023
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 84-98
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: BAYESIAN METHODS
APPROXIMATE INFERENCE
SPATIAL STATISTICS
LAPLACE APPROXIMATION
ADDITIVE MODEL
Info Detail Spesifik: https://doi.org/10.1080/10618600.2022.2099403
Pernyataan Tanggungjawab: Alex Stringer, Patrick Brown, Jamie Stafford

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