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Modeling insurance claims using Bayesian nonparametric regression. [PDF]
Predicting future insurance claims using observed covariates is essential for actuaries in setting appropriate insurance premiums. For this purpose, actuaries commonly employ parametric regression models, which assume the same functional form tying the ...
Mostafa Shams, Kaushik Ghosh
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Bayesian nonparametric method for genetic dissection of brain activation region [PDF]
Biological evidence indicewates that the brain atrophy can be involved at the onset of neuropathological pathways of Alzheimer's disease. However, there is lack of formal statistical methods to perform genetic dissection of brain activation phenotypes ...
Zhuxuan Jin +3 more
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A Bayesian nonparametric method for jointly clustering multiple spatial transcriptomic datasets and simultaneous gene selection [PDF]
In spatial transcriptomics, many algorithms are available for clustering cells into groups based on gene expression and location, although not without limitations. Such limitations include having to know the number of clusters, limiting inference to only
Donald Turner, Yang Ni
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Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of
Nikita S. Pyko +5 more
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Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data in the presence of nonignorable missing
Zhenhuan Wu, Xingde Duan, Wenzhuan Zhang
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Bayesian Analysis for Penalized Spline Regression Using WinBUGS
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression ...
Ciprian M. Crainiceanu +2 more
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Bayesian model averaging for nonparametric discontinuity design.
Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions.
Max Hinne +3 more
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qgam: Bayesian Nonparametric Quantile Regression Modeling in R
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.
Matteo Fasiolo +4 more
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Bayesian nonparametric duration model with censorship
This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S.
Joseph Hakizamungu, Jean-Marie Rolin
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Model Selection for High Dimensional Nonparametric Additive Models via Ridge Estimation
In ultrahigh dimensional data analysis, to keep computational performance well and good statistical properties still working, nonparametric additive models face increasing challenges.
Haofeng Wang +3 more
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