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pro vyhledávání: '"van Waaij, Jan"'
Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that indiv
Externí odkaz:
http://arxiv.org/abs/2302.04596
Autor:
van Waaij, Jan
We consider M SNP data from N individuals who are an admixture of K unknown ancient populations. Let $\Pi_{si}$ be the frequency of the reference allele of individual i at SNP s. So the number of reference alleles at SNP s for a diploid individual is
Externí odkaz:
http://arxiv.org/abs/2202.05540
We propose two new statistics, V and S, to disentangle the population history of related populations from SNP frequency data. If the populations are related by a tree, we show by theoretical means as well as by simulation that the new statistics are
Externí odkaz:
http://arxiv.org/abs/2201.09098
Autor:
van Waaij, Jan
Due to their conjugate posteriors, Gaussian process priors are attractive for estimating the drift of stochastic differential equations with continuous time observations. However, their performance strongly depends on the choice of the hyper-paramete
Externí odkaz:
http://arxiv.org/abs/1909.12710
Publikováno v:
J. Stat Inference Stoch Process (2018) 21: 603
We consider the problem of nonparametric estimation of the drift of a continuously observed one-dimensional diffusion with periodic drift. Motivated by computational considerations, van der Meulen e.a. (2014) defined a prior on the drift as a randoml
Externí odkaz:
http://arxiv.org/abs/1612.05124
Autor:
van Waaij, Jan, van Zanten, Harry
We study random series priors for estimating a functional parameter (f\in L^2[0,1]). We show that with a series prior with random truncation, Gaussian coefficients, and inverse gamma multiplicative scaling, it is possible to achieve posterior contrac
Externí odkaz:
http://arxiv.org/abs/1609.01577
Autor:
van Waaij, Jan, van Zanten, Harry
We study the performance of nonparametric Bayes procedures for one-dimensional diffusions with periodic drift. We improve existing convergence rate results for Gaussian process (GP) priors with fixed hyper parameters. Moreover, we exhibit several pos
Externí odkaz:
http://arxiv.org/abs/1506.00515
Autor:
van Waaij, Jan, van Zanten, Harry
Publikováno v:
In Statistics and Probability Letters April 2017 123:93-99
Publikováno v:
Statistical Inference for Stochastic Processes; Oct2018, Vol. 21 Issue 3, p603-628, 26p
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