... or exceed clusters timeout. We suggest different subsampling strategies strongly reducing the number of events to overcome these difficulties and show how they can be used to draw conclusions on the initial point pattern. global envelope test kernel estimation Ripley’s K function space–time point...

... imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both...

... the effect of dataset subsampling based on a combination of traits (date, country, and PGRHA) on phylogenetic inference and subsequent downstream analysis, such as ancestral trait reconstruction and phylogeographic inference. We subsample and analyze two large HIV-1 Subtype C datasets with sequences...

... problematic sequencing or assembly. After constructing the divergent pathways, the subsampling procedure is performed based on these divergent pathways. First, genomes in the original data set are hierarchically divided into subsampling units, which are groups containing genomes with or within the same...

... of the first, in which subsampling is used to construct an incomplete U-statistic. Both methods are affine invariant and can readily be extended to partially paired two-sample tests with unordered samples. Asymptotic null distributions of the proposed test statistics are derived and the local powers...

... is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model ) Summary We study the properties of two subsampling procedures for networks, vertex...

.... For this completely novel approach, we apply a functional spatiotemporal hierarchical model to investigate the effect of environmental factors and the magnitude of the spatial and temporal dependence. Challenges in computational complexity are faced using a Monte Carlo subsampling approach. The results show...

... effects gives an additional piece of information: confound-free prediction accuracy. confound subsampling phenotype predictive models biomarkers statistical testing deconfounding Child Mind Institute 10.13039/100017974 Predictive models, using machine learning, are becoming a standard tool...

... primitives like privacy amplification by subsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation of differential privacy, which we term ‘f-differential privacy’ (f-DP). This notion of privacy has a number of appealing properties...

... subsampling guidelines sex-specific development Washburn University 10.13039/100006675 The age of a blow fly larva from a body, assuming eggs were laid after a person died, can provide an estimate of the minimum time since death ( Catts and Goff 1992 ). The estimate of larval age is based on a comparison...

...T A Kuffner; S M S Lee; G A Young The subsampling bootstrap ( Politis & Romano, 1994 ), and specifically the overlapping blocks version relevant to the present setting, first splits the original sample into precisely the same overlapping blocks as the moving block bootstrap, each of length...

... optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter...

... that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms. Dimension jumping Missing data Stochastic gradient Langevin dynamics Subsampling After six decades of continual development, Markov chain Monte Carlo, MCMC, has proven to be a powerful...

... become inefficient as the number of predictors or the number of subjects to classify gets large, because of the increasing computational time per step and worsening mixing rates. One strategy is to employ a gradient-based sampler to improve mixing while using data subsamples to reduce the per...

... to a real, within-host HIV sequencing data set with several thousand sequences per patient. Bayesian phylodynamics duplicate sequences subsampling large data sets BEAST 2 fast algorithms The reconstructed haplotypes are then aligned to create a multiple sequence alignment. Before it can be used...

... forests Subsampling U-statistics Address for correspondence: Tim Coleman, Department of Statistics, University of Pittsburgh, 1800 Wesley W. Posvar Hall, 230 South Bouquet Street, Pittsburgh, PA 15260, USA. E-mail: [email protected] 01 2019 03 2020 © 2020 Royal Statistical Society 2020...

... method leads to a smaller prediction error than other basis selection methods. Nonparametric regression Penalized least squares criterion Space-filling design Star discrepancy Subsampling Consider the nonparametric regression model , where is the th observation of the response...

...Ane De Celis; Iván Narváez; Francisco Ortega Alligatoroidea Crocodylia Crocodyloidea Gavialoidea macroevolution palaeotemperature shareholder quorum subsampling To analyse the relationship between subsampled eusuchian palaeodiversity and possible drivers, we compiled data from environmental...

...) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling -clustering- classification (SSCC), a new clustering framework based on random projection...

... procedure that sends a certified letter to all non-responding units. The second studied adaptive design restricts the non-response follow-up for the probability subsample of non-respondents selected for the targeted allocation. We compare the quality effects of the two studied adaptive non-response follow...