Results 11 to 20 of about 324,176 (205)

Fast matrix computations for functional additive models [PDF]

open access: yes, 2014
It is common in functional data analysis to look at a set of related functions: a set of learning curves, a set of brain signals, a set of spatial maps, etc.
Barthelme, Simon
core   +2 more sources

Two contrasting spatial processes with a common variogram: inference about spatial models from higher‐order statistics

open access: yesEuropean Journal of Soil Science, 2010
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these properties over regions or at unsampled locations. A key step in geostatistical analysis is the estimation of a variogram function that describes the spatial covariance structure of the variable in question. If it can be assumed plausibly that the data are a
openaire   +1 more source

Spatial modeling of extreme snow depth

open access: yes, 2011
The spatial modeling of extreme snow is important for adequate risk management in Alpine and high altitude countries. A natural approach to such modeling is through the theory of max-stable processes, an infinite-dimensional extension of multivariate ...
Blanchet, Juliette, Davison, Anthony C.
core   +1 more source

Transitivity for height versus speed: To what extent do the under-7s really have a transitive capacity? [PDF]

open access: yes, 2011
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 Psychology Press.Transitive inference underpins many human reasoning competencies.
Hadfield, L, Robertson, S, Wright, BC
core   +1 more source

State estimation for temporal point processes [PDF]

open access: yes, 2015
This paper is concerned with combined inference for point processes on the real line observed in a broken interval. For such processes, the classic history-based approach cannot be used.
van Lieshout, M. N. M.
core   +3 more sources

A multi-resolution approximation for massive spatial datasets

open access: yes, 2015
Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions.
Katzfuss, Matthias
core   +2 more sources

Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM

open access: yesFEBS Letters, EarlyView.
Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley   +1 more source

Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models [PDF]

open access: yes, 2016
1. Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the ...
Anderson, Barbara J.   +3 more
core   +1 more source

Mapping the evolution of mitochondrial complex I through structural variation

open access: yesFEBS Letters, EarlyView.
Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin   +2 more
wiley   +1 more source

Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

open access: yes, 2019
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data.
Iwata, Tomoharu   +5 more
core   +1 more source

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