pinecone/core-2020-05-10-deduplication huggingface dataset:
Mixture of inhomogeneous matrix models for species-rich ecosystems

Understanding how environmental factors could impact population dynamics is of primary importance for species conservation. Matrix population models are widely used to predict population dynamics. However, in species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. In addition, classical matrix models do not take into account environmental variability. We propose a mixture of regression models with variable selection allowing the simultaneous clustering of species into groups according to vital rate information (recruitment, growth and mortality) and the identification of group-specific explicative environmental variables. We develop an inference method coupling the R packages flexmix and glmnet. We first highlight the effectiveness of the method on simulated datasets. Next, we apply it to data from a tropical rain forest in the Central African Republic. We demonstrate the accuracy of the inhomogeneous mixture matrix model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations. (Résumé d'auteur
Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise

Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean.In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain
Bayesian Hidden Markov Modelling Using Circular-Linear General Projected
  Normal Distribution

We introduce a multivariate hidden Markov model to jointly cluster
time-series observations with different support, i.e. circular and linear.
Relying on the general projected normal distribution, our approach allows for
bimodal and/or skewed cluster-specific distributions for the circular variable.
Furthermore, we relax the independence assumption between the circular and
linear components observed at the same time. Such an assumption is generally
used to alleviate the computational burden involved in the parameter estimation
step, but it is hard to justify in empirical applications. We carry out a
simulation study using different data-generation schemes to investigate model
behavior, focusing on well recovering the hidden structure. Finally, the model
is used to fit a real data example on a bivariate time series of wind speed and
direction.Comment: Environmetrics, (2015
Design-based strategies for sampling spatial units from regular grids with applications to forest surveys, land use, and land cover estimation

The purpose of this paper is to compare some spatial strategies for sampling polygons onto a grid partitioning a study area.
Most of the schemes considered in the paper are aimed at avoiding the selection of neighboring polygons. When one or
more auxiliary variables are similar or well correlated with the values of the survey variable, the auxiliary information
is adopted at estimation level by means of the difference or the regression estimators, or at design level, using the values
of auxiliary variables to determine the inclusion probabilities. Applications to large-scale forest inventories, land use
estimation, and forest cover estimation are discussed. A simulation study is performed to compare the adopted strategies
in terms of bias (if present), accuracy, and accuracy estimation. The simulation is designed to mimic forest inventories and
forest cover estimation, starting from some real situations. An application to plan future surveys for land use estimation in
Italy is reported.L'articolo è disponibile sul sito dell'editore http://onlinelibrary.wiley.co
Exploratory ensemble designs for environmental models using k-extended Latin Hypercubes

Copyright © 2015 John Wiley & Sons, Ltd.publication-status: AcceptedOpen Access articleIn this paper we present a novel, flexible, and multi-purpose class of designs for initial exploration of the parameter spaces of computer models, such as those used to study many features of the environment. The idea applies existing technology aimed at expanding a Latin Hypercube (LHC) in order to generate initial LHC designs that are composed of many smaller LHCs. The resulting design and its component parts are designed so that each is approximately orthogonal and maximises a measure of coverage of the parameter space. Designs of the type advocated for in this paper are particularly useful when we want to simultaneously quantify parametric uncertainty and any uncertainty due to the initial conditions, boundary conditions, or forcing functions required to run the model. This makes the class of designs particularly suited to environmental models, such as climate models that contain all of these features. The proposed designs are particularly suited to initial exploratory ensembles whose goal is to guide the design of further ensembles aimed at, for example, calibrating the model. We introduce a new emulator diagnostic that exploits the structure of the advocated ensemble designs and allows for the assessment of structural weaknesses in the statistical modelling. We provide illustrations of the method through a simple example and describe a 400 member ensemble of the Nucleus for European Modelling of the Ocean (NEMO) ocean model designed using the method. We build an emulator for NEMO using the created design to illustrate the use of our emulator diagnostic test.Engineering and Physical Sciences Research Council (EPSRC
Computationally efficient spatial modeling of annual maximum 24 hour
  precipitation. An application to data from Iceland

We propose a computationally efficient statistical method to obtain
distributional properties of annual maximum 24 hour precipitation on a 1 km by
1 km regular grid over Iceland. A latent Gaussian model is built which takes
into account observations, spatial variations and outputs from a local
meteorological model. A covariate based on the meteorological model is
constructed at each observational site and each grid point in order to
assimilate available scientific knowledge about precipitation into the
statistical model. The model is applied to two data sets on extreme
precipitation, one uncorrected data set and one data set that is corrected for
phase and wind. The observations are assumed to follow the generalized extreme
value distribution. At the latent level, we implement SPDE spatial models for
both the location and scale parameters of the likelihood. An efficient MCMC
sampler which exploits the model structure is constructed, which yields fast
continuous spatial predictions for spatially varying model parameters and
quantiles.Comment: 32 pages, 16 figures, submitted to Environmetric
Parameter inference and model selection in deterministic and stochastic
  dynamical models via approximate Bayesian computation: modeling a wildlife
  epidemic

We consider the problem of selecting deterministic or stochastic models for a
biological, ecological, or environmental dynamical process. In most cases, one
prefers either deterministic or stochastic models as candidate models based on
experience or subjective judgment. Due to the complex or intractable likelihood
in most dynamical models, likelihood-based approaches for model selection are
not suitable. We use approximate Bayesian computation for parameter estimation
and model selection to gain further understanding of the dynamics of two
epidemics of chronic wasting disease in mule deer. The main novel contribution
of this work is that under a hierarchical model framework we compare three
types of dynamical models: ordinary differential equation, continuous time
Markov chain, and stochastic differential equation models. To our knowledge
model selection between these types of models has not appeared previously.
Since the practice of incorporating dynamical models into data models is
becoming more common, the proposed approach may be very useful in a variety of
applications.Comment: 24 pages, 4 figures, submitte
Mixture EMOS model for calibrating ensemble forecasts of wind speed

Ensemble model output statistics (EMOS) is a statistical tool for
post-processing forecast ensembles of weather variables obtained from multiple
runs of numerical weather prediction models in order to produce calibrated
predictive probability density functions (PDFs). The EMOS predictive PDF is
given by a parametric distribution with parameters depending on the ensemble
forecasts. We propose an EMOS model for calibrating wind speed forecasts based
on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions
where model parameters and component weights are estimated by optimizing the
values of proper scoring rules over a rolling training period. The new model is
tested on wind speed forecasts of the 50 member European Centre for
Medium-Range Weather Forecasts ensemble, the 11 member Aire Limit\'ee
Adaptation dynamique D\'eveloppement International-Hungary Ensemble Prediction
System ensemble of the Hungarian Meteorological Service and the eight-member
University of Washington mesoscale ensemble, and its predictive performance is
compared to that of various benchmark EMOS models based on single parametric
families and combinations thereof. The results indicate improved calibration of
probabilistic and accuracy of point forecasts in comparison with the raw
ensemble and climatological forecasts. The mixture EMOS model significantly
outperforms the TN and LN EMOS methods, moreover, it provides better calibrated
forecasts than the TN-LN combination model and offers an increased flexibility
while avoiding covariate selection problems.Comment: 24 pages, 7 tables, 10 figure
Censored and shifted gamma distribution based EMOS model for
  probabilistic quantitative precipitation forecasting

Recently all major weather prediction centres provide forecast ensembles of
different weather quantities which are obtained from multiple runs of numerical
weather prediction models with various initial conditions and model
parametrizations. However, ensemble forecasts often show an underdispersive
character and may also be biased, so that some post-processing is needed to
account for these deficiencies. Probably the most popular modern
post-processing techniques are the ensemble model output statistics (EMOS) and
the Bayesian model averaging (BMA) which provide estimates of the density of
the predictable weather quantity.
  In the present work an EMOS method for calibrating ensemble forecasts of
precipitation accumulation is proposed, where the predictive distribution
follows a censored and shifted gamma (CSG) law with parameters depending on the
ensemble members. The CSG EMOS model is tested on ensemble forecasts of 24 h
precipitation accumulation of the eight-member University of Washington
mesoscale ensemble and on the 11 member ensemble produced by the operational
Limited Area Model Ensemble Prediction System of the Hungarian Meteorological
Service. The predictive performance of the new EMOS approach is compared with
the fit of the raw ensemble, the generalized extreme value (GEV) distribution
based EMOS model and the gamma BMA method. According to the results, the
proposed CSG EMOS model slightly outperforms the GEV EMOS approach in terms of
calibration of probabilistic and accuracy of point forecasts and shows
significantly better predictive skill that the raw ensemble and the BMA model.Comment: 20 pages, 5 figures, 8 table
A comparison of statistical emulation methodologies for multi-wave calibration of environmental models

Expensive computer codes, particularly those used simulating environmental or geological processes such as climate models, require calibration (sometimes called tuning). When calibrating expensive simulators using uncertainty quantification methods, it is usually necessary to use a statistical model called an emulator in place of the computer code when running the calibration algorithm. Though emulators based on Gaussian processes are typically many orders of magnitude faster to evaluate than the simulator they mimic, many applications have sought to speed up the computations by using regression-only emulators within the calculations instead, arguing that the extra sophistication brought using the Gaussian process is not worth the extra computational power. This was the case for the analysis that produced the UK climate projections in 2009. In this paper we compare the effectiveness of both emulation approaches upon a multi-wave calibration framework that is becoming popular in the climate modelling community called \history matching". We find that Gaussian processes offer significant benefits to the reduction of parametric uncertainty over regression-only approaches. We find that in a multi-wave experiment, a combination of regression-only emulators initially, followed by Gaussian process emulators for refocussing experiments can be nearly as effective as using Gaussian processes throughout for a fraction of the computational cost. We also discover a number of design and emulator-dependent features of the multi-wave history matching approach that can cause apparent, yet premature, convergence of our estimates of parametric uncertainty. We compare these approaches to calibration in idealised examples and apply it to a well-known geological reservoir mode
Directional Multivariate Extremes in Environmental Phenomena

Several environmental phenomena can be described by different correlated
variables that must be considered jointly in order to be more representative of
the nature of these phenomena. For such events, identification of extremes is
inappropriate if it is based on marginal analysis. Extremes have usually been
linked to the notion of quantile, which is an important tool to analyze risk in
the univariate setting. We propose to identify multivariate extremes and
analyze environmental phenomena in terms of the directional multivariate
quantile, which allows us to analyze the data considering all the variables
implied in the phenomena, as well as look at the data in interesting directions
that can better describe an environmental catastrophe. Since there are many
references in the literature that propose extremes detection based on copula
models, we also generalize the copula method by introducing the directional
approach. Advantages and disadvantages of the non-parametric proposal that we
introduce and the copula methods are provided in the paper. We show with
simulated and real data sets how by considering the first principal component
direction we can improve the visualization of extremes. Finally, two cases of
study are analyzed: a synthetic case of flood risk at a dam (a 3-variable
case), and a real case study of sea storms (a 5-variable case).Comment: Article with supplementary material in the appendi
Nonparametric construction of probability maps under local stationarity

The environmental contamination risk can be evaluated in a specific area by approximating the probability
that the pollutant under study exceeds a critical value. This issue requires the estimation of the distribution function
involved, which can be addressed by applying the indicator kriging methodology or by approximating the sill of
the variogram of the underlying indicator process. These approaches demand an appropriate characterization of the
indicator variogram, which in turn requires a previous specification of the trend function, if the latter is suspected
to be non-constant. Since accuracy of the results will be strongly dependent on the adequate approximation of both
functions, we suggest proceeding in a different way to avoid these requirements. Thus, in the current paper, two kerneltype
estimators are proposed, based on first approximating the distribution at the sampled sites and then obtaining
a weighted average of the resulting values, to derive a valid estimator at each (sampled or unsampled) location.
Consistency of the kernel approaches is proved under rather general conditions, such as local stationarity and the
existence of derivatives up to the second order of the distribution function. Numerical studies have been carried out
to illustrate the performance of our proposals when compared to those procedures requiring the approximation of the
indicator variogram. In a final step, the kernel-type estimation of the distribution function has been applied to map
the risk of contamination by arsenic in the Central Region of Portugal. With this aim, biomonitoring data of arsenic
concentrations were used to detect those zones with higher risk of arsenic accumulation, which is mainly located on
the northern part of the region.The authors would like to thank the helpful suggestions and
comments from the Editor, the Associate Editor, and the
Reviewers. The authors are also grateful to Karen J. Duncan
for her contribution in the language revision. The first
author’s work has been partially supported by the Spanish
National Research and Development Program project
[TEC2015-65353-R], by the European Regional Development
Fund (ERDF), and by the Galician Regional Government
under project GRC 2015/018 and under agreement for
funding AtlantTIC (Atlantic Research Center for Information
and Communication Technologies). The second author
acknowledges financial support from the Portuguese Funds
through FCT-“Fundação para a Ciência e a Tecnologia,”
within the Project UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio
Analyzing Ozone Concentration by Bayesian Spatio-temporal Quantile
  Regression

Ground level Ozone is one of the six common air-pollutants on which the EPA
has set national air quality standards. In order to capture the spatio-temporal
trend of 1-hour and 8-hour average ozone concentration in the US, we develop a
method for spatio-temporal simultaneous quantile regression. Unlike existing
procedures, in the proposed method, smoothing across the sites is incorporated
within modeling assumptions thus allowing borrowing of information across
locations, an essential step when the number of samples in each location is
low. The quantile function has been assumed to be linear in time and smooth
over space and at any given site is given by a convex combination of two
monotone increasing functions $\xi_1$ and $\xi_2$ not depending on time. A
B-spline basis expansion with increasing coefficients varying smoothly over the
space is used to put a prior and a Bayesian analysis is performed. We analyze
the average daily 1-hour maximum and 8-hour maximum ozone concentration level
data of US and California during 2006-2015 using the proposed method. It is
noted that in the last ten years, there is an overall decreasing trend in both
1-hour maximum and 8-hour maximum ozone concentration level over the most parts
of the US. In California, an overall a decreasing trend of 1-hour maximum ozone
level is observed while no particular overall trend has been observed in the
case of 8-hour maximum ozone level
Nonstationary POT modelling of air pollution concentrations: Statistical
  analysis of the traffic and meteorological impact

Predicting the occurrence, level and duration of high air pollution
concentrations exceeding a given critical level enables researchers to study
the health impact of road traffic on local air quality and to inform public
policy action. Precise estimates of the probabilities of occurrence and level
of extreme concentrations are formidable due to the combination of complex
physical and chemical processes involved. This underpins the need for
developing sophisticated extreme value models, in particular allowing for
non-stationarity of environmental time series. In this paper, extremes of
nitrogen oxide (NO), nitrogen dioxide (NO$_2$) and ozone (O$_3$) concentrations
are investigated using two models. Model I is based on an extended
peaks-over-threshold (POT) approach developed by A. C. Davison and R. L. Smith,
whereby the parameters of the underlying generalized Pareto distribution (GPD)
are treated as functions of covariates (i.e., traffic and meteorological
factors). The new Model II resolves the lack of threshold stability in the
Davison--Smith model by constructing a special functional form for the GPD
parameters. For each of the models, the effects of trafic and meteorological
factors on the frequency and size of extreme values are estimated using Markov
chain Monte Carlo methods. Finally, appropriate goodness-of-fit tests and model
selection criteria confirm that Model II significantly outperforms Model I in
estimation and forecasting of extremes.Comment: 21 pages, 2 figures, 3 tables. Accepted for publication in
  "Environmetrics
Frequency selection in palaeoclimate time series : a model-based approach incorporating possible time uncertainty.\ud

A key aspect of paleoclimate time series analysis is the identification of frequency behavior. Commonly, this is achieved by calculating a power spectrum and comparing this spectrum with that of a simplified model. Traditional hypothesis testing method can then be used to find statistically significant peaks that correspond to different frequencies. Complications occur when the data are multivariate or suffer from time uncertainty. In particular, the presence of joint uncertainties surrounding observations and their timing makes traditional hypothesis testing impractical.\ud
\ud
In this paper, we reexpress the frequency identification problem in the time domain as a variable selection model where each variable corresponds to a different frequency. We place this problem in a Bayesian framework that allows us to place shrinkage prior distributions on the weighting of each frequency, as well as include informative prior information through which we can take account of time uncertainty.\ud
\ud
We validate our approach with simulated data and illustrate it with analysis of mid- to late Holocene water table records from two sites in Northern Ireland—Dead Island and Slieveanorra. Both case studies also show the extent of the challenges that researchers may face. We therefore present one case that shows a good model fit with a clear frequency pattern and the other case where the identification of frequency behavior is impossible. We contrast our results with that of the extant methodology, known as REDFIT