10,988 research outputs found
The importance of scale in spatially varying coefficient modeling
While spatially varying coefficient (SVC) models have attracted considerable
attention in applied science, they have been criticized as being unstable. The
objective of this study is to show that capturing the "spatial scale" of each
data relationship is crucially important to make SVC modeling more stable, and
in doing so, adds flexibility. Here, the analytical properties of six SVC
models are summarized in terms of their characterization of scale. Models are
examined through a series of Monte Carlo simulation experiments to assess the
extent to which spatial scale influences model stability and the accuracy of
their SVC estimates. The following models are studied: (i) geographically
weighted regression (GWR) with a fixed distance or (ii) an adaptive distance
bandwidth (GWRa), (iii) flexible bandwidth GWR (FB-GWR) with fixed distance or
(iv) adaptive distance bandwidths (FB-GWRa), (v) eigenvector spatial filtering
(ESF), and (vi) random effects ESF (RE-ESF). Results reveal that the SVC models
designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa
and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the
most computationally efficient. Conversely GWR and ESF, where SVC estimates are
naively assumed to operate at the same spatial scale for each relationship,
perform poorly. Results also confirm that the adaptive bandwidth GWR models
(GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and
FB-GWR)
Adaptive variance function estimation in heteroscedastic nonparametric regression
We consider a wavelet thresholding approach to adaptive variance function
estimation in heteroscedastic nonparametric regression. A data-driven estimator
is constructed by applying wavelet thresholding to the squared first-order
differences of the observations. We show that the variance function estimator
is nearly optimally adaptive to the smoothness of both the mean and variance
functions. The estimator is shown to achieve the optimal adaptive rate of
convergence under the pointwise squared error simultaneously over a range of
smoothness classes. The estimator is also adaptively within a logarithmic
factor of the minimax risk under the global mean integrated squared error over
a collection of spatially inhomogeneous function classes. Numerical
implementation and simulation results are also discussed.Comment: Published in at http://dx.doi.org/10.1214/07-AOS509 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Spatial aggregation of local likelihood estimates with applications to classification
This paper presents a new method for spatially adaptive local (constant)
likelihood estimation which applies to a broad class of nonparametric models,
including the Gaussian, Poisson and binary response models. The main idea of
the method is, given a sequence of local likelihood estimates (``weak''
estimates), to construct a new aggregated estimate whose pointwise risk is of
order of the smallest risk among all ``weak'' estimates. We also propose a new
approach toward selecting the parameters of the procedure by providing the
prescribed behavior of the resulting estimate in the simple parametric
situation. We establish a number of important theoretical results concerning
the optimality of the aggregated estimate. In particular, our ``oracle'' result
claims that its risk is, up to some logarithmic multiplier, equal to the
smallest risk for the given family of estimates. The performance of the
procedure is illustrated by application to the classification problem. A
numerical study demonstrates its reasonable performance in simulated and
real-life examples.Comment: Published in at http://dx.doi.org/10.1214/009053607000000271 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Sharp estimation in sup norm with random design
The aim of this paper is to recover the regression function with sup norm
loss. We construct an asymptotically sharp estimator which converges with the
spatially dependent rate r\_{n, \mu}(x) = P \big(\log n / (n \mu(x)) \big)^{s /
(2s + 1)}, where is the design density, the regression smoothness,
the sample size and is a constant expressed in terms of a solution to a
problem of optimal recovery as in Donoho (1994). We prove this result under the
assumption that is positive and continuous. This estimator combines
kernel and local polynomial methods, where the kernel is given by optimal
recovery, which allows to prove the result up to the constants for any .
Moreover, the estimator does not depend on . We prove that is optimal in a sense which is stronger than the classical minimax
lower bound. Then, an inhomogeneous confidence band is proposed. This band has
a non constant length which depends on the local amount of data
ON CHOOSING A BASE COVERAGE LEVEL FOR MULTIPLE PERIL CROP INSURANCE CONTRACTS
For multiple peril crop insurance, the U.S. Department of Agriculture'Â’s Risk Management Agency estimates the premium rate for a base coverage level and then uses multiplicative adjustment factors to recover rates at other coverage levels. Given this methodology, accurate estimation of the base coverage level from 65% to 50%. The purpose of this analysis was to provide some insight into whether such a change should or should not be carried out. Not surprisingly, our findings indicate that the higher coverage level should be maintained as the base.Risk and Uncertainty,
Functional Regression
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
A variety of methods is available to quantify uncertainties arising with\-in
the modeling of flow and transport in carbon dioxide storage, but there is a
lack of thorough comparisons. Usually, raw data from such storage sites can
hardly be described by theoretical statistical distributions since only very
limited data is available. Hence, exact information on distribution shapes for
all uncertain parameters is very rare in realistic applications. We discuss and
compare four different methods tested for data-driven uncertainty
quantification based on a benchmark scenario of carbon dioxide storage. In the
benchmark, for which we provide data and code, carbon dioxide is injected into
a saline aquifer modeled by the nonlinear capillarity-free fractional flow
formulation for two incompressible fluid phases, namely carbon dioxide and
brine. To cover different aspects of uncertainty quantification, we incorporate
various sources of uncertainty such as uncertainty of boundary conditions, of
conceptual model definitions and of material properties. We consider recent
versions of the following non-intrusive and intrusive uncertainty
quantification methods: arbitary polynomial chaos, spatially adaptive sparse
grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The
performance of each approach is demonstrated assessing expectation value and
standard deviation of the carbon dioxide saturation against a reference
statistic based on Monte Carlo sampling. We compare the convergence of all
methods reporting on accuracy with respect to the number of model runs and
resolution. Finally we offer suggestions about the methods' advantages and
disadvantages that can guide the modeler for uncertainty quantification in
carbon dioxide storage and beyond
Bandwidth selection in kernel empirical risk minimization via the gradient
In this paper, we deal with the data-driven selection of multidimensional and
possibly anisotropic bandwidths in the general framework of kernel empirical
risk minimization. We propose a universal selection rule, which leads to
optimal adaptive results in a large variety of statistical models such as
nonparametric robust regression and statistical learning with errors in
variables. These results are stated in the context of smooth loss functions,
where the gradient of the risk appears as a good criterion to measure the
performance of our estimators. The selection rule consists of a comparison of
gradient empirical risks. It can be viewed as a nontrivial improvement of the
so-called Goldenshluger-Lepski method to nonlinear estimators. Furthermore, one
main advantage of our selection rule is the nondependency on the Hessian matrix
of the risk, usually involved in standard adaptive procedures.Comment: Published at http://dx.doi.org/10.1214/15-AOS1318 in the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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