1,968 research outputs found
Nonparametric regression for locally stationary random fields under stochastic sampling design
In this study, we develop an asymptotic theory of nonparametric regression
for locally stationary random fields (LSRFs) in observed at irregularly spaced locations
in . We first derive the uniform
convergence rate of general kernel estimators, followed by the asymptotic
normality of an estimator for the mean function of the model. Moreover, we
consider additive models to avoid the curse of dimensionality arising from the
dependence of the convergence rate of estimators on the number of covariates.
Subsequently, we derive the uniform convergence rate and joint asymptotic
normality of the estimators for additive functions. We also introduce
approximately -dependent RFs to provide examples of LSRFs. We find that
these RFs include a wide class of L\'evy-driven moving average RFs.Comment: 50 page
On the estimation of locally stationary functional time series
This paper develops an asymptotic theory for estimating the time-varying
characteristics of locally stationary functional time series. We introduce a
kernel-based method to estimate the time-varying covariance operator and the
time-varying mean function of a locally stationary functional time series.
Subsequently, we derive the convergence rate of the kernel estimator of the
covariance operator and associated eigenvalue and eigenfunctions. We also
establish a central limit theorem for the kernel-based locally weighted sample
mean. As applications of our results, we discuss the prediction of locally
stationary functional time series and methods for testing the equality of
time-varying mean functions in two functional samples.Comment: 37 page
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Older female mice lacking triggering recepter expressed on myeloid cells-2 have worse post-stroke neurological function and enhanced pro-inflammatory responses
Tako-tsubo cardiomyopathy: Clinical presentation and underlying mechanism
AbstractSince Dr Sato at Hiroshima City Hospital first recognized and reported the concept of tako-tsubo cardiomyopathy in 1990, this disorder has become accepted worldwide as a distinct clinical entity. Tako-tsubo cardiomyopathy is an important disorder as a differential diagnosis of acute myocardial infarction. This disorder usually occurs in postmenopausal women of an advanced age, and is characterized by transient left ventricular apical wall motion abnormalities associated with emotional or physical stress. Typically, left ventricular apical wall motion abnormalities are transient and resolve during a period of days to weeks. The prognosis is generally favorable. However, several acute complications have been reported such as congestive heart failure, cardiac rupture, hypotension, left ventricular apical thrombosis, or Torsade de Pointes. Several possible mechanisms such as multivessel coronary artery spasm, coronary microvascular dysfunction, myocarditis, or catecholamine toxicity have been proposed to explain tako-tsubo cardiomyopathy, but its pathophysiology is not well understood
Local polynomial regression for spatial data on
This paper develops a general asymptotic theory of local polynomial (LP)
regression for spatial data observed at irregularly spaced locations in a
sampling region . We adopt a stochastic sampling
design that can generate irregularly spaced sampling sites in a flexible manner
including both pure increasing and mixed increasing domain frameworks. We first
introduce a nonparametric regression model for spatial data defined on
and then establish the asymptotic normality of LP estimators
with general order . We also propose methods for constructing
confidence intervals and establish uniform convergence rates of LP estimators.
Our dependence structure conditions on the underlying processes cover a wide
class of random fields such as L\'evy-driven continuous autoregressive moving
average random fields. As an application of our main results, we discuss a
two-sample testing problem for mean functions and their partial derivatives.Comment: 45 page
Subsampling inference for nonparametric extremal conditional quantiles
This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance parameters in the limiting distribution of the local estimator. A simulation study and empirical example illustrate usefulness of our subsampling inference to investigate extremal phenomena
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