4,289 research outputs found
Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-varying
smoothness in the mean and covariance process. In particular, there may be
certain time intervals exhibiting rapid changes and others in which changes are
slow. If such time-varying smoothness is not accounted for, one can obtain
misleading inferences and predictions, with over-smoothing across erratic time
intervals and under-smoothing across times exhibiting slow variation. This can
lead to mis-calibration of predictive intervals, which can be substantially too
narrow or wide depending on the time. We propose a locally adaptive factor
process for characterizing multivariate mean-covariance changes in continuous
time, allowing locally varying smoothness in both the mean and covariance
matrix. This process is constructed utilizing latent dictionary functions
evolving in time through nested Gaussian processes and linearly related to the
observed data with a sparse mapping. Using a differential equation
representation, we bypass usual computational bottlenecks in obtaining MCMC and
online algorithms for approximate Bayesian inference. The performance is
assessed in simulations and illustrated in a financial application
On parameter estimation for locally stationary long-memory processes
We consider parameter estimation for time-dependent locally stationary long-memory processes. The asymptotic distribution of an estimator based on the local infinite autoregressive representation is derived, and asymptotic formulas for the mean squared error of the estimator, and the asymptotically optimal bandwidth are obtained. In spite of long memory, the optimal bandwidth turns out to be of the order n^(-1/5) and inversely proportional to the square of the second derivative of d. In this sense, local estimation of d is comparable to regression smoothing with iid residuals.long memory, fractional ARIMA process, local stationarity, bandwidth selection
The spectral analysis of nonstationary categorical time series using local spectral envelope
Most classical methods for the spectral analysis are based on the assumption that the time
series is stationary. However, many time series in practical problems shows nonstationary
behaviors. The data from some fields are huge and have variance and spectrum which changes
over time. Sometimes,we are interested in the cyclic behavior of the categorical-valued time
series such as EEG sleep state data or DNA sequence, the general method is to scale the
data, that is, assign numerical values to the categories and then use the periodogram to find
the cyclic behavior. But there exists numerous possible scaling. If we arbitrarily assign the
numerical values to the categories and proceed with a spectral analysis, then the results will
depend on the particular assignment. We would like to find the all possible scaling that
bring out all of the interesting features in the data. To overcome these problems, there have
been many approaches in the spectral analysis.
Our goal is to develop a statistical methodology for analyzing nonstationary categorical
time series in the frequency domain. In this dissertation, the spectral envelope methodology
is introduced for spectral analysis of categorical time series. This provides the general
framework for the spectral analysis of the categorical time series and summarizes information
from the spectrum matrix. To apply this method to nonstationary process, I used the
TBAS(Tree-Based Adaptive Segmentation) and local spectral envelope based on the piecewise
stationary process. In this dissertation,the TBAS(Tree-Based Adpative Segmentation)
using distance function based on the Kullback-Leibler divergence was proposed to find the
best segmentation
A test for second-order stationarity of time series based on unsystematic sub-samples
In this paper, we introduce a new method for testing the stationarity of time
series, where the test statistic is obtained from measuring and maximising the
difference in the second-order structure over pairs of randomly drawn
intervals. The asymptotic normality of the test statistic is established for
both Gaussian and a range of non-Gaussian time series, and a bootstrap
procedure is proposed for estimating the variance of the main statistics.
Further, we show the consistency of our test under local alternatives. Due to
the flexibility inherent in the random, unsystematic sub-samples used for test
statistic construction, the proposed method is able to identify the intervals
of significant departure from the stationarity without any dyadic constraints,
which is an advantage over other tests employing systematic designs. We
demonstrate its good finite sample performance on both simulated and real data,
particularly in detecting localised departure from the stationarity
Inference of time-varying regression models
We consider parameter estimation, hypothesis testing and variable selection
for partially time-varying coefficient models. Our asymptotic theory has the
useful feature that it can allow dependent, nonstationary error and covariate
processes. With a two-stage method, the parametric component can be estimated
with a -convergence rate. A simulation-assisted hypothesis testing
procedure is proposed for testing significance and parameter constancy. We
further propose an information criterion that can consistently select the true
set of significant predictors. Our method is applied to autoregressive models
with time-varying coefficients. Simulation results and a real data application
are provided.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1010 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Covariance matrix estimation for stationary time series
We obtain a sharp convergence rate for banded covariance matrix estimates of
stationary processes. A precise order of magnitude is derived for spectral
radius of sample covariance matrices. We also consider a thresholded covariance
matrix estimator that can better characterize sparsity if the true covariance
matrix is sparse. As our main tool, we implement Toeplitz [Math. Ann. 70 (1911)
351-376] idea and relate eigenvalues of covariance matrices to the spectral
densities or Fourier transforms of the covariances. We develop a large
deviation result for quadratic forms of stationary processes using m-dependence
approximation, under the framework of causal representation and physical
dependence measures.Comment: Published in at http://dx.doi.org/10.1214/11-AOS967 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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