Results 221 to 230 of about 10,511 (262)

Autoregressive Functions Estimation in Nonlinear Bifurcating Autoregressive Models

2015
Bifurcating autoregressive processes, which can be seen as an adaptation of au-toregressive processes for a binary tree structure, have been extensively studied during the last decade in a parametric context. In this work we do not specify any a priori form for the two autoregressive functions and we use nonparametric techniques.
Penda, Sim��on Val��re Bitseki, Olivier, Ad��la��de   +1 more
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A test of nonlinear autoregressive models

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
A study on testing the appropriateness of a particular structure selection and design for block-oriented nonlinear models is presented. Block-oriented nonlinear models characterize some features of Volterra kernels and extract only particular higher-order statistical information.
S.-Y. Mao, P.-X. Lin
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Estimating the Innovation Distribution in Nonlinear Autoregressive Models

Annals of the Institute of Statistical Mathematics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Schick, Anton, Wefelmeyer, Wolfgang
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BAYESIAN THRESHOLD AUTOREGRESSIVE MODELS FOR NONLINEAR TIME SERIES

Journal of Time Series Analysis, 1993
Abstract.This paper provides a Bayesian approach to statistical inference in the threshold autoregressive model for time series. The exact posterior distribution of the delay and threshold parameters is derived, as is the multi‐step‐ahead predictive density. The proposed methods are applied to the Wolfe's sunspot and Canadian lynx data sets.
Geweke, John, Terui, Nobuhiko
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A sparse multiscale nonlinear autoregressive model for seizure prediction

Journal of Neural Engineering, 2021
Abstract Objectives. Accurate seizure prediction is highly desirable for medical interventions such as responsive electrical stimulation. We aim to develop a classification model that can predict seizures by identifying preictal states, i.e.
Pen-Ning Yu, Charles Y Liu, Christianne N Heck, Theodore W Berger, Dong Song   +4 more
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JUMP DETECTION BY WAVELET IN NONLINEAR AUTOREGRESSIVE MODELS

Acta Mathematica Scientia, 1999
Summary: Wavelets are applied to detection of the jump points of a regression function in a nonlinear autoregressive model \(x_t=T (x_{t-1}) +\varepsilon_t\). By checking the empirical wavelet coefficient of the data, which have significantly large absolute values across fine scale levels, the number of the jump points and locations where the jumps ...
Li, Yuan, Xie, Zhongjie
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Nonlinear autoregressive models with heavy-tailed innovation

Science in China Series A, 2005
We discuss the relationship between the stationary marginal tail probability and the innovation's tail probability of nonlinear autoregressive models. We show that under certain conditions that ensure stationarity and ergodicity, the one-dimensional stationary marginal distributions have the heavy-tailed probability property with the same index as that
Jin, Yang, An, Hongzhi
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Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance

IEEE Transactions on Signal Processing, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lu, Sheng, Chon, Ki H.
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NON- AND SEMIPARAMETRIC IDENTIFICATION OF SEASONAL NONLINEAR AUTOREGRESSION MODELS

Econometric Theory, 2002
Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility. All procedures are based on either local constant or local linear estimation. For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and
Tschernig, R.J.V., Yang, L.
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Order Choice in Nonlinear Autoregressive Models

Statistics, 1995
An extensive literature has been devoted to the problem of order choice in autoregressive models. Most of alternative methods to hypothesis tests are based on the minimization of the Akaike Information Criterion (AIC) or on some of its variants. These methods have the main drawback to have to assume a parametric form for the autoregression function ...
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