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Limit theory for the sample autocorrelations and extremes of a GARCH(1,1) process
, 1998
"... The asymptotic theory for the sample autocorrelations and extremes of a GARCH(1; 1) process is provided. Special attention is given to the case when the sum of the ARCH and GARCH parameters is close to one, i.e. when one is close to an infinite variance marginal distribution. This situation has been ..."
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Cited by 93 (20 self)
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The asymptotic theory for the sample autocorrelations and extremes of a GARCH(1; 1) process is provided. Special attention is given to the case when the sum of the ARCH and GARCH parameters is close to one, i.e. when one is close to an infinite variance marginal distribution. This situation has
The sample autocorrelations of financial time series models
"... In this chapter we review some of the limit theory for the sample autocorrelation function (ACF) of linear and nonlinear processes f(x) with regularly varying finitedimensional distributions. We focus in particular on nonlinear process models which have attracted the attention for modeling financ ..."
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Cited by 10 (6 self)
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In this chapter we review some of the limit theory for the sample autocorrelation function (ACF) of linear and nonlinear processes f(x) with regularly varying finitedimensional distributions. We focus in particular on nonlinear process models which have attracted the attention for modeling
(ARMA) Model, Extended Sample Autocorrelation Function (ESACF).
"... This research is aimed at presenting a new, pattern recognitionbased DSS scheme for the time series model identification. The scheme is based on two principles: pattern matching and inductive learning. Pattern matching is used to classify a pattern of the time series into one of the autoregressiv ..."
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of the autoregressive movingaverage models. The pattern is obtained from the extended sample autocorrelations of the time series. Inductive learning is used to enhance the capability of recognizing input patterns, and linear discriminants are used to discriminate one pattern from the others. To implement he idea, a
POINT PROCESS CONVERGENCE OF STOCHASTIC VOLATILITY PROCESSES WITH APPLICATION TO SAMPLE AUTOCORRELATION
, 2001
"... The paper considers one of the standard processes for modeling returns in finance, the stochastic volatility process with regularly varying innovations. The aim of the paper is to show how point process techniques can be used to derive the asymptotic behavior of the sample autocorrelation function o ..."
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Cited by 27 (17 self)
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The paper considers one of the standard processes for modeling returns in finance, the stochastic volatility process with regularly varying innovations. The aim of the paper is to show how point process techniques can be used to derive the asymptotic behavior of the sample autocorrelation function
LPC interpolation by approximation of the sample autocorrelation function
 IEEE Trans. Speech Audio Processing
, 1998
"... Abstractâ€”Conventionally, the energy of analysis frames is not taken into account for linear prediction (LPC) interpolation. Incorporating the frame energy improves the subjective quality of interpolation, but increases the spectral distortion (SD). The main reason for this discrepancy is that the ou ..."
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Cited by 7 (1 self)
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is that the outliers are increased in low energy parts of segments with rapid changes in energy. The energy is most naturally combined with a normalized autocorrelation representation. Index Termsâ€”LPC interpolation, speech coding. I.
SOME PROPERTIES OF THE FINITE TIME SAMPLE AUTOCORRELATION OF THE ELECTROENCEPHALOGRAM
, 1959
"... One goal of quantitative studies of physical phenomena consists in transforming a set of measured variables into another set that will describe the phenomenon under investigation in terms of meaningful parameters. Most analyses of brain waves by means of autocorrelation functions that have been carr ..."
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examined in the present investigation which concerned itself with the problem of estimating the autocorrelation function of the EEG from a finite sample of the EEG time series. A narrowband, Gaussian noise model was assumed in order to study the errors that arise from the estimation of the autocorrelation
Management Science'). Sample Autocorrelation Learning in a Capital Market Model
, 1999
"... Adaptive agent models are supposed to result in the same limit behavior as models with perfectly rational agents. In this article we show that this claim cannot by accepted in general, even in a simple capital market model, where the agents apply sample autocorrelation learning to perform their for ..."
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Adaptive agent models are supposed to result in the same limit behavior as models with perfectly rational agents. In this article we show that this claim cannot by accepted in general, even in a simple capital market model, where the agents apply sample autocorrelation learning to perform
An asymptotically pivotal transform of the residuals sample autocorrelations with application in model checking
 Journal of American Statistical Association
"... We propose an asymptotically distributionfree transform of the sample autocorrelations of residuals in general parametric time series models, possibly nonlinear in variables. The residuals autocorrelation function is the basic model checking tool in time series analysis, but it is not useful when i ..."
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Cited by 2 (1 self)
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We propose an asymptotically distributionfree transform of the sample autocorrelations of residuals in general parametric time series models, possibly nonlinear in variables. The residuals autocorrelation function is the basic model checking tool in time series analysis, but it is not useful when
A NOTE ON THE LIMITING DISTRIBUTION OF SAMPLE AUTOCORRELATIONS IN THE PRESENCE OF A UNIT ROOT by
, 1990
"... vrije Universiteit amsterdam ..."
MODAL DISTRIBUTION SYNTHESIS FROM SUBSAMPLED AUTOCORRELATION FUNCTION
"... The problem of signal synthesis from bilinear timefrequency representations such as the Wigner distribution has been investigated [1,2,4] using methods which exploit an outerproduct interpretation of these distributions. The Modal distribution is a timefrequency distribution specifically designed ..."
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The problem of signal synthesis from bilinear timefrequency representations such as the Wigner distribution has been investigated [1,2,4] using methods which exploit an outerproduct interpretation of these distributions. The Modal distribution is a timefrequency distribution specifically designed to model the quasiharmonic, multisinusoidal, nature of music signals and belongs to the Cohen general class of timefrequency distributions. Existing methods of synthesis from the Modal distribution [3] are based on a sinusoidalanalysissynthesis procedure using estimates of instantaneous frequency and amplitude values. In this paper we develop an innovative synthesis procedure for the Modal distribution based on the outerproduct interpretation of bilinear timefrequency distributions. We also propose a streaming objectoriented implementation of the resynthesis in the SndObj library [6] based on previous work which implemented a streaming implementation of the Modal distribution [7]. The theoretical background to the Modal distribution and to signal synthesis of Wigner distributions is first outlined followed by an explanation of the design and implementation of the Modal distribution synthesis. Suggestions for future extensions to the synthesis procedure are given. 1.
Results 1  10
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