Journal ArticleDOI
Two recursive estimates of autoregressive models based on maximum likelihood
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TLDR
In this paper, a recursive algorithm for computing the resulting estimates for increasing model orders is presented, which is more economical than standard solutions using Gaussian elimination, for example, for high order model fitting, and can be regarded as a special case of the algorithm presented here.Abstract:
Estimates for autoregressive models are obtained by approximating the maximum likelihood estimates in two ways. A recursive algorithm for computing the resulting estimates for increasing model orders is presented. To calculate a pth order estimate 0(p 2) arithmetic operations are required; hence for high order model fitting, the method is more economical than standard solutions using Gaussian elimination, for example. The Levinson–Durbin recursions for the Yule-Walker estimates can be regarded as a special case of the algorithm presented here.read more
Citations
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Book ChapterDOI
Chapter 1 - Spectrum Parameter Estimation in Time Series Analysis†
K.O. Dzhaparidze,A.M. Yaglom +1 more
TL;DR: In this paper, a parametric spectral estimate based on mixed autoregressive-moving average models is proposed for time series, which can be used for diagnostic checking to examine the agreement between the model and the available data, and apply some goodness-of-fit tests.
Journal ArticleDOI
Recursive maximum likelihood estimation of autoregressive processes
TL;DR: In this article, an autoregressive parameter estimator for short data records and/or sharply peaked spectra is presented. The technique is a closer approximation to the true maximum likelihood estimator than that obtained using linear prediction techniques, and it operates in a recursive model order fashion, which allows one to successively fit higher order models to the data.
Journal ArticleDOI
Recursion in short-time signal analysis
TL;DR: It is shown that most of the commonly used features (mean value, energy, autocorrelation function, DFT, Z-transform, entropy, etc.) satisfy these analytic expressions.
Journal ArticleDOI
Point and interval estimation of pollinator importance: a study using pollination data of Silene caroliniana
TL;DR: A Monte Carlo simulation study and a result from mathematical statistics on the variance of the product of two random variables are used to estimate the mean and confidence limits of pollinator importance for three visitor species of the wildflower, Silene caroliniana.
Journal ArticleDOI
Properties and applications of Gaussian autoregressive processes in detection theory (Corresp.)
TL;DR: A sufficient statistic, having dimension 3p+1 , is constructed for p th order stationary Gaussian autoregressive processes and a computationally efficient discriminator based on the statistic is obtained.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Journal ArticleDOI
An exact recursion for the composite nearest‐neighbor degeneracy for a 2×N lattice space
R. B. McQuistan,J. L. Hock +1 more
TL;DR: In this paper, a set theoretic argument is used to develop a recursion relation that yields exactly the composite nearest-neighbor degeneracy for simple, indistinguishable particles distributed on a 2×N lattice space.