Results 261 to 270 of about 45,179 (311)

Autoregressive gamma processes

Journal of Forecasting, 2005
We introduce a class of autoregressive gamma processes with conditional distributions from the family of noncentred gamma (up to a scale factor). The paper provides the stationarity and ergodicity conditions for ARG processes of any autoregressive order p, including long memory, and closed-form expressions of conditional moments.
Christian Gourieroux, Joann Jasiak
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Predictive discrimination for autoregressive processes

Pattern Recognition Letters, 1988
Summary: A predictive method for discriminating between distinct univariate autoregressive classes is derived. The predictive classification rule is derived for the case of known class order, and a rule is given for the case where the orders of the competing autoregressive processes are unknown.
Virgil R. Marco, Dean M. Young, Danny W. Turner   +2 more
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Generalized Autoregressive Gamma Processes

SSRN Electronic Journal, 2021
Nous présentons les processus gamma autorégressifs généralisés (GARG), une catégorie de processus autorégressifs et moyennes mobiles qui est un prolongement de la catégorie existante de processus gamma autorégressifs dans une dimension importante : la dynamique de chacun des moments conditionnels est influencée par une différente moyenne mobile ...
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Autoregressive processes

WIREs Computational Statistics, 2011
AbstractIn this article, the definition, properties, and applications of linear autoregressive processes (or autoregressions) are reviewed. These form an important subset of the class of autoregressive moving‐average (ARMA) processes which are widely used as stationary models for time series data.
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Identification of complex autoregressive processes

ICASSP '79. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
The problem of estimating the spectrum of a complex stochastic process observed in additive white noise is considered. The problem is reduced to one of parameter identification by assuming an autoregressive model for the process. An algorithm for estimating these parameters is derived. Examples are presented to illustrate the use of the algorithm.
J. A. Ponnusamy, Mandyam D. Srinath, Periagaram K. Rajasekaran   +2 more
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Testing proportionality for autoregressive processes

IEEE Transactions on Information Theory, 2003
Summary: We introduce a new hypothesis test to determine wether or not two autoregressive spectral densities are proportional. A test for autoregressive coefficient proportionality or randomness is deduced. We also derive the exact asymptotic behavior for these tests under parametric alternatives and show that, given a significance level, our tests are
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Spatially Varying Autoregressive Processes

Technometrics, 2011
We develop a class of models for processes indexed in time and space that are based on autoregressive (AR) processes at each location. We use a Bayesian hierarchical structure to impose spatial coherence for the coefficients of the AR processes. The priors on such coefficients consist of spatial processes that guarantee time stationarity at each point ...
Aline A. Nobre, Bruno Sansó, Alexandra M. Schmidt   +2 more
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On threshold autoregressive processes

Kybernetika, 1984
This paper is a precise study of a so-called threshold autoregressive process. To get an idea of such a process think of a first order AR process where the coefficient has one value when the state is positive and another in the remaining case. For such a model the stationary distribution of the state as well as the correlation function is determined ...
Jirí Andel, Ivan Netuka, Karel Zvára
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A Convolution-Based Autoregressive Process

2013
We propose a convolution-based approach to the estimation of nonlinear autoregressive processes. The model allows for state-dependent autocorrelation, that is different persistence of the shocks in different phases of the market and dependent innovations, that is drawn from different distributions in different phases of the market.
Cherubini U, Gobbi F
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Information rates of autoregressive processes

IEEE Transactions on Information Theory, 1970
Summary: The rate distortion function \(R(D)\) is calculated for two time-discrete autoregressive sources--the time-discrete Gaussian autoregressive source with a mean-square-error fidelity criterion and the binary-symmetric first-order Markov source with an average probability-of-error per bit fidelity criterion. In both cases it is shown that \(R(D)\)
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