Results 281 to 290 of about 45,179 (311)

Autoregressive processes in optimization

Journal of Applied Probability, 1988
Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e.
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Process Control for the Vector Autoregressive Model

Quality and Reliability Engineering International, 2012
Multivariate monitoring techniques for serially correlated observations have been widely used in various applications. This study examines several issues that have arisen in relation to the statistical quality control for the vector autoregressive (VAR) model, using a Monte Carlo approach.
Cheng, T.-C., Hsieh, P.-H., Yang, S.-F.
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Mismatched DPCM encoding of autoregressive processes

IEEE Transactions on Information Theory, 1990
A method for computing the mean squared error distortion of differential pulse code modulation (DPCM) applied to Gaussian autoregressive sources is developed. This extends previous work wherein the code predictor was matched to the source. A two-dimensional version of the projection method for the computation of the stationary distribution of the joint
Morteza Naraghi-Pour, David L. Neuhoff
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A contribution of bootstrapping autoregressive processes

Kybernetika, 1995
Summary: A sequence of random vectors of elements which depend on time-delayed observations of an autoregressive process is considered and the distribution of smooth functions of the sample mean of such vectors is studied asymptotically. Both classical approximation based on the Edgeworth expansion and the bootstrap distribution are developed.
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Vector Autoregressive Processes

2007
The previous chapter presented a statistical approach to analyse the relations between time series: starting with univariate models, we asked for relations that might exist between two time series. Subsequently, the approach was extended to situations with more than two time series.
Gebhard Kirchgässner, Jürgen Wolters
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Estimation in autoregressive processes with partial observations

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
We consider the problem of estimating the covariance matrix and the transition matrix of vector autoregressive (VAR) processes from partial measurements. This model encompasses settings where there are limitations in the data acquisition of the underlying measurement systems so that data is lost or corrupted by noise.
Milind Rao, Tara Javidi, Yonina C. Eldar, Andrea J. Goldsmith   +3 more
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Autoregressive Processes

2022
Manfred Deistler, Wolfgang Scherrer
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Generalized Autoregressive Processes

2017
Most proofs in this chapter are more involved and are therefore omitted or simplified. For literature and detailed proofs see e.g. Berkes et al. (2003, 2004), Bollerslev (1986), Bougerol and Picard (1992a,b), Brandt (1986), Breiman (1968), Brockwell and Cline (1985), Caines (1988), Furstenberg and Kesten (1960), Giraitis et al.
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A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials

Machines, 2021
Ning Chen, Jiayao Chen, Zhiwen Chen
exaly  

Upsampled Vector Autoregressive Processes

2025 33rd European Signal Processing Conference (EUSIPCO)
Emilio Ruiz-Moreno, Luis Miguel Lopez-Ramos, Baltasar Beferull-Lozano   +2 more
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