Functional model selection for InSAR time series

2016 ◽  
Author(s):  
Ling Chang ◽  
Ramon F. Hanssen
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Functional Model ◽  
Selection For

Functional Model Selection for Sparse Binary Time Series with Multiple Inputs

2012 ◽  
pp. 477-497 ◽  
Author(s):  
Catherine Tu ◽  
Dong Song ◽  
F Breidt ◽  
Theodore Berger ◽  
Haonan Wang
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Functional Model ◽  
Binary Time Series ◽  
Selection For

scholarly journals Model Selection for Broadband Semiparametric Estimation of Long Memory in Time Series

2001 ◽  
Vol 22 (6) ◽  
pp. 679-709 ◽  
Author(s):  
Clifford M. Hurvich
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Long Memory ◽  
Semiparametric Estimation ◽  
Selection For

On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods

2021 ◽  
pp. 1471082X2110347
Author(s):  
Panagiota Tsamtsakiri ◽  
Dimitris Karlis
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Bayesian Model ◽  
Linear Models ◽  
Simulation Experiment ◽  
Mean Values ◽  
The Past ◽  
Competing Models ◽  
Selection For ◽  
Log Linear

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.

scholarly journals Estimation of Lyapunov spectrum and model selection for a chaotic time series

2012 ◽  
Vol 36 (12) ◽  
pp. 6090-6099
Author(s):  
Qinglan Li ◽  
Pengcheng Xu
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Chaotic Time Series ◽  
Lyapunov Spectrum ◽  
Selection For

Model selection for infinite variance time series

1995 ◽  
Vol 24 (4) ◽  
pp. 889-910 ◽  
Author(s):  
R.H. Glendinning
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Infinite Variance ◽  
Selection For

Bayesian Subset Model Selection for Time Series

2004 ◽  
Vol 25 (5) ◽  
pp. 671-690 ◽  
Author(s):  
N. K. Unnikrishnan
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Selection For

scholarly journals On Model Selection for Scalable Time Series Forecasting in Transport Networks

2021 ◽  
pp. 1-10
Author(s):  
Julien Monteil ◽  
Anton Dekusar ◽  
Claudio Gambella ◽  
Yassine Lassoued ◽  
Martin Mevissen
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Forecasting ◽  
Transport Networks ◽  
Selection For
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