
handle: 10525/2183
2002 Mathematics Subject Classification: 62M10. We consider development of daily precipitation models based on [3] for some sites in Bulgaria. The precipitation process is modelled as a two-state first-order nonstationary Markov model. Both the probability of rainfall occurrance and the rainfall intensity are allowed depend on the intensity on the preceeding day. To investigate the existence of long-term trend and of changes in the pattern of seasonal variation we use a synthesis of the methodology presented in [3] and the idea behind the classical running windows technique for data smoothing. The resulting time series of model parameters are used to quantify changes in the precipitation process over the territory of Bulgaria.
Gamma Time Series, Time series, auto-correlation, regression, etc. in statistics (GARCH), Climate Change, Rainfall Modeling, Binary Time Series, Markov Chain, Applications of statistics to environmental and related topics, Generalized Linear Models
Gamma Time Series, Time series, auto-correlation, regression, etc. in statistics (GARCH), Climate Change, Rainfall Modeling, Binary Time Series, Markov Chain, Applications of statistics to environmental and related topics, Generalized Linear Models
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