Clustering Curves based on Change point analysis : A Nonparametric Bayesian Approach

Author(s):  
Sarat C. Dass ◽  
Chae Young Lim ◽  
Tapabrata Maiti ◽  
Zhen Zhang
2004 ◽  
Vol 17 (24) ◽  
pp. 4893-4901 ◽  
Author(s):  
Pao-Shin Chu ◽  
Xin Zhao

Abstract Bayesian analysis is applied to detect change points in the time series of annual tropical cyclone counts over the central North Pacific. Specifically, a hierarchical Bayesian approach involving three layers—data, parameter, and hypothesis—is formulated to demonstrate the posterior probability of the shifts throughout the time from 1966 to 2002. For the data layer, a Poisson process with gamma distributed intensity is presumed. For the hypothesis layer, a “no change in the intensity” hypothesis and a “single change in the intensity” hypothesis are considered. Results indicate that there is a great likelihood of a change point on tropical cyclone rates around 1982, which is consistent with earlier work based on a simple log-linear regression model. A Bayesian approach also provides a means for predicting decadal tropical cyclone variations. A higher number of tropical cyclones is predicted in the next decade when the possibility of the change point in the early 1980s is taken into account.


Author(s):  
Aviral Kumar Tiwari ◽  
Cleiton Guollo Taufemback ◽  
Satish Kumar

Psychometrika ◽  
2015 ◽  
Vol 81 (4) ◽  
pp. 1118-1141 ◽  
Author(s):  
Can Shao ◽  
Jun Li ◽  
Ying Cheng

2018 ◽  
Vol 11 (2) ◽  
pp. 420-433 ◽  
Author(s):  
Adem Yavuz Sönmez ◽  
Semih Kale

Abstract The main purpose of this study was to estimate possible climate change effects on the annual streamflow of Filyos River (Turkey). Data for annual streamflow and climatic parameters were obtained from streamflow gauging stations on the river and Bartın, Karabük, Zonguldak meteorological observation stations. Time series analysis was performed on 46 years of annual streamflow data and 57 years of annual mean climatic data from three monitoring stations to understand the trends. Pettitt change-point analysis was applied to determine the change time and trend analysis was performed to forecast trends. To reveal the relationship between climatic parameters and streamflow, correlation tests, namely, Spearman's rho and Kendall's tau were applied. The results of Pettitt change-point analysis pointed to 2000 as the change year for streamflow. Change years for temperature and precipitation were detected as 1997 and 2000, respectively. Trend analysis results indicated decreasing trends in the streamflow and precipitation, and increasing trend in temperature. These changes were found statistically significant for streamflow (p < 0.05) and temperature (p < 0.01). Also, a statistically significant (p < 0.05) correlation was found between streamflow and precipitation. In conclusion, decreasing precipitation and increasing temperature as a result of climate change initiated a decrease in the river streamflow.


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