Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models

2016 ◽  
Vol 58 (5) ◽  
pp. 1113-1137 ◽  
Author(s):  
Philipp Otto ◽  
Wolfgang Schmid
2020 ◽  
Author(s):  
Miranda J. Fix ◽  
Daniel S. Cooley ◽  
Emeric Thibaud

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 18
Author(s):  
Mária Ďurigová ◽  
Dominika Ballová ◽  
Kamila Hlavčová

Detailed analyses of hydrological data are necessary in order to prove changes in their character. This article focuses on an analysis of the average monthly discharges of 14 stage-discharge gauging stations in Slovakia. The measured period is from 1931 to 2016. The approaches used are hydrological exploration methods, which were created by hydrologists to describe the behavior of hydrological time series. The methods are used to identify a change-point using an analysis of any residuals, the Pettitt test, and an analysis of the relationship between the mean annual discharge deviations from the long-term annual discharge and the deviations of the average monthly discharge from the long-term average monthly discharge. A considerable number of change-points were identified in the 1970s and 1980s. The results of the analyses show changes in the hydrological regimes, but to confirm the accuracy of the outcomes, it is also necessary to examine other hydrological and meteorological elements such as, e.g., precipitation and the air temperature.


2019 ◽  
Vol 139 (3-4) ◽  
pp. 849-859 ◽  
Author(s):  
Tímea Kocsis ◽  
Ilona Kovács-Székely ◽  
Angéla Anda

Abstract This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.


Bernoulli ◽  
2017 ◽  
Vol 23 (2) ◽  
pp. 1408-1447 ◽  
Author(s):  
S. Chakar ◽  
E. Lebarbier ◽  
C. Lévy-Leduc ◽  
S. Robin

2012 ◽  
Vol 18 (65) ◽  
pp. 323
Author(s):  
جنان عباس ناصر

In this study, we compare between the traditional Information Criteria (AIC, SIC, HQ, FPE) with The Modified Divergence Information Criterion (MDIC) which used to determine the order of Autoregressive model (AR) for the data generating process, by using the simulation by generating data from several of Autoregressive models, when the error term is normally distributed with different values for its parameters (the mean and the variance),and for different sample  sizes.


2002 ◽  
Vol 17 (2) ◽  
pp. 289-296 ◽  
Author(s):  
Joe H. Sullivan
Keyword(s):  

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