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V. K. Jandhyala, P. Liu, S. B. Fotopoulos, and I. B. MacNeill

Gaussian series and in the parameter of an exponential distribution. In pursuit of performing change-point analysis of radiosonde temperature series observed at four layers of the earth’s atmosphere, we first develop in this paper the large sample distribution of the change-point MLE when a change has occurred in both the mean vector and the covariance matrix of a multivariate Gaussian series. Then, we extend the Bayesian conjugate posterior derived by Perreault et al. (2000c) for change in the

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Chansoo Kim, Myoung-Seok Suh, and Ki-Ok Hong

studies have been conducted regarding the change point of extreme precipitation, especially on subdaily-scale precipitation in recent years. The goal of this study was to investigate the existence of a change point in the area-averaged annual maximum precipitation ( A3MP ) for six accumulated time periods (1, 3, 6, 12, 24, and 48 h) using Bayesian changepoint analysis during a 30-year period (1976–2005) over South Korea. When we consider the fact that the majority of disasters related to heavy

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Xin Zhao and Pao-Shin Chu

: Bayesian changepoint analysis of tropical cyclone activity: The central North Pacific case. J. Climate , 17 , 4893 – 4901 . Chu , P-S. , X. Zhao , M. Grubbs , and Y. Ruan , 2009 : Extreme rainfall events in the Hawaiian Islands. J. Appl. Meteor. Climatol. , 48 , 502 – 516 . Deser , C. A. , S. Philips , and J. W. Hurrell , 2004 : Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Climate , 17

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Anuradha P. Hewaarachchi, Yingbo Li, Robert Lund, and Jared Rennie

analysis ( Potter 1981 ; Vincent 1998 ; Caussinus and Mestre 2004 ; Menne and Williams 2005 , 2009 ; Lu and Lund 2007 ). The changepoint locations and mean shift sizes need to be estimated to make accurate inferences from the data; in fact, Lund et al. (2001) show that changepoint information is the single most important data feature to account for when reliably estimating a temperature trend at a fixed U.S. station. Once the changepoint times are identified, most other statistical inference

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Yingbo Li and Robert Lund

necessarily induces a true shift in the series. This said, time changes reported in the metadata record seem more likely to be true changepoint times. The typical objective of a changepoint analysis is to identify how many changepoints a series has and where they occur. The appropriate statistical methods to handle documented and undocumented changepoint times radically differ ( Lund and Reeves 2002 ). To appreciate the issue, consider a record of n = 100 annualized temperatures X 1 , …, X n and

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Su Yang, Xiaolan L. Wang, and Martin Wild

. The dataset used in this study is described in section 2 , whereas the method and an example of the inhomogeneity test and adjustment are presented in section 3 . The results of changepoints and their causes and the trend analysis, including the national average SSR series for China, are shown and discussed in section 4 . Finally, conclusions of this study are presented in section 5 . 2. Data Monthly SSR and SSD data and the related metadata at Chinese routine weather stations were released by

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Jaechoul Lee, Shanghong Li, and Robert Lund

from Jacksonville. To obtain changepoints in the monthly average series, a Gaussian MDL analysis akin to that in Lu et al. (2010) was used—a reference series was again constructed from the 40 most correlated neighbors. Table 2 lists our findings. The metadata identify station location or temperature gauge changes in 1895, 1927, 1962, and 1974. There is no metadata record after 1986. The MDL analysis of the monthly averaged series estimates changepoints in 1900, 1931, 1941, 1961, and 1970

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Colin Gallagher, Robert Lund, and Michael Robbins

variance margin . Using this in (2.4) and reworking the numerator into a CUSUM form provides Since c is not known a priori and n is constant, our changepoint statistic is taken as a maximum of the squared values multiplied by n : The statistic C * is also a Gaussian likelihood ratio statistic. This means that if one assumes a Gaussian distribution for (ε t ) and performs a statistical likelihood analysis, then C * arises. In general, likelihood ratio statistics can be difficult to compute, but

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Andrea Toreti, Franz G. Kuglitsch, Elena Xoplaki, and Jürg Luterbacher

precipitation extremes in Europe analyzed for the period 1901–2000 . J. Geophys. Res. , 111 , D22106 , doi:10.1029/2006JD007103 . Pawson , S. , K. Labitzke , and S. Leder , 1998 : Stepwise changes in stratospheric temperature . Geophys. Res. Lett. , 25 , 2157 – 2160 . Perreault , L. , J. Bernier , B. Bobee , and E. Parent , 2000 : Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited . J. Hydrol. , 235 , 221 – 241 . Peterson , T

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Pierre G. F. Gérard-Marchant, David E. Stooksbury, and Lynne Seymour

is not reported in the table, the corresponding S n ,ref is estimated by linear interpolation between the closest reported values. b. Extension to multiple changepoints Lund and Reeves (2002) suggested a simple application of their method for the identification of multiple changepoints. Once a changepoint has been identified at time k , estimates x̂ are calculated on both intervals [1, k ] and [ k + 1, n ] with (2) , and subtracted from the initial series x . The previous analysis is

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