Description Usage Arguments Details Value References See Also Examples
View source: R/CPSPPOTevents.fun.R
This function calculates the occurrence times and other characteristics (length, maximum and mean intensity) of the extreme events of the three indicator processes of a bivariate Common Poisson shock process (CPSP) obtained from a POT approach.
1  CPSPPOTevents.fun(X, Y, thresX, thresY, date = NULL)

X 
Numeric vector. Series (x_i) whose threshold exceedances define the first CPSP marginal process. 
Y 
Numeric vector. Series (y_i) whose threshold exceedances define the second CPSP marginal process. 
thresX 
Numeric value. Threshold used to define the extreme events in (x_i). 
thresY 
Numeric value. Threshold used to define the extreme events in (y_i). 
date 
Optional. A vector or matrix indicating the date of each observation. 
A CPSP N can be decomposed into three independent indicator processes: N_{(1)}, N_{(2)} and N_{(12)}, the processes of the points occurring only in the first marginal process, only in the second and in both of them (simultaneous points). In the CPSP resulting from applying a POT approach, the events in N_{(1)} are a run of consecutive days where (x_i) exceeds its extreme threshold but (y_i) does not exceed its extreme threshold. An extreme event in N_{(2)} is defined analogously. A simultaneous event, or event in N_{(12)}, is a run where both series exceed their thresholds.
For the events defined in each indicator process, three magnitudes (length, maximum intensity and mean intensity) are calculated together with the initial point and the point of maximum excess of each event. In the N_{(12)}, the maximum and the mean intensity of both (x_i) and (y_i) are also calculated. The occurrence point of each event is located at the time of the maximum sum of the excesses over the threholds (where an excess is 0 if the observation does not exceed its corresponding threshold). According to this definition, the occurrence point in N_{(1)} is the point with maximum intensity in (x_i) within the run.
The vectors inddatX
, inddatY
and inddatXY
, elements of the output list,
mark the observations that should be used in the estimation of each indicator process. The
observations in an extreme event which are not the occurrence point are marked with 0
and treated as non observed in the estimation process. The rest are marked with 1 and
must be included in the likelihood function.
A list with components
ImX 
Vector of mean excesses (over the threshold) of the extreme events in N_{(1)}. 
IxX 
Vector of maximum excesses (over the threshold) of the extreme events in N_{(1)}. 
LX 
Vector of lengths of the extreme events in N_{(1)}. 
PxX 
Vector of points of maximum excess of the extreme events in N_{(1)}. 
PiX 
Vector of initial points of the extreme events in N_{(1)}. 
inddatX 
Index equal to 1 in the observations which should be used in the estimation process of N_{(1)}, and to 0 in the others. 
ImY 
Vector of mean excesses (over the threshold) of the extreme events in N_{(2)}. 
IxY 
Vector of maximum excesses (over the threshold) of the extreme events in N_{(2)}. 
LY 
Vector of lengths of the extreme events in N_{(2)}. 
PxY 
Vector of points of maximum excess of the extreme events in N_{(2)}. 
PiY 
Vector of initial points of the extreme events in N_{(2)}. 
inddatY 
Index equal to 1 in the observations which should be used in the estimation process of N_{(2)} and to 0 in the others. 
ImXYx 
Vector of mean excesses of the series (x_i) in N_{(12)}. 
IxXYx 
Vector of maximum excesses the series (x_i) in N_{(12)}. 
ImXYy 
Vector of mean excesses of the series (y_i) in N_{(12)}. 
IxXYy 
Vector of maximum excesses the series (y_i) in N_{(12)}. 
LXY 
Vector of lengths of the extreme events in N_{(12)}. 
PxXY 
Vector of points of maximum excess of the extreme events in N_{(12)}. 
PiXY 
Vector of initial points of the extreme events in N_{(12)}. 
inddatXY 
Index equal to 1 in the observations which should be used in the estimation process of N_{(12)} and to 0 in the others. 
X 
Input argument. 
Y 
Input argument. 
thresX 
Input argument. 
thresY 
Input argument. 
date 
Input argument. 
Abaurrea, J. Asin, J. and Cebrian, A.C. (2015). A Bootstrap Test of Independence Between Three Temporal Nonhomogeneous Poisson Processes and its Application to Heat Wave Modeling. Environmental and Ecological Statistics, 22(1), 127144.
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