Description Usage Arguments Details Value
This function computes the matrices Q_i, such that sequential outlier detection using Cook's distance is equivalent to \bigcap_{i \in I} y^T Q_i y ≥ 0.
1  constrInResponseCookSeq(n, p, PX, PXperp, obs.ordered, numOfOutlier)

n, 
the number of observations. 
p, 
the number of variables, including the intercept. 
PX, 
the projection matrix onto the column space of the design matrix X. 
PXperp, 

obs.ordered, 
the index of observations with their cook's distance in the decreasing order. 
numOfOutlier, 
the number of outliers assumed, must be between 0 and n. 
Using Cook's distance sequentially and assume there are k outliers, the ith data is considered as an outlier if and only if its Cook's distance ranks in topk among all, Then we can characterize this "detection event" as an intersection of quadratic constraints in the response y by \bigcap_{i \in I} y^T Q_i y ≥ 0, where I is a finite index set.
This function returns a list of matrices Q_i.
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