We develop a technique for record linkage on high dimensional data, where the two datasets may not have any common variable, and there may be no training set available. Our methodology is based on sparse, high dimensional principal components. Since large and high dimensional datasets are often...

We develop a technique for record linkage on high dimensional data, where the two datasets may not have any common variable, and there may be no training set available. Our methodology is based on sparse, high dimensional principal components. Since large and high dimensional datasets are often...

Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control at a given time....

High dimensional data routinely arises in image analysis, genetic experiments, network analysis, and various other research areas. Many such datasets do not correspond to well-studied probability distributions, and in several applications the data-cloud prominently displays non-symmetric and...

For estimators of parameters defined as minimisers of Q([theta])=Ef([theta],X), we study the asymptotic and generalised bootstrap properties. We concentrate on the case where Q does not have adequate smoothness for standard analysis to work. We describe the properties required by Q as well as...

In this paper we have three main results. (a) We show that all jackknife schemes are special cases of generalised bootstrap. (b) We introduce a new generalised bootstrap technique called DBS to estimate the mean-squared error of the least-squares estimate in linear models where the number of...