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We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results.(More)
The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible(More)
The paper considers the problem of testing for multiple outliers in a regression model and provides fast approximations to the null distribution of the minimum deletion residual used as a test statistic. Since direct simulation of each combination of number of observations and number of parameters is too time consuming, methods using simple normal samples(More)
Setting of process variables to meet a required specification of quality characteristic (or response variable) in a process, is one of the common problems in the process quality control. But generally there are more than one quality characteristics in the process and the experimenter attempts to optimize all of them simultaneously. Since response variables(More)
The optimum design of experiments for nonlinear models requires parameter sensitivities, that is the derivatives of the response with respect to the parameters. If the differential equations forming the kinetic model do not have an analytical solution, numerical derivatives have to be used. We describe the " direct " method for calculating the sensitivities(More)
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust regression rely on selecting numerous subsamples of the data. We describe new algorithms for LMS and LTS estimators that have increased efficiency due to improved combinatorial sampling. These and other publicly available algorithms are compared for outlier(More)