Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression...

Let $({\bf X}\sb{i},Y\sb{i}), i = 1,2,\cdots,$ be a random sample satisfying a logistic regression model; that is, for each i, log($P(Y\sb{i}$ = $1\vert{\bf X}\sb{i})/P(Y\sb{i}$ = 0$\vert{\bf X}\sb{i})\rbrack$ = ${\bf X}\sbsp{i}{T}\beta\sb0,$ where $Y\sb{i}\in\{$0,1$\},$ ${\bf X}\sb{i}\in{\bf...

Estimation of the maximum and minimum is considered in a random coefficient autoregressive model for bounded data. Limiting distributions and confidence intervals are obtained, for nonrandom sample sizes and also for a stopping rule designed to achieve sufficient precision of the estimates.

Random coefficient autoregressive processes with beta marginals provide a useful family of models for data that are both bounded and dependent over time. We obtain results on sample-size efficient sequential estimation of the mean for such processes.

The problem of determining the size of the training sample needed to achieve sufficiently small misclassification probability is considered. The appropriate sample size is approximated using a stopping rule. The proposed procedure is asymptotically optimal.

The problem of constructing a fixed width confidence band for the density of data observed subject to random right censoring is considered. A suitable stopping rule is proposed and this stopping rule is shown to achieve the desired degree of confidence, asymptotically as the required maximum...