Multivariate Analysis, Design of Experiments, and Survey Sampling

Subir Ghosh

• Sampling designs and prediction methods for Gaussian spatial processes; design techniques for probabilistic sampling of items with variable monetary value; small area estimation -a Bayesian perspective; Bayes sampling designs for selection procedures; cluster coordinated composites of diverse datasets on several spatial scales for designing extensive environmental sample surveys - prospectus on promising protocols; corrected confidence sets for sequentially designed experiments, II -examples; resampling marked point processes; graphical Markov models in multivariate analysis; robust regression with censored and truncated data; multivariate calibration; some consequences of random effects in multivariate survival models; a unified methodology for constructing multivariate autoregressive models; statistical model evaluation and information criteria; multivariate rank tests; asymptotic expansions of the distribution of some test statistics for elliptical populations; a review of variance estimators with extensions to multivariate nonparametric regression models; on affine invariant sign and rank tests in one and two sample multivariate problems; correspondence and component analysis; growth curve models; dealing with uncertainties in queues and networks of queues; optimal Bayesian design for a logistic regression model - geometric and algebraic approaches; structure of weighing mattrices of small order and weight.

"...provides a survey of the recent developments of statistical methods."---Acta Vet., 2000