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The Jolly-Seber method has been the traditional approach to the estimation of demographic parameters in long-term capture-recapture studies of wildlife and fish species. This method involves restrictive assumptions about capture probabilities that can lead to biased estimates, especially of population size and recruitment. Pollock (1982, Journal of Wildlife Management 46, 752-757) proposed a sampling scheme in which a series of closely spaced samples were separated by longer intervals such as a year. For this "robust design," Pollock suggested a flexible ad hoc approach that combines the Jolly-Seber estimators with closed population estimators, to reduce bias caused by unequal catchability, and to provide estimates for parameters that are unidentifiable by the Jolly-Seber method alone. In this paper we provide a formal modelling framework for analysis of data obtained using the robust design. We develop likelihood functions for the complete data structure under a variety of models and examine the relationship among the models. We compute maximum likelihood estimates for the parameters by applying a conditional argument, and compare their performance against those of ad hoc and Jolly-Seber approaches using simulation.

Agricultural screening trials often involve a large number (t) of treatments in a complete block design with limited replication (b = 3 or 4 blocks). The null hypothesis of interest is that of no differences between treatments. For the commonly used analysis of variance (ANOVA) procedure, most texts do not discuss agreement between actual and nominal Type I error rates in the presence of nonnormality, in this small b, large t, situation. Similarly, for the Friedman and the increasingly popular "ANOVA on ranks" procedures, it is not easy to find results concerning null performance given b small and t large. In this article, we therefore present results, from two different bodies of theory, that provide useful insight concerning null performance of these ANOVA and rank procedures when t is large. The two types of theory are (i) the classical approach based on moment approximations to the permutation distribution, and (ii) central-limit-theory-based asymptotics in the nonstandard t--> infinity situation. Both approaches demonstrate the validity of standard ANOVA and of ANOVA on within-block ranks, under nonnormality when t is large. Choice of the procedure to be used on a given data set should therefore be based on consideration of power properties. In general, ANOVA on ranks will be superior to standard ANOVA for data with frequent extreme values.

New rank-based methods for analyzing data from multisite clinical trials are presented in the context of "mixed" linear models. In contrast to current rank methods, the new procedures test for a drug main effect in the presence of a random drug by site interaction (or drug by investigator interaction when there is only one investigator per site). Analogous procedures are also provided for the "fixed-effects" situation, and comparisons are made with current methods. The rationale for an analysis that assumes random investigator effects is described.

A mixture model is described for dose-response studies where measurements on a continuous variable suggest that some animals are not affected by treatment. The model combines a logistic regression on dose for the probability an animal will "respond" to treatment with a linear regression on dose for the mean of the responders. Maximum likelihood estimation via the EM algorithm is described and likelihood ratio tests are used to distinguish between the full model and meaningful reduced-parameter versions. Use of the model is illustrated with three real-data examples.

Jolly (1982, Biometrics 38, 301-321) presented modifications of the Jolly-Seber model for capture-recapture data, which assume constant survival and/or capture rates. Where appropriate, because of the reduced number of parameters, these models lead to more efficient estimators than the Jolly-Seber model. The tests to compare models given by Jolly do not make complete use of the data, and we present here the appropriate modifications, and also indicate how to carry out goodness-of-fit tests which utilize individual capture history information. We also describe analogous models for the case where young and adult animals are tagged. The availability of computer programs to perform the analysis is noted, and examples are given using output from these programs.

Good (1979, Biometrics 35, 483-489) introduced a new randomization test for the two-sample problem where a proportion 1 - p of the treatment group does not respond to the treatment, and suggested that the Wilcoxon test is not effective for this situation. We show to the contrary that the Wilcoxon test is quite useful when p greater than or equal to .6 and point out an error in his definition of a one-tailed randomization test.