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We consider the problem of estimating a time series of population means from a series of sample surveys when the means are known to be nondecreasing. We introduce the standard survey estimators of the series of means, which are not guaranteed to be nondecreasing. We employ the Pool Adjacent Violators Algorithm (PAVA) to turn the series of standard survey estimates into a nondecreasing series. We introduce five methods of constructing confidence intervals for the series of non-decreasing means: normal-theory intervals based on the standard survey point estimator of the mean and the Taylor series estimator of its variance; normal-theory intervals based on the nondecreasing PAVA estimator of the mean and a unique jackknife estimator of its variance developed here (jackknife- z); intervals similar to the aforementioned method but based on Student's- t distribution (jackknife- t); analytical intervals due to Morris; and simultaneous confidence limits due to Korn. We report the results of a Monte Carlo simulation and assess the methods' performance under various scenarios. Jackknife- t intervals exhibited coverage probabilities that are near the nominal value. Taylor, jackknife- z , and Korn intervals found coverage below the nominal value while Morris intervals were excessively conservative. Jackknife intervals were much narrower than Taylor intervals. [ABSTRACT FROM AUTHOR]

The National Marine Fisheries Service is the agency charged with estimating the number of fish removed from US oceans by recreational anglers. Two surveys produce data for these estimates; one measures number of angler trips and the other fish caught per trip by species, geography, and time period. Both surveys collect data from people and have the usual sources of nonsampling error afflicting demographic surveys. Due to lack of accessibility to fishing sites, they also have additional sources such as undercoverage. The rare incidence of saltwater fishing among the general population makes the current method costly and time-consuming. Consequently, new ways of obtaining information to supplement or replace the current method are of interest. One is the electronic logbook (ELB); this approach allows anglers to self-report their catch using cellphones or other communication devices. Estimation of catch from these data are possible with estimators using capture-recapture methods, but new sources of nonsampling error are rising. In this article, we examine three sources of nonsampling error in estimators of catch and approximate their biasing effect. We illustrate the method by comparing the effects using data from an ELB study in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]