Fixed-Width Estimation of the Mean of Logistic Response Function Using Spearman-Karber Estimator

In this paper we study the asymptotic properties of the sequential fixed-width rule developed for the estimation of the mean of the logistic response function based on the quantal responses observed at equally spaced dose levels. The Spearman-Karber (S-K) and Spearman-type variance (S-T-V) estimator are used for the mean and variance, respectively, in the sequential estimation. We compute the coverage probabilities and stop     

Small Sample Bias and Variance of Lincoln-Petersen and Bailey Estimators

The Lincoln-Petersen and Bailey estimators of an unknown population size were compared in a computer simulation of capture-recapture sampling with replacement from small populations. The Bailey estimator was negatively biased and had smaller variance than the Petersen estimator. The Petersen estimator tended to be positively biased. The Lincoln-Petersen variance estimator tended to be positively biased while the Bailey variance estimator was negatively biased.     

Estimation of the Density of G Given Observations of Y=G+E

We study the problem of estimating the density of a random variable G, given observations of a random variable Y = G + E. The random variable E is independent of G and its probability distribution function is considered as known. We build a family of estimators of the density of G using characteristic functions. We then derive a family of estimators of the density of Y based on the model for Y. The estimators are shown to be asymptotically unbiased and consistent. Simulations show that these estimators are better, as measured by integrated squared error, than the standard kernel estimators. Finally, we give an example of the use of this method for the detection of major genes in animal populations.     

Complete allocation sampling: an efficient and easily implemented adaptive sampling design

Adaptive sampling designs are becoming increasingly popular in environmental science, particularly for surveying rare and aggregated populations. An adaptive sample is one in which the survey design is modified, or adapted, in some way on the basis of information gained during the survey. There are many different adaptive survey designs that can be used to estimate animal and plant abundance. In adaptive cluster sampling, additional sample effort is allocated during the survey to the immediate neighborhood in which the species is found. In adaptive stratified sampling, additional sample effort is allocated during the survey to strata of high abundance. The appealing feature of these adaptive designs is that the field biologist gets to do what innately seems sensible when working with rare and aggregated populations—field effort is targeted around where the species is observed in the first wave of the survey. However, there are logistical challenges of applying this principle of targeted field effort while remaining in the framework of probability-based sampling. We propose a simplified adaptive survey design that incorporates both targeting field effort and being logistically feasible. We show with a case study population of rockfish that complete allocation stratified sampling is a very efficient design.     

Predictive Estimation of Finite Population Mean Using a Product Estimator for Two-Stage Sampling

In an attempt to estimate a finite population mean under the predictive approach described in Basu (1971) through the product method of estimation, we created a new product-type estimator for a two-stage sampling procedure. We also report a simulation study that is made in order to understand better the performance of the new estimator compared to the classical product estimator.