Correcting for On-Site Sampling in Random Utility Models

This study demonstrates how the joint distribution of a set of conditional trip counts to a system of recreation-sites can be adjusted for on-site sampling. An econometric approach is proposed that addresses both the size-biased distribution of the sampled visits and the weighted distribution of reported visits to ancillary destinations in a multivariate random utility framework. Estimation results indicate that uncorrected models produce biased estimates of trip counts and welfare measures. The empirical application examines jet skiing in the Lake Tahoe region. Copyright 2005, Oxford University Press.

Minimum count sums for charcoal concentration estimates in pollen slides: accuracy and potential errors

Charcoal particles in pollen slides are often abundant, and thus analysts are faced with the problem of setting the minimum counting sum as small as possible in order to save time. We analysed the reliability of charcoal-concentration estimates based on different counting sums, using simulated low-to high-count samples. Bootstrap simulations indicate that the variability of inferred charcoal concentrations increases progressively with decreasing sums. Below 200 items (i.e., the sum of charcoal particles and exotic marker grains), reconstructed fire incidence is either too high or too low. Statistical comparisons show that the means of bootstrap simulations stabilize after 200 counts. Moreover, a count of 200-300 items is sufficient to produce a charcoal-concentration estimate with less than+5% error if compared with high-count samples of 1000 items for charcoal/marker grain ratios 0.1-0.91. If, however, this ratio is extremely high or low (> 0.91 or < 0.1) and if such samples are frequent, we suggest that marker grains are reduced or added prior to new sample processing