SPLOGS AND ABANDONED BLOGS: The perils of sampling bloggers and their blogs

In this paper the authors explore the problems inherent with sampling bloggers and their blogs, specifically several factors unique to these populations: spam blogs, abandoned blogs, access-restricted blogs, and non-traditional blogs. Reviewing 24 blogging studies, they focus on the four strategies that researchers have employed to sample bloggers and blogs on the Internet: self-selected and convenience samples, sampling through blog hosts, sampling with the assistance of blog aggregators or indexing websites, and sampling from ready-published lists of blogs. They look closely at the practicality of such methods and discuss their tradeoffs. They then examine several techniques that researchers utilize to sample elusive, networked and rare populations in an offline context, emphasizing how they can be applied to sampling bloggers and blogs. In conclusion, they propose that these suggestions be applied to sampling methodologies concerning other Internet-based communities.     

Maximum likelihood procedure adapted to sampling schemes

This paper considers a maximum likelihood procedure for the data obtained by sampling surveys such as stratified sampling and two-stage sampling. We evaluate the variance of the estimators and the distribution of the log-likelihood ratio statistic.     

Model-Based Stratification in Inventory Cost Estimation

This article describes a method for developing an efficiently stratified sampling design for a generalized difference estimator, using a superpopulation model. The effectiveness of model-based stratification is compared to several conventional designs, using a data base from a complete audit of an inventory of 8,069 items. In this application, model-based designs reduce the required sample size from 9% to 40%, compared to the conventional designs.     

One-sided normal prediction intervals and sample sizes for a third-stage sample

One-sided two-stage prediction intervals for a normal population are extended to a third sampling stage. Procedures and tables are given for two situations. In the first situation, methods for obtaining such intervals are presented, and tables for calculating such prediction intervals are provided. In the second situation, a two-stage prediction interval has been applied, and a third stage is now required. Sample sizes are given for the third stage.     

A Simple Demonstration of the Relationship between Classification and Canonical Variates Analysis

The purpose of this article is to provide a perspective on present technology for acquisition of information relative to the status of renewable natural resources, with particular emphasis on forest and marine resources. Commonalities and dichotomies are emphasized in the hope of achieving a synthesis that will promote shared understanding and dialogue between statisticians and natural resource discipline specialists, with the ultimate goal being improved survey methodology.     

Problems in sampling the Native American and Alaska Native populations

Selecting a scientific national sample of Native Americans and Alaska Natives is difficult for at least four reasons: (1) they are a small proportion of the total population, (2) they are not so segregated that geographic oversampling can reach most of the population, (3) criteria for deciding who is a member of the Native American and Alaska Native population are not well defined, and (4) they are so culturally diverse that subclass estimates may be of equal or greater importance than overall estimates. The goal of this paper is to identify key design choices a statistician must face when selecting a sample of these populations, and to discuss alternative strategies. I also provide commentary on sampling plans that have been used for previous surveys, and explain why these plans, while sufficient for the purposes of their particular surveys, do not provide general solutions. Of particular importance is a careful definition of the study population. For example, some studies would focus on the reservation population, others would focus on persons listed on tribal rolls, and still others on persons eligible for coverage by the Indian Health Service. The numbers of persons included in all of these groups is far short of the two million Native Americans and Alaska Natives counted in the 1990 Census. I conclude that there is no sampling strategy that is appropriate for all surveys, but also provide suggestions for certain survey situations.     

Confidence intervals and significance tests for a single trial

Confidence intervals are developed for the location parameter of a continuous, symmetric, unimodal distribution in the casev where only a single observation from the distribution is available. These intervals are similar to those given by Abbott and Rosenblatt (1963), but shorter. The result is extended to include distributions which can be standardized to have unit scale. The procedure is exemplified for the normal distribution and the power of one- and two-sided significance tests are computed under normality.     

Estimation of parameters of the inverse gaussian-weibull mixture model

The main objective of this work is to estimate the 5-dime-nsional vector of parameters (p,μ,λ,α,c) of the mixture of an Inverse Gaussian IG(μ,λ) and Weibull W(α,c) distributions with mixing proportion p. We use the maximum Likelihood method (MLM) and the weighted maximum likelihood method (WMLM), both under the sampling schemes suggested by Hosmer (1973). Simulation study shows that the WMLM performs best, when Hosmer's model 2 is used, in the sense of minimizing the mean square error.     

Examples of Differing Weighted and Unweighted Estimates from a Sample Survey

Unweighted estimators using data collected in a sample survey can be badly biased, whereas weighted estimators are approximately unbiased for population parameters. We present four examples using data from the 1988 National Maternal and Infant Health Survey to demonstrate that weighted and unweighted estimators can be quite different, and to show the underlying causes of such differences.