A Class of Multiplicative Estimators of Laspeyres Price Indexes

Publication of indexes measuring changes in prices of retail, wholesale, export, and import items is an important part of many governmental statistics programs. One form of price index that is often used is the fixed-base Laspeyres, in which a fixed market basket of goods is priced over time. This article introduces a new class of multiplicative estimators of Laspeyres indexes. The optimum within the class is derived for long-term price change and compared with two other members of the class when used for estimating both long-term and short-term change. Theoretical properties are derived under a model in which long-term relative price changes for individual items have common within-stratum means and are correlated over time. Theory for long-term and short-term change estimators is tested in a simulation study in which a large number of stratified probability samples is selected from a population extracted from items priced for the U.S. consumer price index.     

Improving Treatment Integrity in a Human Service Setting Using Lottery-Based Incentives

The present study was conducted to improve implementation fidelity of discrete trial training procedures. Three participants were selected from a special education school providing services for students with intellectual disabilities. Staff behaviors measured included the correct implementation of a prompt hierarchy, the accuracy of data recorded, the correct delivery of prompts, and whether procedures were correctly set up for the following day's activities. Intervention consisted of daily verbal and graphic feedback and a lottery-based incentive system in which staff had to perform at a set goal level for the week in order to be eligible for entry into a drawing. A multiple-baseline-across-participants design was utilized to evaluate the effects of the intervention package, and integrity improved consistently for all participants during the intervention.     

Editorial

In writing this Editorial, it is impossible not to acknowledgethat sense that we are about to embark upon another profoundpolitical and economic shift with the election in America ofPresident Obama and the return to Keynesian following the globaleconomic crisis. Although the neo-con free marketers are stillto be found in every major national government and global financialinstitutions, they have at least been temporarily silenced bythe turmoil and spillover from the financial markets to thepoint where governments embrace regulation and have been franticallyadopting partial nationalization of the banking sector. Meanwhile,Obama speaks of change and new relationships between Americaand the rest of the world. We have, of course, been here before—although     

The psychology of AMBER Alert: Unresolved issues and implications

The AMBER alert system is likely affected by a number of psychological processes, yet remains understudied. The system assumes people will remember Alert information accurately and notify police, but psychological research on related phenomena (e.g., memory, willingness to help) indicates that people may not be able or willing to act in ways the promote the success of the system. In addition, the system is intended to deter child abductions, however, the system could prompt copycat crimes from perpetrators seeking publicity. The system could also cause a precipitation effect in which a perpetrator who sees the Alert could decide to murder the child immediately to avoid capture. Policy recommendations are made based on psychological research and theory, although more research is needed to develop the most effective system possible.     

Solomonic separation: Risk decisions as productivity indicators

A principal provides budgets to agents (e.g., divisions of a firm or the principal’s children) whose expenditures provide her benefits, either materially or because of altruism. Only agents know their potential to generate benefits. We prove that if the more “productive” agents are also more risk-tolerant (as holds in the sample of individuals we surveyed), the principal can screen agents and bolster target efficiency by offering a choice between a nonrandom budget and a two-outcome risky budget. When, at very low allocations, the ratio of the more risk-averse type’s marginal utility to that of the other type is unbounded above (e.g., as with CRRA), the first-best is approached.—A biblical opening enlivens the analysis.     

SEQUENTIAL,BOTTOM‐UP VARIABLE SELECTION FOR HIGH‐DIMENSIONAL CLASSIFICATION

Most methods for variable selection work from the top down and steadily remove features until only a small number remain. They often rely on a predictive model, and there are usually significant disconnections in the sequence of methodologies that leads from the training samples to the choice of the predictor, then to variable selection, then to choice of a classifier, and finally to classification of a new data vector. In this paper we suggest a bottom‐up approach that brings the choices of variable selector and classifier closer together, by basing the variable selector directly on the classifier, removing the need to involve predictive methods in the classification decision, and enabling the direct and transparent comparison of different classifiers in a given problem. Specifically, we suggest ‘wrapper methods’, determined by classifier type, for choosing variables that minimize the classification error rate. This approach is particularly useful for exploring relationships among the variables that are chosen for the classifier. It reveals which variables have a high degree of leverage for correct classification using different classifiers; it shows which variables operate in relative isolation, and which are important mainly in conjunction with others; it permits quantification of the authority with which variables are selected; and it generally leads to a reduced number of variables for classification, in comparison with alternative approaches based on prediction.