Use of the Bootstrap and Cross-Validation in Ridge Regression

Several existing methods for the choice of the ridge parameter are reviewed, and a bootstrap method is proposed. The bootstrap provides independent measures of prediction errors based on multiple predictions along with an estimate of the standard error of prediction. The bootstrap and selected competitors are compared through Monte Carlo simulations for various degrees of design matrix collinearity and varying levels of signal-to-noise ratio. The procedure is also illustrated by application to two published data sets. In one case, the bootstrap choice of the ridge parameter leads to a smaller mean squared error of prediction than the ridge trace method. In the second case, an optimal choice of no perturbation is confirmed. Benefits of the bootstrap choice include its less subjective nature, ease of implementation, and robustness.     

Book Reviews

Aggregate forecasts using McFadden's conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random-utility distribution that is homogeneous across both alternatives and individuals. This article relaxes that assumption. A choice model is developed that describes the random-utility component as a probability-mixture model. Some numerical results illustrate that the derived model is not constrained by the independence-of-irrelevant-alternatives property. An experimental test of visual perceptions demonstrates the potential superiority of the model.     

Media Effects on Group Collaboration: An Empirical Examination in an Ethical Decision‐Making Context

Even though it is widely acknowledged that collaboration underlies much of the decision‐making efforts in contemporary organizations, and that organizational groups are increasingly making decisions that have ethical implications, few studies have examined group ethical decision‐making processes and outcomes. In addition, while there is increasing evidence that groups often collaborate/communicate using different mediating technologies, few studies have examined the effect of the characteristics of the media in group ethical decision‐making contexts. Finally, there is a clear paucity of studies that have investigated group decision making pertaining to information technology (IT)‐related ethical dilemmas, an area of rising importance for information systems (IS) and decision science researchers. This article seeks to address the gaps described above through an experimental study where groups collaborating either in a face‐to‐face context or in a computer‐mediated context (using NetMeeting or Wiki) were required to make a decision with respect to a scenario with an IT‐related ethical dilemma. Results indicate that media characteristics (e.g., anonymity, immediacy of feedback, parallelism) do not have an effect on whether groups make ethical (or unethical) decisions. However, several media characteristics were found to play a significant role on downstream variables, such as the quality of a follow‐up task (i.e., creation of a decision justification document), and overall process satisfaction of the group members.     

Determinants of Individual Giving Behavior in Urban India

This paper, based on a primary sample survey over 1925 earning individuals in the cities of Kolkata, Cuttack and Bengaluru, examines how the individual and household characteristics influence the acts of giving in urban India. The regression results indicate income, family size and property ownership affecting likelihood and extent of giving. Likelihood to give is more with females, though males tend to donate more. There exists threshold income beyond which likelihood to donate is less. Characteristics like age, education, dependency ratio and marital status influence certain acts of giving. As the opportunity cost of non-cash giving increases with the rise in income, cash donations substitute non-cash giving. There also prevails complementarity in the acts of giving. On behavioral front, in addition to work–life balance and pledging, the notion of rational choice seems to be gaining ground.

     

On kernel density estimation based on different stratified sampling with optimal allocation

Kernel density estimation is probably the most widely used non parametric statistical method for estimating probability densities. In this paper, we investigate the performance of kernel density estimator based on stratified simple and ranked set sampling. Some asymptotic properties of kernel estimator are established under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. These findings are also illustrated with the help of a dataset on bilirubin levels in babies in a neonatal intensive care unit.     

BARGAINING WITH SHARED INFORMATION*

This paper formulates and analyzes models of two-party bargaining behavior where each side possesses private information about its preferences that is unavailable to the other. We examine the strategic behavior that bargainers might exhibit when disclosing information either to an arbitrator or to each other. Our results show that when bargaining is over one issue, it is relatively uncomplicated analytically to calculate the optimal strategy, but, despite this, even sophisticated bargainers tend to make wrong assumptions about the behavior of others and to use strategies that are far from optimal. When the bargaining encompasses several factors, computation of optimal strategies becomes very cumbersome and the use of an optimal strategy does not gain a bargainer much over what he could have achieved with truthful revelation. Thus, in theory, truthful revelation is not the best course of action to adopt for a bargainer interested in maximizing individual gain, but in practice the alternatives may involve mistaken assumptions about the behavior of others and may therefore turn out to be inferior to truthful revelation.     

Multiperson Bargaining and Strategic Complexity

We investigate the effect of introducing costs of complexity in the n‐person unanimity bargaining game. As is well‐known, in this game every individually rational allocation is sustainable as a Nash equilibrium (also as a subgame perfect equilibrium if players are sufficiently patient and if n & 2). Moreover, delays in agreement are also possible in such equilibria. By limiting ourselves to a plausible notion of complexity that captures length of memory, we find that the introduction of complexity costs (lexicographically with the standard payoffs) does not reduce the range of possible allocations but does limit the amount of delay that can occur in any agreement. In particular, we show that in any n‐player game, for any allocation z, an agreement on z at any period t can be sustained as a Nash equilibrium of the game with complexity costs if and only if t≤n. We use the limit on delay result to establish that, in equilibrium, the strategies implement stationary behavior. Finally, we also show that ‘noisy Nash equilibrium’ with complexity costs sustains only the unique stationary subgame perfect equilibrium allocation.     

Major League Baseball Player Salaries: Bringing Realism into Introductory Statistics Courses

A dataset consisting of salaries of major league baseball players is published at the start of each season in USA Today, and is also made available on the Internet. It is argued that such an easily available dataset and those similar to it can be successfully used by students in a first statistics course for an interesting introduction to data analysis through summary measures and graphical displays. Such an approach is most natural for many students because of a strong interest in sports and economics. Other statistical ideas can be explored as a natural consequence of the discussions that ensue from such an analysis.