Strategic competitive information systems

A variety of strategic competitive information systems is described and evaluated in terms of three criteria which should be met by strategic information systems. The more sophisticated varieties—those that incorporate specific models into the system—generally meet the criteria better than do the ‘retrieval’ varieties of systems. Since systems of this kind are relatively expensive to develop, this suggests that the area of strategic competitive information may be one of great potential payoff for the integration of models into information systems.     

Cross-validation Revisited

Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, the L₁ distance between the pseudo-likelihood-based estimator and the density does not seem to converge in probability to zero with increasing sample size. Even for normal-tailed densities, the rate of L₁ convergence is disappointingly slow. In this article, we report an interesting finding that with minor modifications both the cross-validation methods can be implemented effectively, even for heavy-tailed densities. For both these estimators, the L₁ distance (from the density) are shown to converge completely to zero irrespective of the tail of the density. The expected L₁ distance also goes to zero. These results hold even in the presence of a strongly mixing-type dependence. Monte Carlo simulations and analysis of the Old Faithful geyser data suggest that if implemented appropriately, contrary to the traditional belief, the cross-validation estimators compare well with the sophisticated plug-in and bootstrap-based estimators.     

Local Smoothing for Kernel Distribution Function Estimation

The problem of bandwidth selection for kernel-based estimation of the distribution function (cdf) at a given point is considered. With appropriate bandwidth, a kernel-based estimator (kdf) is known to outperform the empirical distribution function. However, such a bandwidth is unknown in practice. In pointwise estimation, the appropriate bandwidth depends on the point where the function is estimated. The existing smoothing methods use one common bandwidth to estimate the cdf. The accuracy of the resulting estimates varies substantially depending on the cdf and the point where it is estimated. We propose to select bandwidth by minimizing a bootstrap estimator of the MSE of the kdf. The resulting estimator performs reliably, irrespective of where the cdf is estimated. It is shown to be consistent under i.i.d. as well as strongly mixing dependence assumption. Two applications of the proposed estimator are shown in finance and seismology. We report a dataset on the S & P Nifty index values.     

Local Smoothing Using the Bootstrap

We consider the problem of data-based choice of the bandwidth of a kernel density estimator, with an aim to estimate the density optimally at a given design point. The existing local bandwidth selectors seem to be quite sensitive to the underlying density and location of the design point. For instance, some bandwidth selectors perform poorly while estimating a density, with bounded support, at the median. Others struggle to estimate a density in the tail region or at the trough between the two modes of a multimodal density. We propose a scale invariant bandwidth selection method such that the resulting density estimator performs reliably irrespective of the density or the design point. We choose bandwidth by minimizing a bootstrap estimate of the mean squared error (MSE) of a density estimator. Our bootstrap MSE estimator is different in the sense that we estimate the variance and squared bias components separately. We provide insight into the asymptotic accuracy of the proposed density estimator.     

Does Trust Always Help Gender Role Attitudes? The Role of Individualism and Collectivism

Social trust has a complex interrelationship with attitudes toward gender equality. Social trust has its origins in exchange relationships in preindustrial societies, lowering uncertainty in transactions and easing interpersonal exchanges. The degree to which this trust was extended to opportunities for women in commercial and societal roles, however, differed across cultures. Prior literature finds attitudes toward individualism and collectivism have significant implications for gender equality and patriarchal attitudes. We combine these ideas arguing that the degree to which social trust fosters gender equality depends upon the degrees of individualism and collectivism. Employing World Values Survey data across countries over time, we find that with low levels of individualism, and high degrees of collectivism, higher levels of trust are not effective in reducing the prevalence of gendered patriarchal attitudes—in fact it entrenches them further, worsening gender equality. However, as individualism rises, and collectivism falls, higher levels of trust become effective in reducing the prevalence of patriarchal beliefs. Thus, collectivistic beliefs stand as a barrier to future improvements in women’s equality and economic rights in many societies; preventing social trust from being extended beyond traditional gendered roles.

     

On the tournament equilibrium set

An example is provided showing that Schwartz's tournament equilibrium set is not identical to the minimal covering set of Dutta.     

Social welfare and equality

The main purpose of the paper is to provide a unified framework within which normatively significant equality indices can be derived from social welfare orderings. The paper contains a functional representation of the class of social evaluation functions generating relative equality indices.A preliminary version of this paper was written while the second author was visiting the ISI. Financial support from the CAICYT project No. DI-87075, is greatefully acknowledged. We are grateful to W. Bossert and C. Blackorby for comments and useful discussion. The final version of the paper has greatly benefited from the suggestion of an anonymous referee.