Which countries protect intellectual property? The case of software piracy

Using data on software piracy, we examine how protection of intellectual property varies across countries. Consistent with other studies, we find that intellectual property receives greater protection in developed economies; high-income countries have lower piracy rates. We also find that protection depends on cultural factors. Countries with an individualist culture have lower piracy rates than do countries with a collectivist culture. Piracy rates are also lower in countries that have strong institutions that enforce contracts and protect property from expropriation. These results suggest that national policies toward intellectual property reflect not only economic concerns but also national culture and institutions.     

Partitioned cross-validation

Partitioned cross-validation is proposed as a method for overcoming the large amounts of across sample variability to which ordinary cross-validation is subject. The price for cutting down on the sample noise is that a type of bias is intriduced. A theory is presented for optimal trade-off of this variance and bias. Comparison with other bandwidth selection methods is given.     

Geometric representation of high dimension, low sample size data

Summary.  High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This geometric representation is used to obtain several new statistical insights.     

Robust centroid based classification with minimum error rates for high dimension,low sample size data

A new method of statistical classification (discrimination) is proposed. The method is most effective for high dimension, low sample size data. It uses a robust mean difference as the direction vector and locates the classification boundary by minimizing the error rates. Asymptotic results for assessment and comparison to several popular methods are obtained by using a type of asymptotics of finite sample size and infinite dimensions. The value of the proposed approach is demonstrated by simulations. Real data examples are used to illustrate the performance of different classification methods.     

Weighted Distance Weighted Discrimination and Its Asymptotic Properties

While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dimensions, it was designed for balanced datasets. In the case of unequal costs, biased sampling, or unbalanced data, there are major improvements available, using appropriately weighted versions of DWD (wDWD). A major contribution of this paper is the development of optimal weighting schemes for various nonstandard classification problems. In addition, we discuss several alternative criteria and propose an adaptive weighting scheme (awDWD) and demonstrate its advantages over nonadaptive weighting schemes under some situations. The second major contribution is a theoretical study of weighted DWD. Both high-dimensional low sample-size asymptotics and Fisher consistency of DWD are studied. The performance of weighted DWD is evaluated using simulated examples and two real data examples. The theoretical results are also confirmed by simulations.     

Interactive local bandwidth choice

A tool for user choice of the local bandwidth function for kernel density and nonparametric regression estimates is developed using KDE, a graphical object-oriented package for interactive kernel density estimation written in LISP-STAT. The bandwidth function is a parameterized spline, whose knots are manipulated by the user in one window, while the resulting estimate appears in another window. A real data illustration of this method raises concerns, because an extremely large family of estimates is available. Suggestions are made to overcome this problem so that this tool can be used effectively for presenting final results of a data analysis.     

Dependent SiZer: Goodness-of-Fit Tests for Time Series Models

In this paper, we extend SiZer (SIgnificant ZERo crossing of the derivatives) to dependent data for the purpose of goodness-of-fit tests for time series models. Dependent SiZer compares the observed data with a specific null model being tested by adjusting the statistical inference using an assumed autocovariance function. This new approach uses a SiZer type visualization to flag statistically significant differences between the data and a given null model. The power of this approach is demonstrated through some examples of time series of Internet traffic data. It is seen that such time series can have even more burstiness than is predicted by the popular, long- range dependent, Fractional Gaussian Noise model.     

Planning and implementation of a participatory evaluation strategy: A viable approach in the evaluation of community-based participatory programs addressing cancer disparities

Community-based participatory research (CBPR) has been posited as a promising methodology to address health concerns at the community level, including cancer disparities. However, the major criticism to this approach is the lack of scientific grounded evaluation methods to assess development and implementation of this type of research. This paper describes the process of development and implementation of a participatory evaluation framework within a CBPR program to reduce breast, cervical, and colorectal cancer disparities between African Americans and whites in Alabama and Mississippi as well as lessons learned. The participatory process involved community partners and academicians in a fluid process to identify common ground activities and outcomes. The logic model, a lay friendly approach, was used as the template and clearly outlined the steps to be taken in the evaluation process without sacrificing the rigorousness of the evaluation process. We have learned three major lessons in this process: (1) the importance of constant and open dialogue among partners; (2) flexibility to make changes in the evaluation plan and implementation; and (3) importance of evaluators playing the role of facilitators between the community and academicians. Despite the challenges, we offer a viable approach to evaluation of CBPR programs focusing on cancer disparities.