Suchergebnisse

In this article we review some recent attempts to develop econometric models for assessing the deterrent effect of punishment on crime, as well as analyses carried out to validate these models. The formulation of the basic econometric model considered here is due to Becker, and the detailed specification of the model, along with much of the empirical work reviewed, has been carried out by Ehrlich. We find serious flaws with the Becker- Ehrlich model, with the data used in its empirical implementation, and with Ehrlich's conclusions regarding evidence to support the deterrent effect of punishment on crime. Indeed, we can find no reliable empirical support in the existing economics literature either for or against the deterrence hypothesis.

The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. In a recent paper, Barak et al.[1] extend this approach to the release of a specified set of margins from a multi-way contingency table. Privacy protection in such settings implicitly focuses on small cell counts that might allow for the identification of units that are unique in the database. We explore how well the mechanism works in the context of a series of examples, and the extent to which the proposed differential-privacy mechanism allows for sensible inferences from the released data. We conclude that the methodology, as it is currently formulated, is problematic in the context of the types of large sparse contingency tables encountered in statistical practice.

Front Matter -- Preface -- Contents -- 1 Introduction -- 2 Overview of the 2000 Housing Discrimination Study -- 3 Defining the Population of Interest -- 4 Defining Housing Discrimination -- 5 Developing a Model of Housing Discrimination -- 6 Auditing Discrimination in Underserved Communities -- References -- Appendix A Paired Testing and the 2000 Housing Discrimination Survey -- Appendix B Audit Studies and the Assessment of Discrimination -- Appendix C Workshop Materials.

Law and science share many perspectives, but they also differ in important ways. While much of science is concerned with the effects of causes (EoC), relying upon evidence accumulated from randomized controlled experiments and observational studies, the problem of inferring the causes of effects (CoE) requires its own framing and possibly different data. Philosophers have written about the need to distinguish between the "EoC" and "the CoE" for hundreds of years, but their advice remains murky even today. The statistical literature is only of limited help here as well, focusing largely on the traditional problem of the "EoC." Through a series of examples, we review the two concepts, how they are related, and how they differ. We provide an alternative framing of the "CoE" that differs substantially from that found in the bulk of the scientific literature, and in legal cases and commentary on them. Although in these few pages we cannot fully resolve this issue, we hope to begin to sketch a blueprint for a solution. In so doing, we consider how causation is framed by courts and thought about by philosophers and scientists. We also endeavor to examine how law and science might better align their approaches to causation so that, in particular, courts can take better advantage of scientific expertise.

Traditional statistical methods for confidentiality protection of statistical databases do not scale well to deal with GWAS databases especially in terms of guarantees regarding protection from linkage to external information. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information, although the guarantees may come at a serious price in terms of data utility. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chi-square statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially-private approach to penalized logistic regression.

Preserving the privacy of individual databases when carrying out statistical calculations has a relatively long history in statistics and had been the focus of much recent attention in machine learning. In this paper, we present a protocol for fitting a logistic regression when the data are held by separate parties---without actually combining information sources---by exploiting results from the literature on multi-party secure computation. Our protocol provides only the final result of the calculation compared with other methods that share intermediate values and thus present an opportunity for compromise of values in the individual databases. Our paper has two themes: (1) the development of a secure protocol for computing the logistic parameters, and a demonstration of its performances in practice, and (2) the presentation of an amended protocol that speeds up the computation of the logistic function. We illustrate the nature of the calculations and their accuracy using an extract of data from the Current Population Survey divided between two parties. Throughout, we build our protocol from existing cryptographic primitives, thus the novelty is in designing a concrete procedure for private computation of the logistic regression MLE rather than to propose new cryptographic constructions.

On January 5, 1975, Ronald Reagan completed two terms as governor of
California, and soon thereafter he began taping his nationally
syndicated radio program. Between January 1975 and October 1979,
with the exception of a brief interlude to compete for the
Republican presidential nomination in 1976, the former governor
delivered three-minute commentaries that were broadcast every week
by approximately 300 U.S. radio stations, reaching an audience of
between 20–30 million listeners.