Semicentennial: 1881-1931

273 page

Non-Gaussian Component Analysis using Entropy Methods

Non-Gaussian component analysis (NGCA) is a problem in multidimensional data analysis which, since its formulation in 2006, has attracted considerable attention in statistics and machine learning. In this problem, we have a random variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace $\Gamma$ of the $n$-dimensional Euclidean space such that the orthogonal projection of $X$ onto $\Gamma$ is standard multidimensional Gaussian and the orthogonal projection of $X$ onto $\Gamma^{\perp}$, the orthogonal complement of $\Gamma$, is non-Gaussian, in the sense that all its one-dimensional marginals are different from the Gaussian in a certain metric defined in terms of moments. The NGCA problem is to approximate the non-Gaussian subspace $\Gamma^{\perp}$ given samples of $X$. Vectors in $\Gamma^{\perp}$ correspond to `interesting' directions, whereas vectors in $\Gamma$ correspond to the directions where data is very noisy. The most interesting applications of the NGCA model is for the case when the magnitude of the noise is comparable to that of the true signal, a setting in which traditional noise reduction techniques such as PCA don't apply directly. NGCA is also related to dimension reduction and to other data analysis problems such as ICA. NGCA-like problems have been studied in statistics for a long time using techniques such as projection pursuit. We give an algorithm that takes polynomial time in the dimension $n$ and has an inverse polynomial dependence on the error parameter measuring the angle distance between the non-Gaussian subspace and the subspace output by the algorithm. Our algorithm is based on relative entropy as the contrast function and fits under the projection pursuit framework. The techniques we develop for analyzing our algorithm maybe of use for other related problems

An Exploration of Optimal Stabilization Policy

This paper examines the optimal response of monetary and fiscal policy to a decline in aggregate demand. The theoretical framework is a two-period general equilibrium model in which prices are sticky in the short run and flexible in the long run. Policy is evaluated by how well it raises the welfare of the representative household. Although the model has Keynesian features, its policy prescriptions differ significantly from those of textbook Keynesian analysis. Moreover, the model suggests that the commonly used “bang for the buck” calculations are potentially misleading guides for the welfare effects of alternative fiscal policies.Economic

The health and wealth of US counties: how the small business environment impacts alternative measures of development

In this paper, we evaluate the prospects of small business-driven job creation by assessing the link between small business and population health, an alternative measure of economic development. We combine two literatures from the social capital perspective of aggregate community well-being to model the effects of small-business concentration on aggregate measures of population health. We argue that entrepreneurial culture facilitates collective efficacy for a community and provides a problem-solving capacity for addressing local public health problems. Our analysis demonstrates that communities with a greater concentration of small businesses, ceteris paribus, have greater levels of population health. Implications for theory and research are discussed