Limiting the Collective Right to Exclude

For decades, society’s disparate interests and priorities have stymied attempts to resolve issues of housing affordability and equity. Zoning law and servitude law, both of which have been robustly empowered by decades of jurisprudence, effectively grant communities the legal right and ability to exclude various sorts of residences from their wealthiest neighborhoods. Exclusion by housing type results in exclusion of categories of people, namely, renters, the relatively poor, and racial minorities. Although our society’s housing woes may indeed be intractable if we continue to treat a group’s right to exclude with the level of deference that such exclusionary efforts currently enjoy, this treatment is unjustifiable. Courts should acknowledge and consider the broad public and private costs that are created by a group’s unfettered right to exclude. A more balanced approach would weigh individual autonomy to control property and various public harms resulting from community exclusions against legitimate community needs to exclude certain residents and uses. Judicial limits of the collective right to exclude may enable real progress toward fair and affordable housing to be achieved at last

The Economics of Suicide: An Empirical Study

This study uses economic theory to investigate the impact of socioeconomic factors on the suicide rate in the United States. Using a utility maximization framework based on Hamermesh and Soss’ 1974 model, a panel data set from 2000-2010 is constructed for the 50 states and District of Columbia. This research adds to the literature in the field by focusing on the more recent past and providing additional variables consistent with today’s challenges. The results from the multiple regression analysis can be used to advocate policies that may reduce the suicide rate in the future

“There Was Nothing in Sight but Nature, Nothing...”: Nineteenth-Century Gendered Perceptions of the Overland Trail

One hundred and seventeen years ago, between 1841 and 1867, the Overland Trail saw approximately 350,000 Oregon and California bound North Americans traverse its landscape. This westward migration painted the American frontier with a white sea of wagon covers, spotted the grassy plains with brown patches of oxen herds, and lighted the night sky with open cooking fires. Men and women Overlanders experienced this life-changing event in different ways, which are crucial to understanding the dynamics and interaction between these people and their frontier context. Gender-specific roles and social standards of masculinity and femininity carried from emigrants’ previous lives influenced their perception of the Overland Trail, interaction with the environment, and their future on the western frontier. These influences affected the settlers throughout the entire journey, beginning with their decision for such a move

The capacity region of broadcast channels with intersymbol interference and colored Gaussian noise

We derive the capacity region for a broadcast channel with intersymbol interference (ISI) and colored Gaussian noise under an input power constraint. The region is obtained by first defining a similar channel model, the circular broadcast channel, which can be decomposed into a set of parallel degraded broadcast channels. The capacity region for parallel degraded broadcast channels is known. We then show that the capacity region of the original broadcast channel equals that of the circular broadcast channel in the limit of infinite block length, and we obtain an explicit formula for the resulting capacity region. The coding strategy used to achieve each point on the convex hull of the capacity region uses superposition coding on some or all of the parallel channels and dedicated transmission on the others. The optimal power allocation for any point in the capacity region is obtained via a multilevel water-filling. We derive this optimal power allocation and the resulting capacity region for several broadcast channel models

Information Recovery from Pairwise Measurements

A variety of information processing tasks in practice involve recovering $n$ objects from single-shot graph-based measurements, particularly those taken over the edges of some measurement graph $\mathcal{G}$. This paper concerns the situation where each object takes value over a group of $M$ different values, and where one is interested to recover all these values based on observations of certain pairwise relations over $\mathcal{G}$. The imperfection of measurements presents two major challenges for information recovery: 1) $\textit{inaccuracy}$: a (dominant) portion $1-p$ of measurements are corrupted; 2) $\textit{incompleteness}$: a significant fraction of pairs are unobservable, i.e. $\mathcal{G}$ can be highly sparse. Under a natural random outlier model, we characterize the $\textit{minimax recovery rate}$, that is, the critical threshold of non-corruption rate $p$ below which exact information recovery is infeasible. This accommodates a very general class of pairwise relations. For various homogeneous random graph models (e.g. Erdos Renyi random graphs, random geometric graphs, small world graphs), the minimax recovery rate depends almost exclusively on the edge sparsity of the measurement graph $\mathcal{G}$ irrespective of other graphical metrics. This fundamental limit decays with the group size $M$ at a square root rate before entering a connectivity-limited regime. Under the Erdos Renyi random graph, a tractable combinatorial algorithm is proposed to approach the limit for large $M$ ($M=n^{\Omega(1)}$), while order-optimal recovery is enabled by semidefinite programs in the small $M$ regime. The extended (and most updated) version of this work can be found at (http://arxiv.org/abs/1504.01369).Comment: This version is no longer updated -- please find the latest version at (arXiv:1504.01369

A method of moments estimator of tail dependence

In the world of multivariate extremes, estimation of the dependence structure still presents a challenge and an interesting problem. A procedure for the bivariate case is presented that opens the road to a similar way of handling the problem in a truly multivariate setting. We consider a semi-parametric model in which the stable tail dependence function is parametrically modeled. Given a random sample from a bivariate distribution function, the problem is to estimate the unknown parameter. A method of moments estimator is proposed where a certain integral of a nonparametric, rank-based estimator of the stable tail dependence function is matched with the corresponding parametric version. Under very weak conditions, the estimator is shown to be consistent and asymptotically normal. Moreover, a comparison between the parametric and nonparametric estimators leads to a goodness-of-fit test for the semiparametric model. The performance of the estimator is illustrated for a discrete spectral measure that arises in a factor-type model and for which likelihood-based methods break down. A second example is that of a family of stable tail dependence functions of certain meta-elliptical distributions.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ130 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm