From New London to Norwood: A Year in the Life of Eminent Domain

A little more than a year after the U.S. Supreme Court\u27s decision in Kelo v. City of New London upheld the use of eminent domain for economic development, the Ohio Supreme Court became the first state supreme court to address a factual situation raising the same issues. In City of Norwood v. Horney, the Ohio court repudiated the Kelo rationale and rejected Norwood\u27s proposed takings. Property rights advocates quickly hailed Norwood as a model for other state courts to follow in defending individual land owners from eminent domain abuse. This Note argues that Norwood\u27s holding is incoherent and does nothing to resolve the language-based quagmire that inflames the eminent domain debate. This Note instead contends that the Connecticut Supreme Court\u27s more nuanced Kelo v. City of New London opinion is a superior state court model, which better captures the necessary balance between individual property rights and urban revitalization plans involving eminent domain

Derivatives of Entropy Rate in Special Families of Hidden Markov Chains

Consider a hidden Markov chain obtained as the observation process of an ordinary Markov chain corrupted by noise. Zuk, et. al. [13], [14] showed how, in principle, one can explicitly compute the derivatives of the entropy rate of at extreme values of the noise. Namely, they showed that the derivatives of standard upper approximations to the entropy rate actually stabilize at an explicit finite time. We generalize this result to a natural class of hidden Markov chains called ``Black Holes.'' We also discuss in depth special cases of binary Markov chains observed in binary symmetric noise, and give an abstract formula for the first derivative in terms of a measure on the simplex due to Blackwell.Comment: The relaxed condtions for entropy rate and examples are taken out (to be part of another paper). The section about general principle and an example to determine the domain of analyticity is taken out (to be part of another paper). A section about binary Markov chains corrupted by binary symmetric noise is adde

Direct solar-pumped iodine laser amplifier

This semiannual progress report covers the period from March 1, 1987 to September 30, 1987 under NASA grant NAG1-441 entitled 'Direct solar-pumped iodine laser amplifier'. During this period Nd:YAG and Nd:Cr:GSGG crystals have been tested for the solar-simulator pumped cw laser, and loss mechanisms of the laser output power in a flashlamp-pumped iodine laser also have been identified theoretically. It was observed that the threshold pump-beam intensities for both Nd:YAG and Nd:Cr:GSGG crystals were about 1000 solar constants, and the cw laser operation of the Nd:Cr:GSGG crystal was more difficult than that of the Nd:YAG crystal under the solar-simulator pumping. The possibility of the Nd:Cr:GSGG laser operation with a fast continuously chopped pumping was also observed. In addition, good agreement between the theoretical calculations and the experimental data on the loss mechanisms of a flashlamp-pumped iodine laser at various fill pressures and various lasants was achieved

High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting

Recent research has focused on $\ell_1$ penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates $p$ is considerably larger than the sample size $n$. However, few studies have examined the properties of the estimators when the errors and/or the covariates are serially dependent. In this study, we investigate the theoretical properties of the Lasso estimator for a linear regression with a random design and weak sparsity under serially dependent and/or nonsubGaussian errors and covariates. In contrast to the traditional case, in which the errors are independent and identically distributed and have finite exponential moments, we show that $p$ can be at most a power of $n$ if the errors have only finite polynomial moments. In addition, the rate of convergence becomes slower owing to the serial dependence in the errors and the covariates. We also consider the sign consistency of the model selection using the Lasso estimator when there are serial correlations in the errors or the covariates, or both. Adopting the framework of a functional dependence measure, we describe how the rates of convergence and the selection consistency of the estimators depend on the dependence measures and moment conditions of the errors and the covariates. Simulation results show that a Lasso regression can be significantly more powerful than a mixed-frequency data sampling regression (MIDAS) and a Dantzig selector in the presence of irrelevant variables. We apply the results obtained for the Lasso method to nowcasting with mixed-frequency data, in which serially correlated errors and a large number of covariates are common. The empirical results show that the Lasso procedure outperforms the MIDAS regression and the autoregressive model with exogenous variables in terms of both forecasting and nowcasting