506 research outputs found
Marooned: An Empirical Investigation of Law School Graduates Who Fail the Bar Exam
This article attempts to answer a question that legal academia has been reluctant to even ask: What happens to law school graduates who fail the bar exam? What do they do and how do their lives differ from the lives of their lawyer-classmates? Would their careers have fared any better if they had not gone to law school?
There are probably on the order of 150,000 law school graduates in the United States who have taken but never passed a bar exam; this amounts to one in ten J.D.s and the risk falls disparately on black, Hispanic, and Asian law school graduates. The ranks only will increase if the upward trend in law school enrollment and the downward trend in bar passage continue, as they have over the last decade. Despite the vast and growing literature on lawyers, research on those who fail the bar is virtually non-existent. We know astonishingly little about the law school graduates who experience the consequences of the licensing barrier, whatever they may be
Research in and application of state variable feedback design of guidance control systems for aerospace vehicles Progress report
Weighted least squares parameter estimation, Kalman filter, and random search problems for aerospace guidance control system desig
The New Intrusion
The article focuses on a new taxonomy introduced for organizing privacy regulations across several stages of information flow including observation, capture and dissemnation. It states that the tort of intrusion imposes liability upon seclusion offers and provides recourse for the observation of data. It also highlights the application of intrusion on modern settings such as web tracking technologies and global positioning systems
Limits to consistent on-line forecasting for ergodic time series
This study concerns problems of time-series forecasting under the weakest of
assumptions. Related results are surveyed and are points of departure for the
developments here, some of which are new and others are new derivations of
previous findings. The contributions in this study are all negative, showing
that various plausible prediction problems are unsolvable, or in other cases,
are not solvable by predictors which are known to be consistent when mixing
conditions hold
Weakly Convergent Nonparametric Forecasting of Stationary Time Series
The conditional distribution of the next outcome given the infinite past of a
stationary process can be inferred from finite but growing segments of the
past. Several schemes are known for constructing pointwise consistent
estimates, but they all demand prohibitive amounts of input data. In this paper
we consider real-valued time series and construct conditional distribution
estimates that make much more efficient use of the input data. The estimates
are consistent in a weak sense, and the question whether they are pointwise
consistent is still open. For finite-alphabet processes one may rely on a
universal data compression scheme like the Lempel-Ziv algorithm to construct
conditional probability mass function estimates that are consistent in expected
information divergence. Consistency in this strong sense cannot be attained in
a universal sense for all stationary processes with values in an infinite
alphabet, but weak consistency can. Some applications of the estimates to
on-line forecasting, regression and classification are discussed
Nearest Neighbor Estimators for Random Fields
AbstractGeneralizing the random sequence case, this study defines a k - NN density estimator for random variables with multidimensional lattice points serving as index values. The central result is that under random field stationary and mixing assumptions, as well as standard smoothness postulates, our k - NN estimate is found to be asymptotically normal. This result readily extends to NN-type estimates for jointly distributed random variables. For illustration, a simplified version of the k - NN estimator is applied to obtain the density estimate for a soil-moisture data set selected from the geostatistical literature
Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management
Pointwise consistency of the kriging predictor with known mean and covariance functions
This paper deals with several issues related to the pointwise consistency of
the kriging predictor when the mean and the covariance functions are known.
These questions are of general importance in the context of computer
experiments. The analysis is based on the properties of approximations in
reproducing kernel Hilbert spaces. We fix an erroneous claim of Yakowitz and
Szidarovszky (J. Multivariate Analysis, 1985) that the kriging predictor is
pointwise consistent for all continuous sample paths under some assumptions.Comment: Submitted to mODa9 (the Model-Oriented Data Analysis and Optimum
Design Conference), 14th-19th June 2010, Bertinoro, Ital
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