51,685 research outputs found
Re-figuring Federalism: Nation and State in Health Reform's Next Round
Reviews the evolution of national healthcare reform movements and the relationship between the federal and state governments, with international comparisons. Outlines differences to be resolved over Medicaid and other programs under a reformed system
In-season prediction of batting averages: A field test of empirical Bayes and Bayes methodologies
Batting average is one of the principle performance measures for an
individual baseball player. It is natural to statistically model this as a
binomial-variable proportion, with a given (observed) number of qualifying
attempts (called ``at-bats''), an observed number of successes (``hits'')
distributed according to the binomial distribution, and with a true (but
unknown) value of that represents the player's latent ability. This is a
common data structure in many statistical applications; and so the
methodological study here has implications for such a range of applications. We
look at batting records for each Major League player over the course of a
single season (2005). The primary focus is on using only the batting records
from an earlier part of the season (e.g., the first 3 months) in order to
estimate the batter's latent ability, , and consequently, also to predict
their batting-average performance for the remainder of the season. Since we are
using a season that has already concluded, we can then validate our estimation
performance by comparing the estimated values to the actual values for the
remainder of the season. The prediction methods to be investigated are
motivated from empirical Bayes and hierarchical Bayes interpretations. A newly
proposed nonparametric empirical Bayes procedure performs particularly well in
the basic analysis of the full data set, though less well with analyses
involving more homogeneous subsets of the data. In those more homogeneous
situations better performance is obtained from appropriate versions of more
familiar methods. In all situations the poorest performing choice is the
na\"{{\i}}ve predictor which directly uses the current average to predict the
future average.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS138 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
FACTORS THAT INFLUENCE PRICES PRODUCERS RECEIVE FOR HOGS: STATISTICAL ANALYSIS OF KILLSHEET AND SURVEY DATA
This paper evaluates the results of a survey of Iowa pork producers, examining potential price discrimination by packers. Prices varied greatly across producers, and the examined variables explain just over half of the variation. Factors under the producer's control were the most significant variables and accounted for the vast majority of the explainable difference in price among producers. Packer buying systems also accounted for some difference in producer prices. Finally, variables related to operation size, while statistically significant, increased the explanatory values of the equation very little. KEYWORDS: Market access, carcass merit, hog marketing, price determination, price discrimination
Statistical properties of the method of regularization with periodic Gaussian reproducing kernel
The method of regularization with the Gaussian reproducing kernel is popular
in the machine learning literature and successful in many practical
applications.
In this paper we consider the periodic version of the Gaussian kernel
regularization.
We show in the white noise model setting, that in function spaces of very
smooth functions, such as the infinite-order Sobolev space and the space of
analytic functions, the method under consideration is asymptotically minimax;
in finite-order Sobolev spaces, the method is rate optimal, and the efficiency
in terms of constant when compared with the minimax estimator is reasonably
high. The smoothing parameters in the periodic Gaussian regularization can be
chosen adaptively without loss of asymptotic efficiency. The results derived in
this paper give a partial explanation of the success of the
Gaussian reproducing kernel in practice. Simulations are carried out to study
the finite sample properties of the periodic Gaussian regularization.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Statistics
(http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000045
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