3,096 research outputs found
The semiparametric Bernstein-von Mises theorem
In a smooth semiparametric estimation problem, the marginal posterior for the
parameter of interest is expected to be asymptotically normal and satisfy
frequentist criteria of optimality if the model is endowed with a suitable
prior. It is shown that, under certain straightforward and interpretable
conditions, the assertion of Le Cam's acclaimed, but strictly parametric,
Bernstein-von Mises theorem [Univ. California Publ. Statist. 1 (1953) 277-329]
holds in the semiparametric situation as well. As a consequence, Bayesian
point-estimators achieve efficiency, for example, in the sense of H\'{a}jek's
convolution theorem [Z. Wahrsch. Verw. Gebiete 14 (1970) 323-330]. The model is
required to satisfy differentiability and metric entropy conditions, while the
nuisance prior must assign nonzero mass to certain Kullback-Leibler
neighborhoods [Ghosal, Ghosh and van der Vaart Ann. Statist. 28 (2000)
500-531]. In addition, the marginal posterior is required to converge at
parametric rate, which appears to be the most stringent condition in examples.
The results are applied to estimation of the linear coefficient in partial
linear regression, with a Gaussian prior on a smoothness class for the
nuisance.Comment: Published in at http://dx.doi.org/10.1214/11-AOS921 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Development of flight check-out system Final report
Flight checkout system breadboard design, construction and testin
On a semiparametric survival model with flexible covariate effect.
A semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting estimator is n1/2-consistent, asymptotically normal and achieves the semiparametric efficiency bound. An estimation procedure for the nonparametric part is also given and its asymptotic properties are derived. We provide an application to mortality data.
The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?
We consider the Bayesian analysis of a few complex, high-dimensional models
and show that intuitive priors, which are not tailored to the fine details of
the model and the estimated parameters, produce estimators which perform poorly
in situations in which good, simple frequentist estimators exist. The models we
consider are: stratified sampling, the partial linear model, linear and
quadratic functionals of white noise and estimation with stopping times. We
present a strong version of Doob's consistency theorem which demonstrates that
the existence of a uniformly -consistent estimator ensures that the
Bayes posterior is -consistent for values of the parameter in subsets
of prior probability 1. We also demonstrate that it is, at least, in principle,
possible to construct Bayes priors giving both global and local minimax rates,
using a suitable combination of loss functions. We argue that there is no
contradiction in these apparently conflicting findings.Comment: Published in at http://dx.doi.org/10.1214/14-STS483 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
An efficiency upper bound for inverse covariance estimation
We derive an upper bound for the efficiency of estimating entries in the
inverse covariance matrix of a high dimensional distribution. We show that in
order to approximate an off-diagonal entry of the density matrix of a
-dimensional Gaussian random vector, one needs at least a number of samples
proportional to . Furthermore, we show that with samples, the
hypothesis that two given coordinates are fully correlated, when all other
coordinates are conditioned to be zero, cannot be told apart from the
hypothesis that the two are uncorrelated.Comment: 7 Page
Face vs. empathy: the social foundation of Maithili verb agreement
Maithili features one of the most complex agreement systems of any Indo-Aryan language. Not only nominative and non-nominative subjects, but also objects, other core arguments, and even nonarguments are cross-referenced, allowing for a maximum of three participants encoded by the verb desinences. The categories reflected in the morphology are person, honorific degree, and, in the case of third persons, gender, spatial distance, and focus. However, not all combinations of category choices are equally represented, and there are many cases of neutralization. We demonstrate that the paradigm structure of Maithili verb agreement is not arbitrary but can be predicted by two general principles of interaction in Maithil society: a principle of social hierarchy underlying the evaluation of people's "face” (Brown and Levinson 1987[1978]), and a principle of social solidarity defining degrees of "empathy” (Kuno 1987) to which people identify with others. Maithili verb agreement not only reflects a specific style of social cognition but also constitutes a prime means of maintaining this style by requiring constant attention to its defining parameters. In line with this, we find that the system is partly reduced by uneducated, so-called lower-caste speakers, who are least interested in maintaining this style, especially its emphasis on hierarch
Randomization tests in language typology
Two of the major assumptions that common statistical tests make about random sampling and distribution of the data are not tenable for most typological data. We suggest to use randomization tests, which avoid these assumptions. Randomization is applicable to frequency data, rank data, scalar measurements, and ratings, so most typological data can be analyzed with the same tools. We provided a free computer program, which also includes routines that help determine the degree to which a statistical conclusion is reliable or dependent on a few languages in the sampl
Pivotal estimation in high-dimensional regression via linear programming
We propose a new method of estimation in high-dimensional linear regression
model. It allows for very weak distributional assumptions including
heteroscedasticity, and does not require the knowledge of the variance of
random errors. The method is based on linear programming only, so that its
numerical implementation is faster than for previously known techniques using
conic programs, and it allows one to deal with higher dimensional models. We
provide upper bounds for estimation and prediction errors of the proposed
estimator showing that it achieves the same rate as in the more restrictive
situation of fixed design and i.i.d. Gaussian errors with known variance.
Following Gautier and Tsybakov (2011), we obtain the results under weaker
sensitivity assumptions than the restricted eigenvalue or assimilated
conditions
State of the Art on Stylized Fabrication
© 2018 The Authors Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as ‘stylized fabrication methods’. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion or to devise a particular interaction with the fabricated model. In this state-of-the-art report, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research
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