9,391 research outputs found
Parity-expanded variational analysis for non-zero momentum
In recent years, the use of variational analysis techniques in lattice QCD
has been demonstrated to be successful in the investigation of the rest-mass
spectrum of many hadrons. However, due to parity-mixing, more care must be
taken for investigations of boosted states to ensure that the projected
correlation functions provided by the variational analysis correspond to the
same states at zero momentum. In this paper we present the Parity-Expanded
Variational Analysis (PEVA) technique, a novel method for ensuring the
successful and consistent isolation of boosted baryons through a parity
expansion of the operator basis used to construct the correlation matrix.Comment: 9 pages, 3 figures, 1 tabl
Variational Analysis of Constrained M-Estimators
We propose a unified framework for establishing existence of nonparametric
M-estimators, computing the corresponding estimates, and proving their strong
consistency when the class of functions is exceptionally rich. In particular,
the framework addresses situations where the class of functions is complex
involving information and assumptions about shape, pointwise bounds, location
of modes, height at modes, location of level-sets, values of moments, size of
subgradients, continuity, distance to a "prior" function, multivariate total
positivity, and any combination of the above. The class might be engineered to
perform well in a specific setting even in the presence of little data. The
framework views the class of functions as a subset of a particular metric space
of upper semicontinuous functions under the Attouch-Wets distance. In addition
to allowing a systematic treatment of numerous M-estimators, the framework
yields consistency of plug-in estimators of modes of densities, maximizers of
regression functions, level-sets of classifiers, and related quantities, and
also enables computation by means of approximating parametric classes. We
establish consistency through a one-sided law of large numbers, here extended
to sieves, that relaxes assumptions of uniform laws, while ensuring global
approximations even under model misspecification
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