13,203 research outputs found
Open Access: Science Publishing as Science Publishing Should Be
Full and unimpeded access (Open Access) to science literature is needed. It is not provided by the traditional subscription-based publishing model. Instead of criticizing Open Access and attacking its proponents, traditional publishers should make imaginative and innovative efforts to build their businesses around the needs of their customers rather than around their desire to continue a model that may be lucrative, but that is no longer satisfactory to science or society
Adaptive circular deconvolution by model selection under unknown error distribution
We consider a circular deconvolution problem, in which the density of a
circular random variable must be estimated nonparametrically based on an
i.i.d. sample from a noisy observation of . The additive measurement
error is supposed to be independent of . The objective of this work was to
construct a fully data-driven estimation procedure when the error density
is unknown. We assume that in addition to the i.i.d. sample from ,
we have at our disposal an additional i.i.d. sample drawn independently from
the error distribution. We first develop a minimax theory in terms of both
sample sizes. We propose an orthogonal series estimator attaining the minimax
rates but requiring optimal choice of a dimension parameter depending on
certain characteristics of and , which are not known in practice.
The main issue addressed in this work is the adaptive choice of this dimension
parameter using a model selection approach. In a first step, we develop a
penalized minimum contrast estimator assuming that the error density is known.
We show that this partially adaptive estimator can attain the lower risk bound
up to a constant in both sample sizes and . Finally, by randomizing the
penalty and the collection of models, we modify the estimator such that it no
longer requires any previous knowledge of the error distribution. Even when
dispensing with any hypotheses on , this fully data-driven estimator
still preserves minimax optimality in almost the same cases as the partially
adaptive estimator. We illustrate our results by computing minimal rates under
classical smoothness assumptions.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ422 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
The role of spurions in Higgs-less electroweak effective theories
Inspired by recent developments of moose models we reconsider low-energy
effective theories of Goldstone bosons, gauge fields and chiral fermions
applied to low-energy QCD and to Higgs-less electroweak symmetry breaking.
Couplings and the corresponding reduction of symmetry are introduced via
constraints enforced by a set of non-propagating covariantly constant spurion
fields. Relics of the latter are used as small expansion parameters conjointly
with the usual low-energy expansion. Certain couplings can only appear at
higher orders of the spurion expansion and consequently, they become naturally
suppressed independently of the idea of dimensional deconstruction.
At leading order this leads to a set of generalized Weinberg sum rules and to
the suppression of non-standard couplings to fermions in Higgs-less EWSB models
with the minimal particle content. Within the latter, higher spurion terms
allow for a fermion mass matrix with the standard CKM structure and CP
violation. In addition, Majorana masses for neutrinos are possible. Examples of
non-minimal models are briefly mentioned.Comment: Some precisions added to section 3.4. Reference [13] added. To appear
in EPJ
Adaptive non-parametric estimation in the presence of dependence
We consider non-parametric estimation problems in the presence of dependent
data, notably non-parametric regression with random design and non-parametric
density estimation. The proposed estimation procedure is based on a dimension
reduction. The minimax optimal rate of convergence of the estimator is derived
assuming a sufficiently weak dependence characterized by fast decreasing mixing
coefficients. We illustrate these results by considering classical smoothness
assumptions. However, the proposed estimator requires an optimal choice of a
dimension parameter depending on certain characteristics of the function of
interest, which are not known in practice. The main issue addressed in our work
is an adaptive choice of this dimension parameter combining model selection and
Lepski's method. It is inspired by the recent work of Goldenshluger and Lepski
(2011). We show that this data-driven estimator can attain the lower risk bound
up to a constant provided a fast decay of the mixing coefficients.Comment: 39 pages, 4 figure
Robust estimation of superhedging prices
We consider statistical estimation of superhedging prices using historical
stock returns in a frictionless market with d traded assets. We introduce a
plugin estimator based on empirical measures and show it is consistent but
lacks suitable robustness. To address this we propose novel estimators which
use a larger set of martingale measures defined through a tradeoff between the
radius of Wasserstein balls around the empirical measure and the allowed norm
of martingale densities. We establish consistency and robustness of these
estimators and argue that they offer a superior performance relative to the
plugin estimator. We generalise the results by replacing the superhedging
criterion with acceptance relative to a risk measure. We further extend our
study, in part, to the case of markets with traded options, to a multiperiod
setting and to settings with model uncertainty. We also study convergence rates
of estimators and convergence of superhedging strategies.Comment: This work will appear in the Annals of Statistics. The above version
merges the main paper to appear in print and its online supplemen
Option Pricing and Hedging with Small Transaction Costs
An investor with constant absolute risk aversion trades a risky asset with
general It\^o-dynamics, in the presence of small proportional transaction
costs. In this setting, we formally derive a leading-order optimal trading
policy and the associated welfare, expressed in terms of the local dynamics of
the frictionless optimizer. By applying these results in the presence of a
random endowment, we obtain asymptotic formulas for utility indifference prices
and hedging strategies in the presence of small transaction costs.Comment: 20 pages, to appear in "Mathematical Finance
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