27,029 research outputs found
Early stopping for statistical inverse problems via truncated SVD estimation
We consider truncated SVD (or spectral cut-off, projection) estimators for a
prototypical statistical inverse problem in dimension . Since calculating
the singular value decomposition (SVD) only for the largest singular values is
much less costly than the full SVD, our aim is to select a data-driven
truncation level only based on the knowledge of
the first singular values and vectors. We analyse in detail
whether sequential {\it early stopping} rules of this type can preserve
statistical optimality. Information-constrained lower bounds and matching upper
bounds for a residual based stopping rule are provided, which give a clear
picture in which situation optimal sequential adaptation is feasible. Finally,
a hybrid two-step approach is proposed which allows for classical oracle
inequalities while considerably reducing numerical complexity.Comment: slightly modified version. arXiv admin note: text overlap with
arXiv:1606.0770
Model-independent pricing with insider information: a Skorokhod embedding approach
In this paper, we consider the pricing and hedging of a financial derivative
for an insider trader, in a model-independent setting. In particular, we
suppose that the insider wants to act in a way which is independent of any
modelling assumptions, but that she observes market information in the form of
the prices of vanilla call options on the asset. We also assume that both the
insider's information, which takes the form of a set of impossible paths, and
the payoff of the derivative are time-invariant. This setup allows us to adapt
recent work of Beiglboeck, Cox and Huesmann (2016) to prove duality results and
a monotonicity principle, which enables us to determine geometric properties of
the optimal models. Moreover, we show that this setup is powerful, in that we
are able to find analytic and numerical solutions to certain pricing and
hedging problems
Pandora's Box Problem with Order Constraints
The Pandora's Box Problem, originally formalized by Weitzman in 1979, models
selection from set of random, alternative options, when evaluation is costly.
This includes, for example, the problem of hiring a skilled worker, where only
one hire can be made, but the evaluation of each candidate is an expensive
procedure. Weitzman showed that the Pandora's Box Problem admits an elegant,
simple solution, where the options are considered in decreasing order of
reservation value,i.e., the value that reduces to zero the expected marginal
gain for opening the box. We study for the first time this problem when order -
or precedence - constraints are imposed between the boxes. We show that,
despite the difficulty of defining reservation values for the boxes which take
into account both in-depth and in-breath exploration of the various options,
greedy optimal strategies exist and can be efficiently computed for tree-like
order constraints. We also prove that finding approximately optimal adaptive
search strategies is NP-hard when certain matroid constraints are used to
further restrict the set of boxes which may be opened, or when the order
constraints are given as reachability constraints on a DAG. We complement the
above result by giving approximate adaptive search strategies based on a
connection between optimal adaptive strategies and non-adaptive strategies with
bounded adaptivity gap for a carefully relaxed version of the problem
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