1,071 research outputs found
Efficient robust nonparametric estimation in a semimartingale regression model
The paper considers the problem of robust estimating a periodic function in a
continuous time regression model with dependent disturbances given by a general
square integrable semimartingale with unknown distribution. An example of such
a noise is non-gaussian Ornstein-Uhlenbeck process with the L\'evy process
subordinator, which is used to model the financial Black-Scholes type markets
with jumps. An adaptive model selection procedure, based on the weighted least
square estimates, is proposed. Under general moment conditions on the noise
distribution, sharp non-asymptotic oracle inequalities for the robust risks
have been derived and the robust efficiency of the model selection procedure
has been shown
Nonparametric estimation in a semimartingale regression model. Part 2. Robust asymptotic efficiency
In this paper we prove the asymptotic efficiency of the model selection
procedure proposed by the authors in the first part. To this end we introduce
the robust risk as the least upper bound of the quadratical risk over a broad
class of observation distributions. Asymptotic upper and lower bounds for the
robust risk have been derived. The asymptotic efficiency of the procedure is
proved. The Pinsker constant is found
Efficient First-Order Temporal Logic for Infinite-State Systems
In this paper we consider the specification and verification of
infinite-state systems using temporal logic. In particular, we describe
parameterised systems using a new variety of first-order temporal logic that is
both powerful enough for this form of specification and tractable enough for
practical deductive verification. Importantly, the power of the temporal
language allows us to describe (and verify) asynchronous systems, communication
delays and more complex properties such as liveness and fairness properties.
These aspects appear difficult for many other approaches to infinite-state
verification.Comment: 16 pages, 2 figure
On one property of martingales with conditionally Gaussian increments and its application in the theory of nonasymptotic inference
A transformation of a discrete-time martingale with conditionally Gaussian increments into a sequence of i.i.d. standard Gaussian random variables is proposed as based on a sequence of stopping times constructed using the quadratic variation. It is shown that sequential estimators for the parameters in AR(1) and generalized first-order autoregressive models have a nonasymptotic normal distribution
Module extraction via query inseparability in OWL 2 QL
We show that deciding conjunctive query inseparability for OWL 2 QL ontologies is PSpace-hard and in ExpTime. We give polynomial-time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction
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