1,128 research outputs found
Asymptotic minimax risk of predictive density estimation for non-parametric regression
We consider the problem of estimating the predictive density of future
observations from a non-parametric regression model. The density estimators are
evaluated under Kullback--Leibler divergence and our focus is on establishing
the exact asymptotics of minimax risk in the case of Gaussian errors. We derive
the convergence rate and constant for minimax risk among Bayesian predictive
densities under Gaussian priors and we show that this minimax risk is
asymptotically equivalent to that among all density estimators.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ222 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Digital RoF Aided Cooperative Distributed Antennas with FFR in Multicell Multiuser Networks
The achievable throughput of the entire cellular area is investigated, when employing fractional frequency reuse techniques in conjunction with realistically modelled imperfect optical fibre aided distributed antenna systems (DAS). Given a fixed total transmit power, a substantial improvement of the cell-edge area’s throughput can be achieved without reducing the cell-centre’s throughput. The cell-edge’s throughput supported in the worst-case direction is significantly enhanced by the cooperative linear transmit processing technique advocated. Explicitly, a cell-edge throughput of η = 5 bits/s/Hz may be maintained for a imperfect optical fibre model, regardless of the specific geographic distribution of the users
Improved minimax predictive densities under Kullback--Leibler loss
Let and be
independent p-dimensional multivariate normal vectors with common unknown mean
. Based on only observing , we consider the problem of obtaining a
predictive density for that is close to as
measured by expected Kullback--Leibler loss. A natural procedure for this
problem is the (formal) Bayes predictive density
under the uniform prior , which is best
invariant and minimax. We show that any Bayes predictive density will be
minimax if it is obtained by a prior yielding a marginal that is superharmonic
or whose square root is superharmonic. This yields wide classes of minimax
procedures that dominate , including Bayes
predictive densities under superharmonic priors. Fundamental similarities and
differences with the parallel theory of estimating a multivariate normal mean
under quadratic loss are described.Comment: Published at http://dx.doi.org/10.1214/009053606000000155 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Imperfect Digital Fibre Optic Link Based Cooperative Distributed Antennas with Fractional Frequency Reuse in Multicell Multiuser Networks
The achievable throughput of the entire cellular area is investigated, when employing fractional frequency reuse techniques in conjunction with realistically modelled imperfect optical fibre aided distributed antenna systems (DAS) operating in a multicell multiuser scenario. Given a fixed total transmit power, a substantial improvement of the cell-edge area's throughput can be achieved without reducing the cell-centre's throughput. The cell-edge's throughput supported in the worst-case direction is significantly enhanced by the cooperative linear transmit processing technique advocated. Explicitly, a cell-edge throughput of bits/s/Hz may be maintained for an imperfect optical fibre model, regardless of the specific geographic distribution of the users
Admissible predictive density estimation
Let and be independent
-dimensional multivariate normal vectors with common unknown mean .
Based on observing , we consider the problem of estimating the true
predictive density of under expected Kullback--Leibler loss. Our
focus here is the characterization of admissible procedures for this problem.
We show that the class of all generalized Bayes rules is a complete class, and
that the easily interpretable conditions of Brown and Hwang [Statistical
Decision Theory and Related Topics (1982) III 205--230] are sufficient for a
formal Bayes rule to be admissible.Comment: Published in at http://dx.doi.org/10.1214/07-AOS506 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Two Rules on the Protein-Ligand Interaction
So far, we still lack a clear molecular mechanism to explain the protein-ligand interaction on the basis of electronic structure of a protein. By combining the calculation of the full electronic structure of a protein along with its hydrophobic pocket and the perturbation theory, we found out two rules on the protein-ligand interaction. One rule is the interaction only occurs between the lowest unoccupied molecular orbitals (LUMOs) of a protein and the highest occupied molecular orbital (HOMO) of its ligand, not between the HOMOs of a protein and the LUMO of its ligand. The other rule is only those residues or atoms located both on the LUMOs of a protein and in a surface pocket of a protein are activity residues or activity atoms of the protein and the corresponding pocket is the ligand binding site. These two rules are derived from the characteristics of energy levels of a protein and might be an important criterion of drug design
Effects of practical impairments on cooperative distributed antennas combined with fractional frequency reuse
Cooperative Multiple Point (CoMP) transmission aided Distributed Antenna Systems (DAS) are proposed for increasing the received Signal-to-Interference-plus-Noise-Ratio (SINR) in the cell-edge area of a cellular system employing Fractional Frequency Reuse (FFR) in the presence of realistic imperfect Channel State Information (CSI) as well as synchronisation errors between the transmitters and the receivers. Our simulation results demonstrate that the CoMP aided DAS scenario is capable of increasing the attainable SINR by up to 3dB in the presence of a wide range of realistic imperfections
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