18,297 research outputs found
A Framework on Moment Model Reduction for Kinetic Equation
By a further investigation on the structure of the coefficient matrix of the
globally hyperbolic regularized moment equations for Boltzmann equation in [Z.
Cai, Y. Fan and R. Li, Comm. Math. Sci., 11 (2013), pp. 547-571], we propose a
uniform framework to carry out model reduction to general kinetic equations, to
achieve certain moment system. With this framework, the underlying reason why
the globally hyperbolic regularization in [Z. Cai, Y. Fan and R. Li, Comm.
Math. Sci., 11 (2013), pp. 547-571] works is revealed. The even fascinating
point is, with only routine calculation, existing models are represented and
brand new models are discovered. Even if the study is restricted in the scope
of the classical Grad's 13-moment system, new model with global hyperbolicity
can be deduced.Comment: 22 page
Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
This paper proposes a new method for estimating sparse precision matrices in
the high dimensional setting. It has been popular to study fast computation and
adaptive procedures for this problem. We propose a novel approach, called
Sparse Column-wise Inverse Operator, to address these two issues. We analyze an
adaptive procedure based on cross validation, and establish its convergence
rate under the Frobenius norm. The convergence rates under other matrix norms
are also established. This method also enjoys the advantage of fast computation
for large-scale problems, via a coordinate descent algorithm. Numerical merits
are illustrated using both simulated and real datasets. In particular, it
performs favorably on an HIV brain tissue dataset and an ADHD resting-state
fMRI dataset.Comment: Maintext: 24 pages. Supplement: 13 pages. R package scio implementing
the proposed method is available on CRAN at
https://cran.r-project.org/package=scio . Published in J of Multivariate
Analysis at
http://www.sciencedirect.com/science/article/pii/S0047259X1400260
On the Performance of NOMA with Hybrid ARQ
In this paper, we investigate the outage performance of hybrid automatic
repeat request with chase combining (HARQ-CC) assisted downlink non-orthogonal
multiple access (NOMA) systems. A closed-form expression of the individual
outage probability and the diversity gain are obtained firstly. Based on the
developed analytical outage probability, a tradeoff between the minimum number
of retransmissions and the transmit power allocation coefficient is then
provided for a given target rate. The provided simulation results demonstrate
the accuracy of the developed analytical results. Moreover, it is shown that
NOMA combined with the HARQ-CC can achieve a significant advantage when only
average channel state information is known at the transmitter. Particularly,
the performance of the user with less transmit power in NOMA systems can be
efficiently improved by utilizing HARQ-CC
Hazard models with varying coefficients for multivariate failure time data
Statistical estimation and inference for marginal hazard models with varying
coefficients for multivariate failure time data are important subjects in
survival analysis. A local pseudo-partial likelihood procedure is proposed for
estimating the unknown coefficient functions. A weighted average estimator is
also proposed in an attempt to improve the efficiency of the estimator. The
consistency and asymptotic normality of the proposed estimators are established
and standard error formulas for the estimated coefficients are derived and
empirically tested. To reduce the computational burden of the maximum local
pseudo-partial likelihood estimator, a simple and useful one-step estimator is
proposed. Statistical properties of the one-step estimator are established and
simulation studies are conducted to compare the performance of the one-step
estimator to that of the maximum local pseudo-partial likelihood estimator. The
results show that the one-step estimator can save computational cost without
compromising performance both asymptotically and empirically and that an
optimal weighted average estimator is more efficient than the maximum local
pseudo-partial likelihood estimator. A data set from the Busselton Population
Health Surveys is analyzed to illustrate our proposed methodology.Comment: Published at http://dx.doi.org/10.1214/009053606000001145 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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