1,162 research outputs found
Optimal variance estimation without estimating the mean function
We study the least squares estimator in the residual variance estimation
context. We show that the mean squared differences of paired observations are
asymptotically normally distributed. We further establish that, by regressing
the mean squared differences of these paired observations on the squared
distances between paired covariates via a simple least squares procedure, the
resulting variance estimator is not only asymptotically normal and root-
consistent, but also reaches the optimal bound in terms of estimation variance.
We also demonstrate the advantage of the least squares estimator in comparison
with existing methods in terms of the second order asymptotic properties.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ432 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
A Belief Propagation Based Framework for Soft Multiple-Symbol Differential Detection
Soft noncoherent detection, which relies on calculating the \textit{a
posteriori} probabilities (APPs) of the bits transmitted with no channel
estimation, is imperative for achieving excellent detection performance in
high-dimensional wireless communications. In this paper, a high-performance
belief propagation (BP)-based soft multiple-symbol differential detection
(MSDD) framework, dubbed BP-MSDD, is proposed with its illustrative application
in differential space-time block-code (DSTBC)-aided ultra-wideband impulse
radio (UWB-IR) systems. Firstly, we revisit the signal sampling with the aid of
a trellis structure and decompose the trellis into multiple subtrellises.
Furthermore, we derive an APP calculation algorithm, in which the
forward-and-backward message passing mechanism of BP operates on the
subtrellises. The proposed BP-MSDD is capable of significantly outperforming
the conventional hard-decision MSDDs. However, the computational complexity of
the BP-MSDD increases exponentially with the number of MSDD trellis states. To
circumvent this excessive complexity for practical implementations, we
reformulate the BP-MSDD, and additionally propose a Viterbi algorithm
(VA)-based hard-decision MSDD (VA-HMSDD) and a VA-based soft-decision MSDD
(VA-SMSDD). Moreover, both the proposed BP-MSDD and VA-SMSDD can be exploited
in conjunction with soft channel decoding to obtain powerful iterative
detection and decoding based receivers. Simulation results demonstrate the
effectiveness of the proposed algorithms in DSTBC-aided UWB-IR systems.Comment: 14 pages, 12 figures, 3 tables, accepted to appear on IEEE
Transactions on Wireless Communications, Aug. 201
A statistical normalization method and differential expression analysis for RNA-seq data between different species
Background: High-throughput techniques bring novel tools but also statistical
challenges to genomic research. Identifying genes with differential expression
between different species is an effective way to discover evolutionarily
conserved transcriptional responses. To remove systematic variation between
different species for a fair comparison, the normalization procedure serves as
a crucial pre-processing step that adjusts for the varying sample sequencing
depths and other confounding technical effects.
Results: In this paper, we propose a scale based normalization (SCBN) method
by taking into account the available knowledge of conserved orthologous genes
and hypothesis testing framework. Considering the different gene lengths and
unmapped genes between different species, we formulate the problem from the
perspective of hypothesis testing and search for the optimal scaling factor
that minimizes the deviation between the empirical and nominal type I errors.
Conclusions: Simulation studies show that the proposed method performs
significantly better than the existing competitor in a wide range of settings.
An RNA-seq dataset of different species is also analyzed and it coincides with
the conclusion that the proposed method outperforms the existing method. For
practical applications, we have also developed an R package named "SCBN" and
the software is available at
http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the performance of recognition and robust feature
representation has not been well studied. In this paper, we propose to leverage
intra-class variance in metric learning of triplet network to improve the
performance of fine-grained recognition. Through partitioning training images
within each category into a few groups, we form the triplet samples across
different categories as well as different groups, which is called Group
Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is
strengthened by incorporating intra-class variance with GS-TRS, which may
contribute to the optimization objective of triplet network. Extensive
experiments over benchmark datasets CompCar and VehicleID show that the
proposed GS-TRS has significantly outperformed state-of-the-art approaches in
both classification and retrieval tasks.Comment: 6 pages, 5 figure
- …