8220837
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its univariate counterpart. The lower level of interest in multivariate kernel density estimation is mainly due to the increased difficulty in deriving an optimal data-driven bandwidth as the dimension of the data increases. We provide Markov chain Monte Carlo (MCMC) algorithms for estimating optimal bandwidth matrices for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters whose posterior density can be obtained through the likelihood cross-validation criterion. Numerical studies for bivariate data show that the MCMC algorithm generally performs better than the plug-in algorithm under the Kullback-Leibler information criterion, and is as good as the plug-in algorithm under the mean integrated squared error (MISE) criterion. Numerical studies for five-dimensional data show that our algorithm is superior to the normal reference rule. Our MCMC algorithm is the first data-driven bandwidth selector for multivariate kernel density estimation that is applicable to data of any dimension.
194152
A class of nonlinear stochastic volatility models and its implications for pricing currency options
A class of stochastic volatility (SV) models is proposed by applying the Box-Cox transformation to the volatility equation. This class of nonlinear SV (N-SV) models encompasses all standard SV models, including the well-known lognormal (LN) SV model. It allows to empirically compare and test all standard specifications in a very convenient way and provides a measure of the degree of departure from the classical models. A likelihood-based technique is developed for analyzing the model. Daily dollar/pound exchange rate data provide some evidence against LN model and strong evidence against all the other classical specifications. An efficient algorithm is proposed to study the economic importance of the proposed model on pricing currency options.
30931074
A Distributed Object Database's Basic Design Issues
14773046
Predicting methylation status of CpG islands in the human brain
MOTIVATION
Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.


RESULTS
Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database.


AVAILABILITY
An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html


CONTACT
mzhang@cshl.edu


SUPPLEMENTARY INFORMATION
Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.
12113641
Neighbor number, valley seeking and clustering
This paper proposes a novel nonparametric clustering algorithm capable of identifying shape-free clusters. This algorithm is based on a nonparametric estimation of the normalized density derivative (NDD) and the local convexity of the density distribution function, both of which are represented in a very concise form in terms of neighbor numbers. We use NDD to measure the dissimilarity between each pair of observations in a local neighborhood and to build a connectivity graph. Combined with the local convexity, this similarity measure can detect observations in local minima (valleys) of the density function, which separate observations in different major clusters. We demonstrate that this algorithm has a close relationship with the single-linkage hierarchical clustering and can be viewed as its extension. The performance of the algorithm is tested with both synthetic and real datasets. An example of color image segmentation is also given. Comparisons with several representative existing algorithms show that the proposed method can robustly identify major clusters even when there are complex configurations and/or large overlaps.
26512495
Multiscale tumor detection and segmentation in mammograms
Breast cancer is a leading cause of death among women. Automatic computer-aided analysis method can help radiologists to make more reliable and efficient decisions. Tumors and other abnormalities are of basic interests that need to be segmented and extracted in mammograms. In this paper, a new wavelet-packet multiscale image segmentation method is presented first. By combining this new method with a multiscale region-based segmentation method, we develop a new generic systematic scheme to segment suspicious tumor areas with exact boundaries in mammograms based on multiscale analysis in both grayscale and space. Examples are presented to illustrate the effectiveness of the technique. The new segmentation scheme can also be used in analysis of other abnormal tissues in mammograms.
14209049
Maintaining data warehouses over changing information sources
I n recent years, the number of digital information storage and retrieval systems has increased immensely. These information sources are generally interconnected via some network, and hence the task of integrating data from different sources to serve it up to users is an increasingly important one [10]. Applications that could benefit from this wealth of digital information are thus experiencing a pressing need for suitable integration tools that allow them to make effective use of such distributed and diverse data sets. In contrast to the on-demand approach to information integration, the approach of tailored information repository construction, commonly referred to as data warehousing, is characterized by the following properties: