Search Results
Results 451 - 500 of 1823
< 5 6 7 8 9 10 11 12 13 14 15 >
Navaratna W C W - - 2008
Estimation of population size with missing zero-class is an important problem that is encountered in epidemiological assessment studies. Fitting a Poisson model to the observed data by the method of maximum likelihood and estimation of the population size based on this fit is an approach that has been widely used ...
Tatarinova Tatiana - - 2008
In this paper, we study Bayesian analysis of nonlinear hierarchical mixture models with a finite but unknown number of components. Our approach is based on Markov chain Monte Carlo (MCMC) methods. One of the applications of our method is directed to the clustering problem in gene expression analysis. From a ...
Nelson Joshua D - - 2008
Although open ocean time-series sites have been areas of microbial research for years, relatively little is known about the population dynamics of bacterioplankton communities in the coastal ocean on kilometer spatial and seasonal temporal scales. To gain a better understanding of microbial community variability, monthly samples of bacterial biomass were ...
Suliga M - - 2008
In this paper we propose a new pixel clustering model applied to the analysis of digital mammograms. The clustering represents here the first step in a more general method and aims at the creation of a concise data-set (clusters) for automatic detection and classification of masses, which are typically among ...
Bottai Matteo - - 2008
We present a method for surface estimation over some area of interest using spatial multilevel semiparametric models, in which the spatial correlation is modeled through splines with random coefficients associated with a set of knots. Multiple sets of random effects are associated with partitions of the entire area of interest ...
Irigoien I - - 2008
This paper presents a solution to two problems that arise in the classification of data such as types of tumor, samples of gene expression profiles or general biomedical data. First, to estimate the real number of clusters in a data set and second to decide whether a new unit belongs ...
Schrøder Thomas B - - 2008
Simulations of the random barrier model show that ac currents at extreme disorder are carried almost entirely by the percolating cluster slightly above threshold; thus contributions from isolated low-activation-energy clusters are negligible. The effective medium approximation in conjunction with the Alexander-Orbach conjecture lead to an excellent analytical fit to the ...
Johnson Michael L ML Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA. - - 2008
Hormone signaling is often pulsatile, and multiparameter deconvolution procedures have long been used to identify and characterize secretory events. However, the existing programs have serious limitations, including the subjective nature of initial peak selection, lack of statistical verification of presumed bursts, and user-unfriendliness of the application. Here we describe a ...
McBride Thomas J - - 2008
Computational models predict that experience-driven clustering of coactive synapses is a mechanism for information storage. This prediction has remained untested, because it is difficult to approach through time-lapse analysis. Here, we exploit a unique feature of the barn owl auditory localization pathway that permits retrospective analysis of prelearned and postlearned ...
Moody Michael P - - 2008
The applicability of the binomial frequency distribution is outlined for the analysis of the evolution nanoscale atomic clustering of dilute solute in an alloy subject to thermal ageing in 3D atom probe data. The conventional chi(2) statistics and significance testing are demonstrated to be inappropriate for comparison of quantity of ...
Morris Shaun K - - 2008
Obtaining quality data in a timely manner from humanitarian emergencies is inherently difficult. Conditions of war, famine, population displacement, and other humanitarian disasters, cause limitations in the ability to widely survey. These limitations hold the potential to introduce fatal biases into study results. The cluster sample method is the most ...
Theodosiou T - - 2008
MOTIVATION: Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed ...
Zaliapin Ilya - - 2008
We introduce a statistical methodology for clustering analysis of seismicity in the time-space-energy domain and use it to establish the existence of two statistically distinct populations of earthquakes: clustered and nonclustered. This result can be used, in particular, for nonparametric aftershock identification. The proposed approach expands the analysis of Baiesi ...
Bona M T - - 2008
The aim of this paper focuses on the determination of nine coal properties related to combustion power plants (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal kg(-1)), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) by mid-infrared spectroscopy. For that, a wide and diverse ...
Olier Iván - - 2008
Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. In this paper, the capabilities of Generative Topographic Mapping Through Time, a model with foundations in probability theory, that performs simultaneous time ...
Desai Manisha - - 2008
We used 3 approaches to analyzing clustered data to assess the impact of model choice on interpretation. Approaches 1 and 2 specified random intercept models but differed in standard versus novel specification of covariates, which impacts ability to separate within- and between-cluster effects. Approach 3 was based on standard analysis ...
Yang Miin-Shen - - 2008
In the fuzzy c -means (FCM) clustering algorithm, almost none of the data points have a membership value of 1. Moreover, noise and outliers may cause difficulties in obtaining appropriate clustering results from the FCM algorithm. The embedding of FCM into switching regressions, called the fuzzy c -regressions (FCRs), still ...
Silver Henry - - 2008
Currently, assignment of cognitive test results to particular cognitive domains is guided by theoretical considerations and expert judgments which may vary. More objective means of classification may advance understanding of the relationships between test performance and the cognitive functions probed. We examined whether "atheoretical" analyses of cognitive test data can ...
Brunner Nicolas - - 2008
Given a set of correlations originating from measurements on a quantum state of unknown Hilbert space dimension, what is the minimal dimension d necessary to describe such correlations? We introduce the concept of dimension witness to put lower bounds on d. This work represents a first step in a broader ...
Maitra Ranjan - - 2009
A new methodology is proposed for clustering datasets in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. Our suggested approach is a scheme which, under assumption of homogeneous spherical clusters, iteratively builds ...
Smýkal Petr - - 2008
One hundred and sixty-four accessions representing Czech and Slovak pea (Pisum sativum L.) varieties bred over the last 50 years were evaluated for genetic diversity using morphological, simple sequence repeat (SSR) and retrotransposon-based insertion polymorphism (RBIP) markers. Polymorphic information content (PIC) values of 10 SSR loci and 31 RBIP markers ...
Ozden Ilker - - 2008
In vivo multiphoton fluorescence microscopy allows imaging of cellular structures in brain tissue to depths of hundreds of micrometers and, when combined with the use of activity-dependent indicator dyes, opens the possibility of observing intact, functioning neural circuitry. We have developed tools for analyzing in vivo multiphoton data sets to ...
Yang Kuan - - 2008
With the exponential growth of genomics data, the demand for reliable clustering methods is increasing every day. Despite the wide usage of many clustering algorithms, the accuracy of these algorithms has been evaluated mostly on simulated data sets and seldom on real biological data for which a "correct answer" is ...
Frank K L - - 2008
This study developed a methodology to temporally classify large scale, upper level atmospheric conditions over North America, utilizing a newly-developed upper level synoptic classification (ULSC). Four meteorological variables: geopotential height, specific humidity, and u- and v-wind components, at the 500 hPa level over North America were obtained from the NCEP/NCAR ...
Henrys P A - - 2009
We propose a method to test for significant differences in the levels of clustering between two spatial point processes (cases and controls) while taking into account differences in their first-order intensities. The key advance on earlier methods is that the controls are not assumed to be a Poisson process. Inference ...
Geraci Filippo - - 2008
The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algorithms and inspection of results. Among the salient features AMIC@ offers, there ...
Cheng Yu - - 2009
The work is motivated by the Cache County Study of Aging, a population-based study in Utah, in which sibship associations in dementia onset are of interest. Complications arise because only a fraction of the population ever develops dementia, with the majority dying without dementia. The application of standard dependence analyses ...
Zhang Tonglin - - 2009
Spatial clustering is commonly modeled by a Bayesian method under the framework of generalized linear mixed effect models (GLMMs). Spatial clusters are commonly detected by a frequentist method through hypothesis testing. In this article, we provide a frequentist method for assessing spatial properties of GLMMs. We propose a strategy that ...
Xiang Tao - - 2008
This paper aims to address the problem of modelling video behaviour captured in surveillancevideos for the applications of online normal behaviour recognition and anomaly detection. A novelframework is developed for automatic behaviour profiling and online anomaly sampling/detectionwithout any manual labelling of the training dataset. The framework consists of the followingkey ...
Baumes Laurent A - - 2008
This study shows how chemistry knowledge and reasoning are taken into account for building a new methodology that aims at automatically grouping data having a chronological structure. We consider combinatorial catalytic experiments where the evolution of a reaction (e.g., conversion) over time is expected to be analyzed. The mathematical tool ...
Kuehnert Barbara - - 2008
This study aims for improved understanding of whether and how coordination patterns of supraglottal gestures in complex syllable onsets are driven by competing demands of motor economy for the speaker and high recoverability for the listener. Specifically, EMA data for four German and three French speakers was acquired for C1C2 ...
Cervenka Pierre - - 2008
The prototype of a multibeam front-scan sonar has been developed within the frame of a MAST contract (n degrees MAS3-CT97-0090 DG12-ESCY, acronym COSMOS). A large amount of data has been collected at sea. With the forward looking geometry of acquisition, the foot-prints of successive pings overlap largely, so that most ...
Pan Feng - - 2008
Simultaneously clustering columns and rows (co-clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and ...
Stedman Margaret R - - 2008
Health care quality improvement interventions are often evaluated in randomized trials in which individual physicians serve as the unit of randomization. These cluster randomized trials present a unique data structure that consists of many clusters of highly variable size. The appropriate method of analysis for these trials is unknown. We ...
Cervantes Omar - - 2008
Four beaches that share physiographic characteristics (sandy, wide, and long) but differ in socioeconomic and cultural terms (three are located in northwestern Mexico and one in California, USA) were evaluated by beach users. Surveys (565) composed of 36 questions were handed out to beach users on weekends and holidays in ...
Semidey-Flecha Lymarie - - 2008
First-principles calculations offer a useful complement to experimental approaches for characterizing hydrogen permeance through dense metal membranes. A challenge in applying these methods to disordered alloys is to make quantitative predictions for the net solubility and diffusivity of interstitial H based on the spatially local information that can be obtained ...
Zhang Mengsheng - - 2008
Subspace clustering has attracted great attention due to its capability of finding salient patterns in high dimensional data. Order preserving subspace clusters have been proven to be important in high throughput gene expression analysis, since functionally related genes are often co-expressed under a set of experimental conditions. Such co-expression patterns ...
Kazembe Lawrence N - - 2009
Availability of geo-referenced data has increased applications of spatially explicit models to understand important health problems in developing countries. This study aims to investigate joint and disease-specific spatial clusters of fever and diarrhoea at a highly disaggregate level, while simultaneously estimating the influence of other covariates. Using the 2000 Malawi ...
Yuan Ao - - 2008
Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but ...
Lo Kenneth - - 2008
The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full ...
Tesch Aaron D - - 2008
Delay discounting (DD) is a term typically used to describe the devaluation of rewards over time, and much research across a wide variety of domains has illustrated that people in general prefer a smaller reward delivered soon as opposed to a larger reward delivered at a later stage. Despite numerous ...
Lavanya G Roopa - - 2008
RAPD profiles were used to identify the extent of diversity among 54 accessions of mung bean that included both improved and local land races. Out of the 40 primers screened, seven primers generated 174 amplification products with an average of 24.85 bands per primer. The RAPD profiles were analysed for ...
Coory M - - 2008
The aim of statistical analyses in cluster investigations is to estimate the probability that the aggregation of cases could be due to chance. As a result of several statistical problems - including the post-hoc nature of the analysis and the subjective nature of implied multiple comparisons - this cannot be ...
Cucala L - - 2008
In this article we propose a new technique for identifying clusters in temporal point processes. This relies on the comparision between all the m -order spacings and it is totally independent of any alternative hypothesis. A recursive procedure is introduced and allows to identify multiple clusters independently. This new scan ...
Stanberry Larissa - - 2008
An unsupervised stochastic clustering method based on the ferromagnetic Potts spin model is introduced as a powerful tool to determine functionally connected regions. The method provides an intuitively simple approach to clustering and makes no assumptions of the number of clusters in the data or their underlying distribution. The performance ...
Donald William A - - 2008
The Thomson model, used for calculating thermodynamic properties of cluster ions from macroscopic properties, and variations of this model were compared to each other and to experimental data for both hydrated mono- and divalent ions. Previous models that used the Thomson equation to calculate sequential binding thermodynamic values of hydrated ...
Van den Nest M - - 2008
We prove that the 2D Ising model is complete in the sense that the partition function of any classical q-state spin model (on an arbitrary graph) can be expressed as a special instance of the partition function of a 2D Ising model with complex inhomogeneous couplings and external fields. In ...
Daszykowski M - - 2008
Hyphenated techniques such as capillary electrophoresis-mass spectrometry (CE-MS) or high-performance liquid chromatography with diode array detection (HPLC-DAD), etc., are known to produce a huge amount of data since each sample is characterized by a two-way data table. In this paper different ways of obtaining sample-related information from a set of ...
Foulkes Andrea S - - 2008
This manuscript describes a novel, linear mixed-effects model-fitting technique for the setting in which correlated data indicators are not completely observed. Mixed modeling is a useful analytical tool for characterizing genotype-phenotype associations among multiple potentially informative genetic loci. This approach involves grouping individuals into genetic clusters, where individuals in the ...
Salem S A - - 2008
The self-organizing oscillator network (SOON) is a comparatively new clustering algorithm that does not require the knowledge of the number of clusters. The SOON is distance based, and its clustering behavior is different to density-based algorithms in a number of ways. This paper examines the effect of adjusting the control ...
< 5 6 7 8 9 10 11 12 13 14 15 >