Home About us Contact
|
Home About us Contact | ||
|
|
||
Scale Parameter (scale + parameter)
Selected AbstractsMulti-scale Feature Extraction on Point-Sampled SurfacesCOMPUTER GRAPHICS FORUM, Issue 3 2003Mark Pauly We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructuredpoint cloud as input, our method first applies principal component analysis on local neighborhoods toclassify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we thencompute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the featureswhile maintaining a close connection to the underlying surface, we use an adaptation of active contour models.Central to our method is a multi-scale classification operator that allows feature analysis at multiplescales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves thereliability of the detection phase and makes our method more robust in the presence of noise. To illustrate theusefulness of our method, we have implemented a non-photorealistic point renderer to visualize point-sampledsurfaces as line drawings of their extracted feature curves. [source] Stochastic computational modelling of highly heterogeneous poroelastic media with long-range correlationsINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 1 2004Diego G. Frias Abstract The compaction of highly heterogeneous poroelastic reservoirs with the geology characterized by long-range correlations displaying fractal character is investigated within the framework of the stochastic computational modelling. The influence of reservoir heterogeneity upon the magnitude of the stresses induced in the porous matrix during fluid withdrawal and rock consolidation is analysed by performing ensemble averages over realizations of a log-normally distributed stationary random hydraulic conductivity field. Considering the statistical distribution of this parameter characterized by a coefficient of variation governing the magnitude of heterogeneity and a correlation function which decays with a power-law scaling behaviour we show that the combination of these two effects result in an increase in the magnitude of effective stresses of the rock during reservoir depletion. Further, within the framework of a perturbation analysis we show that the randomness in the hydraulic conductivity gives rise to non-linear corrections in the upscaled poroelastic equations. These corrections are illustrated by a self-consistent recursive hierarchy of solutions of the stochastic poroelastic equations parametrized by a scale parameter representing the fluctuating log-conductivity standard deviation. A classical example of land subsidence caused by fluid extraction of a weak reservoir is numerically simulated by performing Monte Carlo simulations in conjunction with finite elements discretizations of the poroelastic equations associated with an ensemble of geologies. Numerical results illustrate the effects of the spatial variability and fractal character of the permeability distribution upon the evolution of the Mohr,Coulomb function of the rock. Copyright © 2004 John Wiley & Sons, Ltd. [source] The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statisticsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2009D. Maraun Abstract The annual cycle of extreme 1-day precipitation events across the UK is investigated by developing a statistical model and fitting it to data from 689 rain gauges. A generalized extreme-value distribution (GEV) is fit to the time series of monthly maxima, across all months of the year simultaneously, by approximating the annual cycles of the location and scale parameters by harmonic functions, while keeping the shape parameter constant throughout the year. We average the shape parameter of neighbouring rain gauges to decrease parameter uncertainties, and also interpolate values of all model parameters to give complete coverage of the UK. The model reveals distinct spatial patterns for the estimated parameters. The annual mean of the location and scale parameter is highly correlated with orography. The annual cycle of the location parameter is strong in the northwest UK (peaking in late autumn or winter) and in East Anglia (where it peaks in late summer), and low in the Midlands. The annual cycle of the scale parameter exhibits a similar pattern with strongest amplitudes in East Anglia. The spatial patterns of the annual cycle phase suggest that they are linked to the dominance of frontal precipitation for generating extreme precipitation in the west and convective precipitation in the southeast of the UK. The shape parameter shows a gradient from positive values in the east to negative values in some areas of the west. We also estimate 10-year and 100-year return levels at each rain gauge, and interpolated across the UK. Copyright © 2008 Royal Meteorological Society [source] Comparison of updating techniques in transferability analysis of work trip mode choice models in developing countriesJOURNAL OF ADVANCED TRANSPORTATION, Issue 2 2010Djoen San Santoso Abstract This study analyzes the performances of updating techniques in transferability of mode choice models in developing countries. A model specification, estimated in Ho Chi Minh City, was transferred to Phnom Penh. Naïve transfer and four updating methods associated with small sized samples were used in the transfer process and were evaluated based on statistical perspective and predictive ability. The study also illustrates the problems faced in model transferability development, due to the lack of available and suitable data in Phnom Penh. This lack is strongly related to different methods and structures applied in collecting the data. Simplified approaches to the difficulties are proposed in the study. The results show that updating ASCs, updating both ASCs and scale parameter, and use of combined transfer estimators all produce significant improvement, both statistically and in predictability, in updating the model. The last two methods have proven to be superior to the first method, owing to the inclusion of transfer bias considerations in the estimations. However, small data samples should not have large transfer bias when using combined transfer estimators. It is also concluded that naïvely transferring a model is not recommended, and Bayesian updating should be avoided when transfer bias exists. Copyright © 2010 John Wiley & Sons, Ltd. [source] A study of time-between-events control chart for the monitoring of regularly maintained systemsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2009Michael B. C. Khoo Abstract Owing to usage, environment and aging, the condition of a system deteriorates over time. Regular maintenance is often conducted to restore its condition and to prevent failures from occurring. In this kind of a situation, the process is considered to be stable, thus statistical process control charts can be used to monitor the process. The monitoring can help in making a decision on whether further maintenance is worthwhile or whether the system has deteriorated to a state where regular maintenance is no longer effective. When modeling a deteriorating system, lifetime distributions with increasing failure rate are more appropriate. However, for a regularly maintained system, the failure time distribution can be approximated by the exponential distribution with an average failure rate that depends on the maintenance interval. In this paper, we adopt a modification for a time-between-events control chart, i.e. the exponential chart for monitoring the failure process of a maintained Weibull distributed system. We study the effect of changes on the scale parameter of the Weibull distribution while the shape parameter remains at the same level on the sensitivity of the exponential chart. This paper illustrates an approach of integrating maintenance decision with statistical process monitoring methods. Copyright © 2008 John Wiley & Sons, Ltd. [source] Development of design flood hydrographs using probability density functionsHYDROLOGICAL PROCESSES, Issue 4 2010Niranjan Pramanik Abstract Probability density functions (PDFs) are used to fit the shape of hydrographs and have been popularly used for the development of synthetic unit hydrographs by many hydrologists. Nevertheless, modelling the shapes of continuous stream flow hydrographs, which are probabilistic in nature, is rare. In the present study, a novel approach was followed to model the shape of stream flow hydrographs using PDF and subsequently to develop design flood hydrographs for various return periods. Four continuous PDFs, namely, two parameter Beta, Weibull, Gamma and Lognormal, were employed to fit the shape of the hydrographs of 22 years at a site of Brahmani River in eastern India. The shapes of the observed and PDF fitted hydrographs were compared and root mean square errors, error of peak discharge (EQP) and error of time to peak (ETP) were computed. The best-fitted shape and scale parameters of all PDFs were subjected to frequency analysis and the quartiles corresponding to 20-, 50-, 100- and 200-year were estimated. The estimated parameters of each return period were used to develop the flood hydrographs for 20-, 50-, 100- and 200-year return periods. The peak discharges of the developed design flood hydrographs were compared with the design discharges estimated from the frequency analysis of 22 years of annual peak discharges at that site. Lognormal-produced peak discharge was very close to the estimated design discharge in case of 20-year flood hydrograph. On the other hand, peak discharge obtained using the Weibull PDF had close agreement with the estimated design discharge obtained from frequency analysis in case of 50-, 100- and 200-year return periods. The ranking of the PDFs based on estimation of peak of design flood hydrograph for 50-, 100- and 200-year return periods was found to have the following order: Weibull > Beta > Lognormal > Gamma. Copyright © 2009 John Wiley & Sons, Ltd. [source] The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statisticsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2009D. Maraun Abstract The annual cycle of extreme 1-day precipitation events across the UK is investigated by developing a statistical model and fitting it to data from 689 rain gauges. A generalized extreme-value distribution (GEV) is fit to the time series of monthly maxima, across all months of the year simultaneously, by approximating the annual cycles of the location and scale parameters by harmonic functions, while keeping the shape parameter constant throughout the year. We average the shape parameter of neighbouring rain gauges to decrease parameter uncertainties, and also interpolate values of all model parameters to give complete coverage of the UK. The model reveals distinct spatial patterns for the estimated parameters. The annual mean of the location and scale parameter is highly correlated with orography. The annual cycle of the location parameter is strong in the northwest UK (peaking in late autumn or winter) and in East Anglia (where it peaks in late summer), and low in the Midlands. The annual cycle of the scale parameter exhibits a similar pattern with strongest amplitudes in East Anglia. The spatial patterns of the annual cycle phase suggest that they are linked to the dominance of frontal precipitation for generating extreme precipitation in the west and convective precipitation in the southeast of the UK. The shape parameter shows a gradient from positive values in the east to negative values in some areas of the west. We also estimate 10-year and 100-year return levels at each rain gauge, and interpolated across the UK. Copyright © 2008 Royal Meteorological Society [source] A general class of hierarchical ordinal regression models with applications to correlated roc analysisTHE CANADIAN JOURNAL OF STATISTICS, Issue 4 2000Hemant Ishwaran Abstract The authors discuss a general class of hierarchical ordinal regression models that includes both location and scale parameters, allows link functions to be selected adaptively as finite mixtures of normal cumulative distribution functions, and incorporates flexible correlation structures for the latent scale variables. Exploiting the well-known correspondence between ordinal regression models and parametric ROC (Receiver Operating Characteristic) curves makes it possible to use a hierarchical ROC (HROC) analysis to study multilevel clustered data in diagnostic imaging studies. The authors present a Bayesian approach to model fitting using Markov chain Monte Carlo methods and discuss HROC applications to the analysis of data from two diagnostic radiology studies involving multiple interpreters. RÉSUMÉ Les auteurs s'intéressent à une classe assez vaste de modèles de régression ordinale avec paramètres de localisation et d'échelle, laquelle permet la sélection adaptative de fonctions de lien s'exprimant comme mélanges finis de fonctions de répartition normales et fournit des structures de correlation flexibles pour les variables d'échelle latentes. En exploitant la correspondance bien connue entre les modèles de régression ordinale et les courbes d'efficacité paramétriques (CEP) des tests diagnostiques, il est possible d'analyser des données d'imagerie médicate diagnostique regroupées à plusieurs niveaux au moyen d'une CEP hiéiarchique. Les auteurs décrivent une approche bayésienne pour l'ajustement de tels modèles au moyen des méthodes de Monte Carlo à cha,ne de Markov et présentent deux applications concrètes concernant l'interprétation de clichés radiologiques [source] Bayesian Semiparametric Multiple ShrinkageBIOMETRICS, Issue 2 2010Richard F. MacLehose Summary High-dimensional and highly correlated data leading to non- or weakly identified effects are commonplace. Maximum likelihood will typically fail in such situations and a variety of shrinkage methods have been proposed. Standard techniques, such as ridge regression or the lasso, shrink estimates toward zero, with some approaches allowing coefficients to be selected out of the model by achieving a value of zero. When substantive information is available, estimates can be shrunk to nonnull values; however, such information may not be available. We propose a Bayesian semiparametric approach that allows shrinkage to multiple locations. Coefficients are given a mixture of heavy-tailed double exponential priors, with location and scale parameters assigned Dirichlet process hyperpriors to allow groups of coefficients to be shrunk toward the same, possibly nonzero, mean. Our approach favors sparse, but flexible, structure by shrinking toward a small number of random locations. The methods are illustrated using a study of genetic polymorphisms and Parkinson's disease. [source] Estimating a Multivariate Familial Correlation Using Joint Models for Canonical Correlations: Application to Memory Score Analysis from Familial Hispanic Alzheimer's Disease StudyBIOMETRICS, Issue 2 2009Hye-Seung Lee Summary Analysis of multiple traits can provide additional information beyond analysis of a single trait, allowing better understanding of the underlying genetic mechanism of a common disease. To accommodate multiple traits in familial correlation analysis adjusting for confounders, we develop a regression model for canonical correlation parameters and propose joint modeling along with mean and scale parameters. The proposed method is more powerful than the regression method modeling pairwise correlations because it captures familial aggregation manifested in multiple traits through maximum canonical correlation. [source] | |||||||||||||