The Experts below are selected from a list of 16917 Experts worldwide ranked by ideXlab platform

Davar Khalili - One of the best experts on this subject based on the ideXlab platform.

  • comprehensive evaluation of regional flood frequency analysis by l and lh moments ii development of lh moments parameters for the generalized pareto and generalized logistic distributions
    2009
    Co-Authors: Ali Meshgi, Davar Khalili
    Abstract:

    As part II of a sequence of two papers, previously developed L-moments by Hosking (1990), and the LH-moments by Wang (1997) are further investigated. The LH-moments (L to L4) are used to develop the regional parameters of the generalized extreme value distribution, generalized Pareto (GPA) distribution and the generalized logistic (GLO) distributions. These Respective Probability distribution functions (PDFs) are evaluated in terms of their performances. Flood peaks by the corresponding PDFs are compared with those generated by Monte Carlo simulation of randomized data, considering the Respective LH-moments. The influence of the LH-moments on estimated PDFs are studied by evaluating the relative bias (RBIAS) in quantile estimation due to variability of the k parameter. Karkhe watershed located in western Iran was used as a case study area. Part I of this study identified the study area as regions A and B. The minimum calculated relative root mean square error (RRMSE) and RBIAS between simulated flood peaks and flood peaks by the corresponding PDFs were used in PDF selection, considering the Respective LH-moments. The boxplots of the RRMSE tests identified the L3 level of the GPA distribution as the suitable PDF for sample sizes 20 and 80; for region A. Similar results were found for the RBIAS test. As for region B, the boxplots of the RRMSE tests indicated similar results for the three PDFs. However, the boxplots of the RBIAS tests identified the L4 level of the GLO most suitable for sample sizes 20 and 80. Relative efficiencies of the LH-moments were investigated, measured as RRMSE ratios of L-moments over the Respective LH-moments. For the most parts the findings of this part of the study were similar to those of part I.

Rahim Moineddin - One of the best experts on this subject based on the ideXlab platform.

  • a monte carlo simulation study comparing linear regression beta regression variable dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design
    2014
    Co-Authors: Christopher Meaney, Rahim Moineddin
    Abstract:

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval - (0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their Respective Probability models. The randomly simulated draws from the various Probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the response data are generated from a discrete multinomial distribution with support on (0,1). The linear regression model, the variable-dispersion beta regression model and the fractional logit regression model all perform well across the simulation experiments under consideration. When employing beta regression to estimate covariate effects on (0,1) response data, researchers should ensure their dispersion sub-model is properly specified, else inferential errors could arise.

Ali Meshgi - One of the best experts on this subject based on the ideXlab platform.

  • comprehensive evaluation of regional flood frequency analysis by l and lh moments ii development of lh moments parameters for the generalized pareto and generalized logistic distributions
    2009
    Co-Authors: Ali Meshgi, Davar Khalili
    Abstract:

    As part II of a sequence of two papers, previously developed L-moments by Hosking (1990), and the LH-moments by Wang (1997) are further investigated. The LH-moments (L to L4) are used to develop the regional parameters of the generalized extreme value distribution, generalized Pareto (GPA) distribution and the generalized logistic (GLO) distributions. These Respective Probability distribution functions (PDFs) are evaluated in terms of their performances. Flood peaks by the corresponding PDFs are compared with those generated by Monte Carlo simulation of randomized data, considering the Respective LH-moments. The influence of the LH-moments on estimated PDFs are studied by evaluating the relative bias (RBIAS) in quantile estimation due to variability of the k parameter. Karkhe watershed located in western Iran was used as a case study area. Part I of this study identified the study area as regions A and B. The minimum calculated relative root mean square error (RRMSE) and RBIAS between simulated flood peaks and flood peaks by the corresponding PDFs were used in PDF selection, considering the Respective LH-moments. The boxplots of the RRMSE tests identified the L3 level of the GPA distribution as the suitable PDF for sample sizes 20 and 80; for region A. Similar results were found for the RBIAS test. As for region B, the boxplots of the RRMSE tests indicated similar results for the three PDFs. However, the boxplots of the RBIAS tests identified the L4 level of the GLO most suitable for sample sizes 20 and 80. Relative efficiencies of the LH-moments were investigated, measured as RRMSE ratios of L-moments over the Respective LH-moments. For the most parts the findings of this part of the study were similar to those of part I.

Christopher Meaney - One of the best experts on this subject based on the ideXlab platform.

  • a monte carlo simulation study comparing linear regression beta regression variable dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design
    2014
    Co-Authors: Christopher Meaney, Rahim Moineddin
    Abstract:

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval - (0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their Respective Probability models. The randomly simulated draws from the various Probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the response data are generated from a discrete multinomial distribution with support on (0,1). The linear regression model, the variable-dispersion beta regression model and the fractional logit regression model all perform well across the simulation experiments under consideration. When employing beta regression to estimate covariate effects on (0,1) response data, researchers should ensure their dispersion sub-model is properly specified, else inferential errors could arise.

Breite Christian - One of the best experts on this subject based on the ideXlab platform.

  • Effect of viscoplasticity of the epoxy matrix on long-term stress redistribution around fibre breaks in a composite subjected to high static tensile load
    2019
    Co-Authors: Breite Christian, Chevalier Jérémy, Pardoen Thomas, Lomov S.v., Gorbatikh L., Swolfs Y., Composites 2019, 7th Eccomas Thematic Conference On The Mechanical Response Of Composites
    Abstract:

    important for the time-dependent failure of composites. The creep strain development was therefore characterized for a commercial epoxy resin system (North Thin Ply Technology 736LT). These measurements were then plugged into a micro-mechanical finite element model. This model represented a standard composite microstructure consisting of T700 carbon fibres at a 50% volume fraction and was used to assess the time-dependent effect of stress redistribution around a fibre break. As the shear stresses in the matrix went down over time, the stress transfer region spreads out in fibre direction, which effectively lowers the stress concentration in the break plane but increases the effective overload in the longitudinal direction. The results of the modelled representative volume element can be translated into a timedependent local load sharing approximation, which is used as input data for the fibre break model from Swolfs et al. [2]. The advanced fibre break model is able to determine the strength degradation over time of a unidirectional composite bundle exposed to high static tensile load. By studying the development of the degradation paired with the Respective Probability for a certain path, better lifetime predictions for continuously loaded components can be made. Modelling predictions are intended to be verified with the help of Synchrotron Computed Tomography at the submicron scale by holding a specimen at a constant displacement. The analysis of the scan data is still ongoing

  • Effect of viscoplasticity of the epoxy matrix on long-term stress redistribution around fibre breaks in a composite subjected to high static tensile load
    2019
    Co-Authors: Breite Christian, Gorbatikh Larissa, Chevalier Jérémy, Pardoen Thomas, Feyen Vincent, Lomov Stepan, Swolfs Yentl
    Abstract:

    The wide-spread opinion persists that epoxy matrices behave in a purely brittle manner. Their plastic behaviour is commonly neglected for modelling of composites. Recent results, however, show the highly strain-dependent plastic behaviour of epoxy and suggest that their creep behaviour is important for the time-dependent failure of composites. The creep strain development was therefore characterized for a commercial epoxy resin system (North Thin Ply Technology 736LT). These measurements were then plugged into a micro-mechanical finite element model. This model represented a standard composite microstructure consisting of T700 carbon fibres at a 50% volume fraction and was used to assess the time-dependent effect of stress redistribution around a fibre break. As the shear stresses in the matrix went down over time, the stress transfer region spreads out in fibre direction, which effectively lowers the stress concentration in the break plane but increases the effective overload in the longitudinal direction. The results of the modelled representative volume element can be translated into a timedependent local load sharing approximation, which is used as input data for the fibre break model from Swolfs et al. The advanced fibre break model is able to determine the strength degradation over time of a unidirectional composite bundle exposed to high static tensile load. By studying the development of the degradation paired with the Respective Probability for a certain path, better lifetime predictions for continuously loaded components can be made. Modelling predictions are intended to be verified with the help of Synchrotron Computed Tomography at the submicron scale by holding a specimen at a constant displacement. The analysis of the scan data is still ongoing.status: publishe