The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Mark E. Orazem - One of the best experts on this subject based on the ideXlab platform.

  • Identification of Resistivity Distributions in Dielectric Layers by Measurement Model Analysis of Impedance Spectroscopy
    Electrochimica Acta, 2016
    Co-Authors: Yu-min Chen, Mark E. Orazem, Bernard Tribollet, Anh Nguyen, Nadine Pébère, Marco Musiani, Vincent Vivier
    Abstract:

    The Voigt measurement model, developed in the 1990s for identification of the Error Structure of impedance measurements, is shown here to have utility in identifying resistivity distributions that give rise to frequency dispersion. The analysis was validated by application to synthetic data derived from a constant–phase–element model, a power–law distribution of resistivity, and an exponential distribution corresponding to a Young impedance. The application to experimental data obtained from coated aluminum demonstrates its utility for interpretation of impedance measurements.

  • A systematic approach toward Error Structure identification for impedance spectroscopy
    Journal of Electroanalytical Chemistry, 2004
    Co-Authors: Mark E. Orazem
    Abstract:

    The state-of-the-art is reviewed for the use of measurement models for assessing the stochastic and bias Error Structure of impedance measurements. The methods are illustrated for published impedance data that contain both capacitive and inductive components. This systematic Error analysis demonstrates that, in spite of differences between sequential impedance scans and the appearance of inductive and incomplete capacitive loops, the individual data sets represented a pseudo-stationary system and could be interpreted in terms of a stationary model.

  • Validation of the measurement model concept for Error Structure identification
    Electrochimica Acta, 2004
    Co-Authors: Pavan K. Shukla, Mark E. Orazem, Oscar D. Crisalle
    Abstract:

    2016-12-26T15:05:21

  • On the Error Structure of Impedance Measurements Simulation of FRA Instrumentation
    Journal of The Electrochemical Society, 2003
    Co-Authors: Steven L. Carson, Mark E. Orazem, Oscar D. Crisalle, Luis H. Garcia-rubio
    Abstract:

    A new paradigm is introduced for the investigation of Errors in frequency-domain measurements. The propagation of Errors from time-domain measurements to the desired complex variables in the frequency domain was analyzed for the frequency response analysis (FRA) algorithm, one of two techniques commonly used for spectroscopy measurements. Errors in the frequency domain were found to be normally distributed, even when the Errors in the time-domain were not normally distributed and when the measurement technique introduced bias Errors. For additive Errors in time-domain signals, the Errors in the real and imaginary impedance were found to be uncorrelated, and the variances of the real and imaginary parts of the complex impedance were equal. The equality of variances was realized except for cases where the time-domain signals contained proportional Errors. The statistical characteristics of the results were in good agreement with experimental observations.

  • The Error Structure of Impedance Spectra for Systems with a Large Ohmic Resistance with Respect to the Polarization Impedance
    Journal of The Electrochemical Society, 1996
    Co-Authors: Mark E. Orazem, Touriya El Moustafid, Claude Deslouis, Bernard Tribollet
    Abstract:

    Electrochemical impedance spectra were obtained for polyaniline membranes in weak and strong acid electrolytes. The measurement model approach for Error analysis was used to show that the standard deviation of the real and imaginary parts of the impedance were equal, even for systems containing a large solution resistance with respect to the polarization impedance. These results were confirmed using analogue RC circuits that did not require use of the measurement model approach. A model for the Error Structure is proposed that is in agreement with data obtained for large and small values of the solution resistance.

J. S. Wroblewski - One of the best experts on this subject based on the ideXlab platform.

  • An analysis of Error Structure in modeling the stock-recruitment data of gadoid stocks using generalized linear models
    Canadian Journal of Fisheries and Aquatic Sciences, 2004
    Co-Authors: Yan Jiao, David C. Schneider, Yong Chen, J. S. Wroblewski
    Abstract:

    When modeling the stock-recruitment (S-R) relationship, the Cushing, Ricker, and other S-R models are fit- ted to the observed S-R data by estimating parameters with assumptions made concerning the model Error Structure. Using a generalized linear model approach, we explored and identified the appropriate model Error Structure in model- ing S-R data for gadoid stocks. The S-R parameter estimation was found to be influenced by the choice of Error distri- butions assumed in the analysis. In modeling S-R data for gadoid stocks, the Beverton-Holt model was found to be more sensitive to the assumption of model Error distribution than the Cushing and Ricker models. The lognormal and gamma distributions had higher probability of being acceptable model Error distributions. Cluster analyses and sum- mary statistics of Error distributions in S-R modeling did not show consistent patterns in the identification of an ac- ceptable model Error Structure among species, geographic distributions, and sample sizes. A better understanding of the factors and mechanisms resulting in differences in the choice of appropriate model Error distributions for different pop- ulations is needed in future research. We recommend that the generalized linear model be used to identify acceptable model Error Structures in quantifying S-R relationships.

  • A simulation study of impacts of Error Structure on modeling stock-recruitment data using generalized linear models
    Canadian Journal of Fisheries and Aquatic Sciences, 2004
    Co-Authors: Yan Jiao, David C. Schneider, Yong Chen, J. S. Wroblewski
    Abstract:

    Stock-recruitment (S-R) models are commonly fitted to S-R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of Error Structure on S-R model- ing. The generalized linear model, which can readily deal with different Error Structures, was used in estimating param- eters. This study suggests that the quality of S-R parameter estimation, measured by estimation Errors, can be influenced by the realism of Error Structure assumed in an estimation, the number of S-R data points, and the number of outliers in modeling. A small number of S-R data points and the presence of outliers in S-R data could increase the difficulty in identifying an appropriate Error Structure in modeling, which might lead to large biases in the S-R parameter estimation. This study shows that generalized linear model methods can help identify an appropriate Error distribution in S-R modeling, leading to an improved estimation of parameters even when there are outliers and the number of S-R data points is small. We recommend the generalized linear model be used for quantifying stock-recruitment relationships. Resume : On a l'habitude d'ajuster les modeles stock-recrutement (S-R) aux donnees S-R a l'aide d'une methode des moindres carres. On presuppose que les erreurs de modelisation suivent une distribution normale ou lognormale, sans tenir compte si cette presupposition est realiste ou non. Une simulation de type Monte Carlo nous a permis d'evaluer l'impact de la presupposition d'une Structure d'erreur sur la modelisation S-R. Le modele lineaire generalise, qui peut s'accommoder facilement de diverses Structures d'erreur, a servi a estimer les parametres. Notre etude laisse croire que la qualite de l'estimation des parametres S-R, refletee dans les erreurs d'estimation, peut etre influencee par le realisme de la Structure d'erreur choisie pour l'estimation, par le nombre de donnees de S-R et le nombre de donnees aberrantes dans la modelisation. Un petit nombre de donnees S-R et la presence de donnees aberrantes dans les donnees S-R peu- vent rendre plus difficile l'identification d'une Structure d'erreur appropriee dans la modelisation, ce qui peut mener a de fortes distorsions dans l'estimation des parametres S-R. Notre etude demontre que les methodes reliees au modele lineaire generalise permettent de definir une distribution d'erreur appropriee dans la modelisation S-R, ce qui mene a une meilleure estimation des parametres, meme lorsqu'il y a des donnees aberrantes et qu'il y a peu de donnees S-R. Nous recommandons l'utilisation du modele lineaire generalise pour quantifier les relations stock-recrutement.

Michael West - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian statistical modeling of spatially correlated Error Structure in atmospheric tracer inverse analysis
    Atmospheric Chemistry and Physics, 2011
    Co-Authors: C. Mukherjee, Prasad S. Kasibhatla, Michael West
    Abstract:

    Abstract. We present and discuss the use of Bayesian modeling and computational methods for atmospheric chemistry inverse analyses that incorporate evaluation of spatial Structure in model-data residuals. Motivated by problems of refining bottom-up estimates of source/sink fluxes of trace gas and aerosols based on satellite retrievals of atmospheric chemical concentrations, we address the need for formal modeling of spatial residual Error Structure in global scale inversion models. We do this using analytically and computationally tractable conditional autoregressive (CAR) spatial models as components of a global inversion framework. We develop Markov chain Monte Carlo methods to explore and fit these spatial Structures in an overall statistical framework that simultaneously estimates source fluxes. Additional aspects of the study extend the statistical framework to utilize priors on source fluxes in a physically realistic manner, and to formally address and deal with missing data in satellite retrievals. We demonstrate the analysis in the context of inferring carbon monoxide (CO) sources constrained by satellite retrievals of column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument on the TERRA satellite, paying special attention to evaluating performance of the inverse approach using various statistical diagnostic metrics. This is developed using synthetic data generated to resemble MOPITT data to define a proof-of-concept and model assessment, and then in analysis of real MOPITT data. These studies demonstrate the ability of these simple spatial models to substantially improve over standard non-spatial models in terms of statistical fit, ability to recover sources in synthetic examples, and predictive match with real data.

  • Bayesian statistical modeling of spatially correlated Error Structure in atmospheric tracer inverse analysis
    2011
    Co-Authors: C. Mukherjee, Prasad S. Kasibhatla, Michael West
    Abstract:

    Abstract. Inverse modeling applications in atmospheric chemistry are increasingly addressing the challenging statistical issues of data synthesis by adopting refined statistical analysis methods. This paper advances this line of research by addressing several central questions in inverse modeling, focusing specifically on Bayesian statistical computation. Motivated by problems of refining bottom-up estimates of source/sink fluxes of trace gas and aerosols based on increasingly high-resolution satellite retrievals of atmospheric chemical concentrations, we address head-on the need for integrating formal spatial statistical methods of residual Error Structure in global scale inversion models. We do this using analytically and computationally tractable spatial statistical models, know as conditional autoregressive spatial models, as components of a global inversion framework. We develop Markov chain Monte Carlo methods to explore and fit these spatial Structures in an overall statistical framework that simultaneously estimates source fluxes. Additional aspects of the study extend the statistical framework to utilize priors in a more physically realistic manner, and to formally address and deal with missing data in satellite retrievals. We demonstrate the analysis in the context of inferring carbon monoxide (CO) sources constrained by satellite retrievals of column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument on the TERRA satellite, paying special attention to evaluating performance of the inverse approach using various statistical diagnostic metrics. This is developed using synthetic data generated to resemble MOPITT data to define a~proof-of-concept and model assessment, and then in analysis of real MOPITT data.

Yong Chen - One of the best experts on this subject based on the ideXlab platform.

  • An analysis of Error Structure in modeling the stock-recruitment data of gadoid stocks using generalized linear models
    Canadian Journal of Fisheries and Aquatic Sciences, 2004
    Co-Authors: Yan Jiao, David C. Schneider, Yong Chen, J. S. Wroblewski
    Abstract:

    When modeling the stock-recruitment (S-R) relationship, the Cushing, Ricker, and other S-R models are fit- ted to the observed S-R data by estimating parameters with assumptions made concerning the model Error Structure. Using a generalized linear model approach, we explored and identified the appropriate model Error Structure in model- ing S-R data for gadoid stocks. The S-R parameter estimation was found to be influenced by the choice of Error distri- butions assumed in the analysis. In modeling S-R data for gadoid stocks, the Beverton-Holt model was found to be more sensitive to the assumption of model Error distribution than the Cushing and Ricker models. The lognormal and gamma distributions had higher probability of being acceptable model Error distributions. Cluster analyses and sum- mary statistics of Error distributions in S-R modeling did not show consistent patterns in the identification of an ac- ceptable model Error Structure among species, geographic distributions, and sample sizes. A better understanding of the factors and mechanisms resulting in differences in the choice of appropriate model Error distributions for different pop- ulations is needed in future research. We recommend that the generalized linear model be used to identify acceptable model Error Structures in quantifying S-R relationships.

  • A simulation study of impacts of Error Structure on modeling stock-recruitment data using generalized linear models
    Canadian Journal of Fisheries and Aquatic Sciences, 2004
    Co-Authors: Yan Jiao, David C. Schneider, Yong Chen, J. S. Wroblewski
    Abstract:

    Stock-recruitment (S-R) models are commonly fitted to S-R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of Error Structure on S-R model- ing. The generalized linear model, which can readily deal with different Error Structures, was used in estimating param- eters. This study suggests that the quality of S-R parameter estimation, measured by estimation Errors, can be influenced by the realism of Error Structure assumed in an estimation, the number of S-R data points, and the number of outliers in modeling. A small number of S-R data points and the presence of outliers in S-R data could increase the difficulty in identifying an appropriate Error Structure in modeling, which might lead to large biases in the S-R parameter estimation. This study shows that generalized linear model methods can help identify an appropriate Error distribution in S-R modeling, leading to an improved estimation of parameters even when there are outliers and the number of S-R data points is small. We recommend the generalized linear model be used for quantifying stock-recruitment relationships. Resume : On a l'habitude d'ajuster les modeles stock-recrutement (S-R) aux donnees S-R a l'aide d'une methode des moindres carres. On presuppose que les erreurs de modelisation suivent une distribution normale ou lognormale, sans tenir compte si cette presupposition est realiste ou non. Une simulation de type Monte Carlo nous a permis d'evaluer l'impact de la presupposition d'une Structure d'erreur sur la modelisation S-R. Le modele lineaire generalise, qui peut s'accommoder facilement de diverses Structures d'erreur, a servi a estimer les parametres. Notre etude laisse croire que la qualite de l'estimation des parametres S-R, refletee dans les erreurs d'estimation, peut etre influencee par le realisme de la Structure d'erreur choisie pour l'estimation, par le nombre de donnees de S-R et le nombre de donnees aberrantes dans la modelisation. Un petit nombre de donnees S-R et la presence de donnees aberrantes dans les donnees S-R peu- vent rendre plus difficile l'identification d'une Structure d'erreur appropriee dans la modelisation, ce qui peut mener a de fortes distorsions dans l'estimation des parametres S-R. Notre etude demontre que les methodes reliees au modele lineaire generalise permettent de definir une distribution d'erreur appropriee dans la modelisation S-R, ce qui mene a une meilleure estimation des parametres, meme lorsqu'il y a des donnees aberrantes et qu'il y a peu de donnees S-R. Nous recommandons l'utilisation du modele lineaire generalise pour quantifier les relations stock-recrutement.

  • An empirical study on estimators for linear regression analyses in fisheries and ecology
    Fisheries Research, 2000
    Co-Authors: Yong Chen, Donald A. Jackson
    Abstract:

    Abstract Linear regression analysis is often used in fisheries and ecological studies. Parameters in a linear model are estimated by fitting the model to observed fisheries data with assumptions made concerning model Error Structure. The commonly used estimation method in fisheries and ecology is ordinary least squares (LS) which is based on the Gauss–Markov assumption on the model Error. Data observed in fisheries studies are often contaminated by various Errors. Outliers frequently arise when fitting models to the data. The model Error Structure is difficult to define with confidence in fisheries and ecological studies. It is thus necessary to evaluate the robustness of an estimator to assumptions on the model Error Structure. In this study, we evaluate five estimators, least squares (LS), geometric means (GM), least median of squares (LMS), LMS-based reweighted least squares (RLS), and LMS-based reweighted geometric means (RGM), in fitting linear models with assumptions of different model Error Structures. We show that the selection of a suitable estimator for a regression analysis depends upon the Error Structures of the dependent and independent variables. However, overall the LMS-based RGM method tends to be more robust than other estimators to the assumed Error Structures. We suggest a three-step procedure in analyzing fisheries and ecological data using linear regression analysis: identify outliers by a LMS analysis, evaluate the identified outliers based on background information about the study, and then apply the LMS-based GM where appropriate. The method used in step 3 can be changed if the Error Structures of observed data are known.

Shlomo Bekhor - One of the best experts on this subject based on the ideXlab platform.

  • Development and estimation of a semi-compensatory model with a flexible Error Structure
    Transportation Research Part B: Methodological, 2012
    Co-Authors: Sigal Kaplan, Yoram Shiftan, Shlomo Bekhor
    Abstract:

    In decisions involving many alternatives, such as residential choice, individuals conduct a two-stage decision process, consisting of eliminating non-viable alternatives and choice from the retained choice set. In light of the potential of semi-compensatory discrete choice models to mathematically represent such decisions, research is inching ahead with the aim of alleviating their high computational complexity and their severe restrictive assumptions. To date, still a major barrier for the implementation of semi-compensatory models is their underlying assumption of independently and identically distributed Error terms across alternatives at the choice stage. This study relaxes the assumption by introducing nested substitution patterns and alternatively random taste heterogeneity at the choice stage, thus equating the structural flexibility of semi-compensatory models to their compensatory counterparts. The proposed model is applied to off-campus rental apartment choice by students. Results show the feasibility and importance of introducing a flexible Error Structure into semi-compensatory models.

  • A Semi-Compensatory Residential Choice Model With Flexible Error Structure
    2011
    Co-Authors: Sigal Kaplan, Yoram Shiftan, Shlomo Bekhor
    Abstract:

    Spatial choices entailing many alternatives (e.g., residence, trip destination) are typically represented by compensatory models based on utility maximization with exogenous choice set generation, which might lead to incorrect choice sets and hence to biased demand elasticity estimates. Semi-compensatory models show promise in increasing the accuracy of choice set specification by integrating choice set formation within discrete choice models. These models represent a two-stage process consisting of an elimination-based choice set formation upon satisfying criteria thresholds followed by utility-based choice. However, they are subject to simplifying assumptions that impede their application in urban planning. This paper proposes a novel semi-compensatory model that alleviates the simplifying assumptions concerning (i) the number of alternatives, (ii) the representation of choice set formation, and (iii) the Error Structure. The proposed semi-compensatory model represents a sequence of choice set formation based on the conjunctive heuristic with correlated thresholds, and utility-based choice accommodating alternatively nested substitution patterns across the alternatives and random taste variation across the population. The proposed model is applied to off-campus rental apartment choice of students. The population sample for model estimation consists of 1,893 residential choices from 631 students, who participated in a stated-preference web-based survey of rental apartment choice. The survey comprised a two-stage choice experiment supplemented by a questionnaire, which elicited socio-economic characteristics, attitudes and preferences. During the experiment, respondents searched an apartment dataset by a list of thresholds for pre-defined criteria and then ranked their three most preferred apartments from the resulting choice set. The survey website seamlessly recorded the chosen apartments and their respective thresholds. Results show (i) the estimated model for a realistic universal realm of 200 alternatives, (ii) the representation of correlated threshold as a function of individual characteristics, and (iii) the feasibility and importance of introducing a flexible Error Structure into semi-compensatory models.

  • A semi-compensatory residential choice model with flexible Error Structure
    2010
    Co-Authors: Sigal Kaplan, Yoram Shiftan, Shlomo Bekhor
    Abstract:

    This paper presents the development and estimation of a semi-compensatory residential choice model with a flexible Error Structure. The model assumes that apartment seekers engage in a two-stage process, consisting of a non-compensatory strategy to retain only alternatives that meet search-criteria thresholds, followed by a compensatory strategy to finalize the choice. The model can accommodate nested substitution patterns across the alternatives as well as random taste variation across the population. The proposed model is applied to off-campus rental apartment choices by university students. The model estimation is based on database search and choice outcomes retrieved from a synthetic real-estate website inspired by actual on-line real-estate portals. Results show the potential of the proposed semi-compensatory model to realistically represent residential choice and other spatial choices related to regional, urban and transport planning.