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Model Coefficients (model + coefficient)
Selected AbstractsImproved calculation of the net analyte signal in inverse multivariate calibrationJOURNAL OF CHEMOMETRICS, Issue 6 2001Joan Ferré Abstract The net analyte signal (NAS) is the part of the measured signal that a calibration model relates to the property of interest (e.g. analyte concentration). Accurate values of the NAS are required in multivariate calibration to calculate analytical figures of merit such as sensitivity, selectivity, signal-to-noise ratio and limit of detection. This paper presents an improved version of the calculation method for the NAS in inverse models proposed by Lorber et al. (Anal. Chem. 1997; 69: 1620). Model coefficients and predictions calculated with the improved NAS are the same as those from the common equations of principal component regression (PCR) and partial least squares (PLS) regression. The necessary alterations to the calculations of sensitivity, selectivity and the pseudounivariate presentation of the model are also provided. Copyright © 2001 John Wiley & Sons, Ltd. [source] Large eddy simulation of flow and scalar transport in a round jetHEAT TRANSFER - ASIAN RESEARCH (FORMERLY HEAT TRANSFER-JAPANESE RESEARCH), Issue 3 2004Hitoshi Suto Abstract Large eddy simulation (LES) was performed for a spatially developing round jet and its scalar transport at four steps of Reynolds number set between 1200 and 1,000,000. A simulated domain, which extends 30 times the nozzle diameter, includes initial, transitional, and established stage of jet. A modified version of convection outflow condition was proposed in order to diminish the effect of a downstream boundary. Tested were two kinds of subgrid scale (SOS) models: a Smagorinsky model (SM) and a dynamic Smagorinsky model (DSM). In the former model, parameters are kept at empirically deduced constants, while in the latter, they are calculated using different levels of space filtering. Data analysis based on the decay law of jet clearly presented the performance of SGS models. Simulated results by SM and DSM compared favorably with existing measurements of jet and its scalar transport. However, the quantitative accuracy of DSM was better than that of SM at a transitional stage of flow field. Computed parameters by DSM, coefficient for SGS stresses, CR and SGS eddy diffusivity ratio, ,SGS, were not far from empirical constants of SM. Optimization of the model coefficient was suggested in DSM so that coefficient CR was nearly equal in the established stage of jet but it was reduced in low turbulence close to the jet nozzle. © 2004 Wiley Periodicals, Inc. Heat Trans Asian Res, 33(3): 175,188, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/htj.20001 [source] Identification of Time-Variant Modal Parameters Using Time-Varying Autoregressive with Exogenous Input and Low-Order Polynomial FunctionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 7 2009C. S. Huang By developing the equivalent relations between the equation of motion of a time-varying structural system and the TVARX model, this work proves that instantaneous modal parameters of a time-varying system can be directly estimated from the TVARX model coefficients established from displacement responses. A moving least-squares technique incorporating polynomial basis functions is adopted to approximate the coefficient functions of the TVARX model. The coefficient functions of the TVARX model are represented by polynomials having time-dependent coefficients, instead of constant coefficients as in traditional basis function expansion approaches, so that only low orders of polynomial basis functions are needed. Numerical studies are carried out to investigate the effects of parameters in the proposed approach on accurately determining instantaneous modal parameters. Numerical analyses also demonstrate that the proposed approach is superior to some published techniques (i.e., recursive technique with a forgetting factor, traditional basis function expansion approach, and weighted basis function expansion approach) in accurately estimating instantaneous modal parameters of a structure. Finally, the proposed approach is applied to process measured data for a frame specimen subjected to a series of base excitations in shaking table tests. The specimen was damaged during testing. The identified instantaneous modal parameters are consistent with observed physical phenomena. [source] Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks,HYDROLOGICAL PROCESSES, Issue 16 2009Anne B. Hoos Abstract Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States,higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow. Published in 2009 by John Wiley & Sons, Ltd. [source] An efficient approach for computing non-Gaussian ARMA model coefficients using Pisarenko's methodINTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 3 2005Adnan Al-Smadi Abstract This paper addresses the problem of estimating the coefficients of a general autoregressive moving average (ARMA) model from only third order cumulants (TOCs) of the noisy observations of the system output. The observed signal may be corrupted by additive coloured Gaussian noise. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian sequence. The input is not observed. The unknown model coefficients are obtained using eigenvalue,eigenvector decomposition. The derivation of this procedure is an extension of Pisarenko harmonic autocorrelation-based (PHA) method to third order statistics. It will be shown that the desired ARMA coefficients vector corresponds to the eigenvector associated with the minimum eigenvalue of a data covariance matrix of TOCs. The proposed method is also compared with well-known algorithms as well as with the PHA method. Copyright © 2005 John Wiley & Sons, Ltd. [source] Modeling tropical cyclone intensity with quantile regressionINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 10 2009Thomas H. Jagger Abstract Wind speeds from tropical cyclones (TCs) occurring near the USA are modeled with climate variables (covariates) using quantile regression. The influences of Atlantic sea-surface temperature (SST), the Pacific El Niño, and the North Atlantic oscillation (NAO) on near-coastal TC intensity are in the direction anticipated from previous studies using Poisson regression on cyclone counts and are, in general, strongest for higher intensity quantiles. The influence of solar activity, a new covariate, peaks near the median intensity level, but the relationship switches sign for the highest quantiles. An advantage of the quantile regression approach over a traditional parametric extreme value model is that it allows easier interpretation of model coefficients (parameters) with respect to changes to the covariates since coefficients vary as a function of quantile. It is proven mathematically that parameters of the Generalized Pareto Distribution (GPD) for extreme events can be used to estimate regression coefficients for the extreme quantiles. The mathematical relationship is demonstrated empirically using the subset of TC intensities exceeding 96 kt (49 m/s). Copyright © 2008 Royal Meteorological Society [source] Interaction strengths in food webs: issues and opportunitiesJOURNAL OF ANIMAL ECOLOGY, Issue 3 2004Eric L. Berlow Summary 1Recent efforts to understand how the patterning of interaction strength affects both structure and dynamics in food webs have highlighted several obstacles to productive synthesis. Issues arise with respect to goals and driving questions, methods and approaches, and placing results in the context of broader ecological theory. 2Much confusion stems from lack of clarity about whether the questions posed relate to community-level patterns or to species dynamics, and to what authors actually mean by the term ,interaction strength'. Here, we describe the various ways in which this term has been applied and discuss the implications of loose terminology and definition for the development of this field. 3Of particular concern is the clear gap between theoretical and empirical investigations of interaction strengths and food web dynamics. The ecological community urgently needs to explore new ways to estimate biologically reasonable model coefficients from empirical data, such as foraging rates, body size, metabolic rate, biomass distribution and other species traits. 4Combining numerical and analytical modelling approaches should allow exploration of the conditions under which different interaction strengths metrics are interchangeable with regard to relative magnitude, system responses, and species identity. 5Finally, the prime focus on predator,prey links in much of the research to date on interaction strengths in food webs has meant that the potential significance of non-trophic interactions, such as competition, facilitation and biotic disturbance, has been largely ignored by the food web community. Such interactions may be important dynamically and should be routinely included in future food web research programmes. [source] The PLS model space revisitedJOURNAL OF CHEMOMETRICS, Issue 2 2009Svante Wold Abstract Pell, Ramos and Manne (PRM) in a recent article in this journal claim that the ,conventional' PLS algorithm with orthogonal scores has an inherent inconsistency in that it uses different model spaces for calculating the prediction model coefficients and for calculating the X -space model and it's residuals [1]. We disagree with PRM. All PLS model scores, residuals, coefficients, etc., obtained by the conventional PLS algorithm do come from the same underlying latent variable (LV) model, and not from different models or model spaces as PRM suggest. PRM have simply posed a different model with different assumptions and obtained slightly different results, as should have been expected. Copyright © 2008 John Wiley & Sons, Ltd. [source] Dual- and triple-mode matrix approximation and regression modellingAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2003Stan Lipovetsky Abstract We propose a dual- and triple-mode least squares for matrix approximation. This technique applied to the singular value decomposition produces the classical solution with a new interpretation. Applied to regression modelling, this approach corresponds to a regularized objective and yields a new solution with properties of a ridge regression. The results for regression are robust and suggest a convenient tool for the analysis and interpretation of the model coefficients. Numerical results are given for a marketing research data set. Copyright © 2003 John Wiley & Sons, Ltd. [source] | ||||||||||