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Candidate Models (candidate + models)
Selected AbstractsPacific herring, Clupea pallasi, recruitment in the Bering Sea and north-east Pacific Ocean, II: relationships to environmental variables and implications for forecastingFISHERIES OCEANOGRAPHY, Issue 4 2000Erik H. Williams Previous studies have shown that Pacific herring populations in the Bering Sea and north-east Pacific Ocean can be grouped based on similar recruitment time series. The scale of these groups suggests large-scale influence on recruitment fluctuations from the environment. Recruitment time series from 14 populations were analysed to determine links to various environmental variables and to develop recruitment forecasting models using a Ricker-type environmentally dependent spawner,recruit model. The environmental variables used for this investigation included monthly time series of the following: southern oscillation index, North Pacific pressure index, sea surface temperatures, air temperatures, coastal upwelling indices, Bering Sea wind, Bering Sea ice cover, and Bering Sea bottom temperatures. Exploratory correlation analysis was used for focusing the time period examined for each environmental variable. Candidate models for forecasting herring recruitment were selected by the ordinary and recent cross-validation prediction errors. Results indicated that forecasting models using air and sea surface temperature data lagged to the year of spawning generally produced the best forecasting models. Multiple environmental variables showed marked improvements in prediction over single-environmental-variable models. [source] Forecasting realized volatility: a Bayesian model-averaging approachJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2009Chun Liu How to measure and model volatility is an important issue in finance. Recent research uses high-frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model-averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility. Copyright © 2009 John Wiley & Sons, Ltd. [source] The transferability of distribution models across regions: an amphibian case studyDIVERSITY AND DISTRIBUTIONS, Issue 3 2009Flavio Zanini ABSTRACT Aim, Predicting species distribution is of fundamental importance for ecology and conservation. However, distribution models are usually established for only one region and it is unknown whether they can be transferred to other geographical regions. We studied the distribution of six amphibian species in five regions to address the question of whether the effect of landscape variables varied among regions. We analysed the effect of 10 variables extracted in six concentric buffers (from 100 m to 3 km) describing landscape composition around breeding ponds at different spatial scales. We used data on the occurrence of amphibian species in a total of 655 breeding ponds. We accounted for proximity to neighbouring populations by including a connectivity index to our models. We used logistic regression and information-theoretic model selection to evaluate candidate models for each species. Location, Switzerland. Results, The explained deviance of each species' best models varied between 5% and 32%. Models that included interactions between a region and a landscape variable were always included in the most parsimonious models. For all species, models including region-by-landscape interactions had similar support (Akaike weights) as models that did not include interaction terms. The spatial scale at which landscape variables affected species distribution varied from 100 m to 1000 m, which was in agreement with several recent studies suggesting that land use far away from the ponds can affect pond occupancy. Main conclusions, Different species are affected by different landscape variables at different spatial scales and these effects may vary geographically, resulting in a generally low transferability of distribution models across regions. We also found that connectivity seems generally more important than landscape variables. This suggests that metapopulation processes may play a more important role in species distribution than habitat characteristics. [source] Bilby distribution and fire: a test of alternative models of habitat suitability in the Tanami Desert, AustraliaECOGRAPHY, Issue 6 2007Richard Southgate The distribution of the bilby Macrotis lagotis was assessed in the Tanami Desert using stratified random plots, repetitively sampled transects, aerial survey transects, and ground truth plots. Compared to a previous assessment of distribution, the extent of occurrence has changed little in the last 20 yr. However, the area of occupancy is small relative to the extent of occurrence and <25% of the current geographic range has bilby sign <20 km apart. Generalised linear modelling was used to determine the strength of association between bilby occurrence and habitat variables and identify refugia characteristics. Four competing candidate models were examined to determine whether bilby occurrence associated significantly with productive substrates and introduced herbivores, the distribution of key predator species, the pattern of fire, and climatic gradients including rainfall and temperature. For the entire study area, bilby presence associated most strongly with variables of mean annual rainfall, substrate type and the probability of dingo occurrence. Proximity to recently burnt habitat formed a significant predictor of bilby occurrence in a model derived for a reduced part of the study area where most sign was found. The work suggested that the current frameworks underpinning understanding of biotic distributions in arid Australia are deficient, and that climatic gradients, lateritic and rocky systems, and predators need to be incorporated into our thinking in the future. The extent of occurrence based on outlier records from opportunistic reports provided a misleading indication of the true status of the bilby. [source] Sublethal effects of methylmercury on fecal metabolites of testosterone, estradiol, and corticosterone in captive juvenile white ibises (Eudocimus albus),ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 5 2009Evan M. Adams Abstract Methylmercury (MeHg) is a globally distributed neurotoxin, endocrine disruptor, and teratogen, and its effects on birds are poorly understood, especially within an environmentally relevant exposure range. In an effort to understand the potential causal relationship between MeHg exposure and endocrine development, we established four dietary exposure groups (0 [control], 0.05, 0.1, and 0.3 mg/kg wet wt/d of MeHg) of postfledging white ibises (Eudocimus albus) in a divided, free-flight aviary that spanned the estimated range of environmental exposure for this species. Fecal samples were collected from individually identified ibises over six months in 2005 and processed for hormone evaluation. Significant sex-related differences in fecal estradiol concentrations, though unpredicted in direction, suggest that this steroid could be related to juvenile development in this species. Using repeated-measures general linear models, we tested a set of candidate models to explain variation in endocrine expression. We found that MeHg exposure led to significant differences in fecal estradiol concentrations between the control and medium-dose groups, whereas differences in fecal corticosterone concentrations were observed between the control and both the low- and high-dose groups. These results suggest highly nonlinear dose-response patterns for MeHg. Many endocrine-disrupting contaminants are theorized to affect multiple endpoints in a nonlinear manner, making results difficult to interpret using a traditional toxicological approach. The evidence presented here suggests that endocrine effects of MeHg exposure could behave similarly. [source] A steady-state modeling approach to validate an in vivo mechanism of the GAL regulatory network in Saccharomyces cerevisiaeFEBS JOURNAL, Issue 20 2004Malkhey Verma Cellular regulation is a result of complex interactions arising from DNA,protein and protein,protein binding, autoregulation, and compartmentalization and shuttling of regulatory proteins. Experiments in molecular biology have identified these mechanisms recruited by a regulatory network. Mathematical models may be used to complement the knowledge-base provided by in vitro experimental methods. Interactions identified by in vitro experiments can lead to the hypothesis of multiple candidate models explaining the in vivo mechanism. The equilibrium dissociation constants for the various interactions and the total component concentration constitute constraints on the candidate models. In this work, we identify the most plausible in vivo network by comparing the output response to the experimental data. We demonstrate the methodology using the GAL system of Saccharomyces cerevisiae for which the steady-state analysis reveals that Gal3p neither dimerizes nor shuttles between the cytoplasm and the nucleus. [source] Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computationFRESHWATER BIOLOGY, Issue 10 2007DONG-KYUN KIM Summary 1. An ecological model was developed using genetic programming (GP) to predict the time-series dynamics of the diatom, Stephanodiscus hantzschii for the lower Nakdong River, South Korea. Eight years of weekly data showed the river to be hypertrophic (chl. a, 45.1 ± 4.19 ,g L,1, mean ± SE, n = 427), and S. hantzschii annually formed blooms during the winter to spring flow period (late November to March). 2. A simple non-linear equation was created to produce a 3-day sequential forecast of the species biovolume, by means of time series optimization genetic programming (TSOGP). Training data were used in conjunction with a GP algorithm utilizing 7 years of limnological variables (1995,2001). The model was validated by comparing its output with measurements for a specific year with severe blooms (1994). The model accurately predicted timing of the blooms although it slightly underestimated biovolume (training r2 = 0.70, test r2 = 0.78). The model consisted of the following variables: dam discharge and storage, water temperature, Secchi transparency, dissolved oxygen (DO), pH, evaporation and silica concentration. 3. The application of a five-way cross-validation test suggested that GP was capable of developing models whose input variables were similar, although the data are randomly used for training. The similarity of input variable selection was approximately 51% between the best model and the top 20 candidate models out of 150 in total (based on both Root Mean Squared Error and the determination coefficients for the test data). 4. Genetic programming was able to determine the ecological importance of different environmental variables affecting the diatoms. A series of sensitivity analyses showed that water temperature was the most sensitive parameter. In addition, the optimal equation was sensitive to DO, Secchi transparency, dam discharge and silica concentration. The analyses thus identified likely causes of the proliferation of diatoms in ,river-reservoir hybrids' (i.e. rivers which have the characteristics of a reservoir during the dry season). This result provides specific information about the bloom of S. hantzschii in river systems, as well as the applicability of inductive methods, such as evolutionary computation to river-reservoir hybrid systems. [source] Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebratesFRESHWATER BIOLOGY, Issue 2 2006JOHN VAN SICKLE Summary 1. The predictive modelling approach to bioassessment estimates the macroinvertebrate assemblage expected at a stream site if it were in a minimally disturbed reference condition. The difference between expected and observed assemblages then measures the departure of the site from reference condition. 2. Most predictive models employ site classification, followed by discriminant function (DF) modelling, to predict the expected assemblage from a suite of environmental variables. Stepwise DF analysis is normally used to choose a single subset of DF predictor variables with a high accuracy for classifying sites. An alternative is to screen all possible combinations of predictor variables, in order to identify several ,best' subsets that yield good overall performance of the predictive model. 3. We applied best-subsets DF analysis to assemblage and environmental data from 199 reference sites in Oregon, U.S.A. Two sets of 66 best DF models containing between one and 14 predictor variables (that is, having model orders from one to 14) were developed, for five-group and 11-group site classifications. 4. Resubstitution classification accuracy of the DF models increased consistently with model order, but cross-validated classification accuracy did not improve beyond seventh or eighth-order models, suggesting that the larger models were overfitted. 5. Overall predictive model performance at model training sites, measured by the root-mean-squared error of the observed/expected species richness ratio, also improved steadily with DF model order. But high-order DF models usually performed poorly at an independent set of validation sites, another sign of model overfitting. 6. Models selected by stepwise DF analysis showed evidence of overfitting and were outperformed by several of the best-subsets models. 7. The group separation strength of a DF model, as measured by Wilks',, was more strongly correlated with overall predictive model performance at training sites than was DF classification accuracy. 8. Our results suggest improved strategies for developing reliable, parsimonious predictive models. We emphasise the value of independent validation data for obtaining a realistic picture of model performance. We also recommend assessing not just one or two, but several, candidate models based on their overall performance as well as the performance of their DF component. 9. We provide links to our free software for stepwise and best-subsets DF analysis. [source] Designing predictors for MIMO switching supervisory controlINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2001Edoardo Mosca Abstract The paper studies the problem of inferring the behaviour of a linear feedback loop made up by an uncertain MIMO plant and a given candidate controller from data taken from the plant possibly driven by a different controller. In such a context, it is shown here that a convenient tool to work with is a quantity called normalized discrepancy. This is a measure of mismatch between the loop made up by the unknown plant in feedback with the candidate controller and the nominal ,tuned-loop' related to the same candidate controller. It is shown that discrepancy can in principle be obtained by resorting to the concept of a virtual reference, and conveniently computed in real time by suitably filtering an output prediction error. The latter result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse distribution of candidate models. Copyright © 2001 John Wiley & Sons, Ltd. [source] Assessing species density and abundance of tropical trees from remotely sensed data and geostatisticsAPPLIED VEGETATION SCIENCE, Issue 4 2009J. Luis Hernández-Stefanoni Abstract Question: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species? Location: Quintana Roo, Yucatan peninsula, Mexico. Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model-averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation. [source] Interpretation of very low resolution X-ray electron-density maps using core objectsACTA CRYSTALLOGRAPHICA SECTION D, Issue 7 2009Philipp Heuser A novel approach to obtaining structural information from macromolecular X-ray data extending to resolutions as low as 20,Ĺ is presented. Following a simple map-segmentation procedure, the approximate shapes of the domains forming the structure are identified. A pattern-recognition comparative analysis of these shapes and those derived from the structures of domains from the PDB results in candidate structural models that can be used for a fit into the density map. It is shown that the placed candidate models can be employed for subsequent phase extension to higher resolution. [source] Pharmacokinetic interaction between efavirenz and dual protease inhibitors in healthy volunteersBIOPHARMACEUTICS AND DRUG DISPOSITION, Issue 2 2008Qing Ma Abstract The combination of efavirenz with HIV-1 protease inhibitors (PI) results in complex interactions secondary to mixed induction and inhibition of oxidative metabolism. ACTG A5043 was a prospective, open-label, controlled, two-period, multiple-dose study with 55 healthy volunteers. The objective of the present study was to evaluate the potential pharmacokinetic interaction between efavirenz and dual PIs. The subjects received a daily dose of 600,mg efavirenz for 10 days with amprenavir 600,mg twice daily added at day 11 and were randomized to receive nelfinavir, indinavir, ritonavir, saquinavir, or no second PI on days 15,21. Intensive pharmacokinetic studies were conducted on day 14 and 21. Efavirenz plasma concentrations were fit to candidate models using weighted non-linear regression. The disposition of efavirenz was described by a linear two-compartment model with first order absorption following a fitted lag time. Apparent clearance (CLt/F), volume of distribution at steady state (Vss/F), inter-compartmental clearance, and the central and peripheral volume of distribution were estimated. The mean CLt/F and Vss/F of efavirenz were 0.126,l/h/kg and 4.412,l/kg, respectively. Both AUC and CLt/F of efavirenz remained unchanged after 7 days of dual PI dosing. The mean Vss/F of efavirenz increased an average of 89% across arms, ranging from 52% (nelfinavir) to 115% (indinavir) relative to efavirenz with amprenavir alone. Increases were also observed in Vp/F after the addition of nelfinavir, indinavir, ritonavir and saquinavir by 85%, 170%, 162% and 111%, respectively. In conclusion, concomitant administration of dual PIs is unlikely to have any clinically significant effect on the pharmacokinetics of CYP2B6 substrates in general or oral efavirenz specifically. Copyright © 2007 John Wiley & Sons, Ltd. [source] | ||||||||||||||||