Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique
Glosup, J.G.; Axelrod M.C.
1994-11-15
The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of our interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method.
Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique
Glosup, J.G.; Axelrod, M.C.
1994-08-12
The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method. The problem involves a probability model for underwater noise due to distant shipping.
The T cell-selective IL-2 mutant AIC284 mediates protection in a rat model of Multiple Sclerosis.
Weishaupt, Andreas; Paulsen, Daniela; Werner, Sandra; Wolf, Nelli; Köllner, Gabriele; Rübsamen-Schaeff, Helga; Hünig, Thomas; Kerkau, Thomas; Beyersdorf, Niklas
2015-05-15
Targeting regulatory T cells (Treg cells) with interleukin-2 (IL-2) constitutes a novel therapeutic approach for autoimmunity. As anti-cancer therapy with IL-2 has revealed substantial toxicities a mutated human IL-2 molecule, termed AIC284 (formerly BAY 50-4798), has been developed to reduce these side effects. To assess whether AIC284 is efficacious in autoimmunity, we studied its therapeutic potential in an animal model for Multiple Sclerosis. Treatment of Lewis rats with AIC284 increased Treg cell numbers and protected the rats from Experimental Autoimmune Encephalomyelitis (EAE). AIC284 might, thus, also efficiently prevent progression of autoimmune diseases in humans. PMID:25903730
Vrieze, Scott I.
2012-01-01
This article reviews the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models given their growing use in theory testing and construction. We discuss theoretical statistical results in regression and illustrate more important issues with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is effcient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not effcient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use AIC or BIC depends on many factors, including: the loss function employed, the study's methodological design, the substantive research question, and the notion of a true model and its applicability to the study at hand. PMID:22309957
ERIC Educational Resources Information Center
Vrieze, Scott I.
2012-01-01
This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important…
AIC, BIC, Bayesian evidence against the interacting dark energy model
NASA Astrophysics Data System (ADS)
Szydłowski, Marek; Krawiec, Adam; Kurek, Aleksandra; Kamionka, Michał
2015-01-01
Recent astronomical observations have indicated that the Universe is in a phase of accelerated expansion. While there are many cosmological models which try to explain this phenomenon, we focus on the interacting CDM model where an interaction between the dark energy and dark matter sectors takes place. This model is compared to its simpler alternative—the CDM model. To choose between these models the likelihood ratio test was applied as well as the model comparison methods (employing Occam's principle): the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Bayesian evidence. Using the current astronomical data: type Ia supernova (Union2.1), , baryon acoustic oscillation, the Alcock-Paczynski test, and the cosmic microwave background data, we evaluated both models. The analyses based on the AIC indicated that there is less support for the interacting CDM model when compared to the CDM model, while those based on the BIC indicated that there is strong evidence against it in favor of the CDM model. Given the weak or almost non-existing support for the interacting CDM model and bearing in mind Occam's razor we are inclined to reject this model.
AIC649 Induces a Bi-Phasic Treatment Response in the Woodchuck Model of Chronic Hepatitis B
Paulsen, Daniela; Weber, Olaf; Ruebsamen-Schaeff, Helga; Tennant, Bud C.; Menne, Stephan
2015-01-01
AIC649 has been shown to directly address the antigen presenting cell arm of the host immune defense leading to a regulated cytokine release and activation of T cell responses. In the present study we analyzed the antiviral efficacy of AIC649 as well as its potential to induce functional cure in animal models for chronic hepatitis B. Hepatitis B virus transgenic mice and chronically woodchuck hepatitis virus (WHV) infected woodchucks were treated with AIC649, respectively. In the mouse system AIC649 decreased the hepatitis B virus titer as effective as the “gold standard”, Tenofovir. Interestingly, AIC649-treated chronically WHV infected woodchucks displayed a bi-phasic pattern of response: The marker for functional cure—hepatitis surface antigen—first increased but subsequently decreased even after cessation of treatment to significantly reduced levels. We hypothesize that the observed bi-phasic response pattern to AIC649 treatment reflects a physiologically “concerted”, reconstituted immune response against WHV and therefore may indicate a potential for inducing functional cure in HBV-infected patients. PMID:26656974
AIC649 Induces a Bi-Phasic Treatment Response in the Woodchuck Model of Chronic Hepatitis B.
Paulsen, Daniela; Weber, Olaf; Ruebsamen-Schaeff, Helga; Tennant, Bud C; Menne, Stephan
2015-01-01
AIC649 has been shown to directly address the antigen presenting cell arm of the host immune defense leading to a regulated cytokine release and activation of T cell responses. In the present study we analyzed the antiviral efficacy of AIC649 as well as its potential to induce functional cure in animal models for chronic hepatitis B. Hepatitis B virus transgenic mice and chronically woodchuck hepatitis virus (WHV) infected woodchucks were treated with AIC649, respectively. In the mouse system AIC649 decreased the hepatitis B virus titer as effective as the "gold standard", Tenofovir. Interestingly, AIC649-treated chronically WHV infected woodchucks displayed a bi-phasic pattern of response: The marker for functional cure--hepatitis surface antigen--first increased but subsequently decreased even after cessation of treatment to significantly reduced levels. We hypothesize that the observed bi-phasic response pattern to AIC649 treatment reflects a physiologically "concerted", reconstituted immune response against WHV and therefore may indicate a potential for inducing functional cure in HBV-infected patients. PMID:26656974
NASA Astrophysics Data System (ADS)
Cama, Mariaelena; Cristi Nicu, Ionut; Conoscenti, Christian; Quénéhervé, Geraldine; Maerker, Michael
2016-04-01
Landslide susceptibility can be defined as the likelihood of a landslide occurring in a given area on the basis of local terrain conditions. In the last decades many research focused on its evaluation by means of stochastic approaches under the assumption that 'the past is the key to the future' which means that if a model is able to reproduce a known landslide spatial distribution, it will be able to predict the future locations of new (i.e. unknown) slope failures. Among the various stochastic approaches, Binary Logistic Regression (BLR) is one of the most used because it calculates the susceptibility in probabilistic terms and its results are easily interpretable from a geomorphological point of view. However, very often not much importance is given to multicollinearity assessment whose effect is that the coefficient estimates are unstable, with opposite sign and therefore difficult to interpret. Therefore, it should be evaluated every time in order to make a model whose results are geomorphologically correct. In this study the effects of multicollinearity in the predictive performance and robustness of landslide susceptibility models are analyzed. In particular, the multicollinearity is estimated by means of Variation Inflation Index (VIF) which is also used as selection criterion for the independent variables (VIF Stepwise Selection) and compared to the more commonly used AIC Stepwise Selection. The robustness of the results is evaluated through 100 replicates of the dataset. The study area selected to perform this analysis is the Moldavian Plateau where landslides are among the most frequent geomorphological processes. This area has an increasing trend of urbanization and a very high potential regarding the cultural heritage, being the place of discovery of the largest settlement belonging to the Cucuteni Culture from Eastern Europe (that led to the development of the great complex Cucuteni-Tripyllia). Therefore, identifying the areas susceptible to
Model Selection for Geostatistical Models
Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.
2006-02-01
We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.
Franchini, M; Coppola, A; Rocino, A; Zanon, E; Morfini, M; Accorsi, Arianna; Aru, Anna Brigida; Biasoli, Chiara; Cantori, Isabella; Castaman, Giancarlo; Cesaro, Simone; Ciabatta, Carlo; De Cristofaro, Raimondo; Delios, Grazia; Di Minno, Giovanni; D'Incà, Marco; Dragani, Alfredo; Ettorre, Cosimo Pietro; Gagliano, Fabio; Gamba, Gabriella; Gandini, Giorgio; Giordano, Paola; Giuffrida, Gaetano; Gresele, Paolo; Latella, Caterina; Luciani, Matteo; Margaglione, Maurizio; Marietta, Marco; Mazzucconi, Maria Gabriella; Messina, Maria; Molinari, Angelo Claudio; Notarangelo, Lucia Dora; Oliovecchio, Emily; Peyvandi, Flora; Piseddu, Gavino; Rossetti, Gina; Rossi, Vincenza; Santagostino, Elena; Schiavoni, Mario; Schinco, Piercarla; Serino, Maria Luisa; Tagliaferri, Annarita; Testa, Sophie
2014-03-01
Despite great advances in haemophilia care in the last 20 years, a number of questions on haemophilia therapy remain unanswered. These debated issues primarily involve the choice of the product type (plasma-derived vs. recombinant) for patients with different characteristics: specifically, if they were infected by blood-borne virus infections, and if they bear high or low risk of inhibitor development. In addition, the most appropriate treatment regimen in non-inhibitor and inhibitor patients compel physicians operating at the haemophilia treatment centres (HTCs) to take important therapeutic decisions, which are often based on their personal clinical experience rather than on evidence-based recommendations from published literature data. To know the opinion on the most controversial aspects in haemophilia care of Italian expert physicians, who are responsible for common clinical practice and therapeutic decisions, we have conducted a survey among the Directors of HTCs affiliated to the Italian Association of Haemophilia Centres (AICE). A questionnaire, consisting of 19 questions covering the most important topics related to haemophilia treatment, was sent to the Directors of all 52 Italian HTCs. Forty Directors out of 52 (76.9%) responded, accounting for the large majority of HTCs affiliated to the AICE throughout Italy. The results of this survey provide for the first time a picture of the attitudes towards clotting factor concentrate use and product selection of clinicians working at Italian HTCs. PMID:24533954
NASA Astrophysics Data System (ADS)
Bambang Avip Priatna, M.; Lukman, Sumiaty, Encum
2016-02-01
This paper aims to determine the properties of Correspondence Analysis (CA) estimator to estimate latent variable models. The method used is the High-Dimensional AIC (HAIC) method with simulation of Bernoulli distribution data. Stages are: (1) determine the matrix CA; (2) create a model of the CA estimator to estimate the latent variables by using HAIC; (3) simulated the Bernoulli distribution data with repetition 1,000,748 times. The simulation results show the CA estimator models work well.
Model Selection Information Criteria for Non-Nested Latent Class Models.
ERIC Educational Resources Information Center
Lin, Ting Hsiang; Dayton, C. Mitchell
1997-01-01
The use of these three model selection information criteria for latent class models was studied for nonnested models: (1) Akaike's information criterion (H. Akaike, 1973) (AIC); (2) the Schwarz information (G. Schwarz, 1978) (SIC) criterion; and (3) the Bozdogan version of the AIC (CAIC) (H. Bozdogan, 1987). Situations in which each is preferable…
Calu-3 model under AIC and LCC conditions and application for protein permeability studies.
Marušić, Maja; Djurdjevič, Ida; Drašlar, Kazimir; Caserman, Simon
2014-01-01
Broad area of respiratory epithelium with mild surface conditions is an attractive possibility when trans-mucosal delivery of protein drugs is considered. A mucus and cellular barrier of respiratory epithelium can be modelled in vitro by Calu-3 cell line. We have monitored morphology and barrier properties of Calu-3 culture on permeable supports while developing into liquid covered or air interfaced and mucus lined cellular barrier. Besides morphological differences, cultures differed in electrical resistance and permeability to proteins as well. The accelerated permeability to proteins in these models, due to permeability modulator MP C16, was examined. The effect on electrical resistance of cellular layer was rapid in both cultures suggesting easy access of MP C16 to cells even though its overall impact on cell permeability was strongly reduced in mucus covered culture. Differences in properties of the two models enable better understanding of protein transmucosal permeability, suggesting route of transport and MP C16 modulator action. PMID:24664333
ERIC Educational Resources Information Center
Beretvas, S. Natasha; Murphy, Daniel L.
2013-01-01
The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5…
Comparison of six statistical approaches in the selection of appropriate fish growth models
NASA Astrophysics Data System (ADS)
Zhu, Lixin; Li, Lifang; Liang, Zhenlin
2009-09-01
The performance of six statistical approaches, which can be used for selection of the best model to describe the growth of individual fish, was analyzed using simulated and real length-at-age data. The six approaches include coefficient of determination ( R 2), adjusted coefficient of determination (adj.- R 2), root mean squared error (RMSE), Akaike’s information criterion (AIC), bias correction of AIC (AIC c ) and Bayesian information criterion (BIC). The simulation data were generated by five growth models with different numbers of parameters. Four sets of real data were taken from the literature. The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data. The best supported model by the data was identified using each of the six approaches. The results show that R 2 and RMSE have the same properties and perform worst. The sample size has an effect on the performance of adj.- R 2, AIC, AIC c and BIC. Adj.- R 2 does better in small samples than in large samples. AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large. AIC c and BIC have best performance in small and large sample cases, respectively. Use of AIC c or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.
Model selection for multi-component frailty models.
Ha, Il Do; Lee, Youngjo; MacKenzie, Gilbert
2007-11-20
Various frailty models have been developed and are now widely used for analysing multivariate survival data. It is therefore important to develop an information criterion for model selection. However, in frailty models there are several alternative ways of forming a criterion and the particular criterion chosen may not be uniformly best. In this paper, we study an Akaike information criterion (AIC) on selecting a frailty structure from a set of (possibly) non-nested frailty models. We propose two new AIC criteria, based on a conditional likelihood and an extended restricted likelihood (ERL) given by Lee and Nelder (J. R. Statist. Soc. B 1996; 58:619-678). We compare their performance using well-known practical examples and demonstrate that the two criteria may yield rather different results. A simulation study shows that the AIC based on the ERL is recommended, when attention is focussed on selecting the frailty structure rather than the fixed effects. PMID:17476647
Stanley, T.R.; Burnham, K.P.
1998-01-01
Specification of an appropriate model is critical to valid stalistical inference. Given the "true model" for the data is unknown, the goal of model selection is to select a plausible approximating model that balances model bias and sampling variance. Model selection based on information criteria such as AIC or its variant AICc, or criteria like CAIC, has proven useful in a variety of contexts including the analysis of open-population capture-recapture data. These criteria have not been intensively evaluated for closed-population capture-recapture models, which are integer parameter models used to estimate population size (N), and there is concern that they will not perform well. To address this concern, we evaluated AIC, AICc, and CAIC model selection for closed-population capture-recapture models by empirically assessing the quality of inference for the population size parameter N. We found that AIC-, AICc-, and CAIC-selected models had smaller relative mean squared errors than randomly selected models, but that confidence interval coverage on N was poor unless unconditional variance estimates (which incorporate model uncertainty) were used to compute confidence intervals. Overall, AIC and AICc outperformed CAIC, and are preferred to CAIC for selection among the closed-population capture-recapture models we investigated. A model averaging approach to estimation, using AIC. AICc, or CAIC to estimate weights, was also investigated and proved superior to estimation using AIC-, AICc-, or CAIC-selected models. Our results suggested that, for model averaging, AIC or AICc. should be favored over CAIC for estimating weights.
Dynamic microphones M-87/AIC and M-101/AIC and earphone H-143/AIC. [for space shuttle
NASA Technical Reports Server (NTRS)
Reiff, F. H.
1975-01-01
The electrical characteristics of the M-87/AIC and M-101/AIC dynamic microphone and H-143 earphones were tested for the purpose of establishing the relative performance levels of units supplied by four vendors. The microphones and earphones were tested for frequency response, sensitivity, linearity, impedance and noise cancellation. Test results are presented and discussed.
Information criteria and selection of vibration models.
Ruzek, Michal; Guyader, Jean-Louis; Pézerat, Charles
2014-12-01
This paper presents a method of determining an appropriate equation of motion of two-dimensional plane structures like membranes and plates from vibration response measurements. The local steady-state vibration field is used as input for the inverse problem that approximately determines the dispersion curve of the structure. This dispersion curve is then statistically treated with Akaike information criterion (AIC), which compares the experimentally measured curve to several candidate models (equations of motion). The model with the lowest AIC value is then chosen, and the utility of other models can also be assessed. This method is applied to three experimental case studies: A red cedar wood plate for musical instruments, a thick paper subjected to unknown membrane tension, and a thick composite sandwich panel. These three cases give three different situations of a model selection. PMID:25480053
Parameter recovery and model selection in mixed Rasch models.
Preinerstorfer, David; Formann, Anton K
2012-05-01
This study examines the precision of conditional maximum likelihood estimates and the quality of model selection methods based on information criteria (AIC and BIC) in mixed Rasch models. The design of the Monte Carlo simulation study included four test lengths (10, 15, 25, 40), three sample sizes (500, 1000, 2500), two simulated mixture conditions (one and two groups), and population homogeneity (equally sized subgroups) or heterogeneity (one subgroup three times larger than the other). The results show that both increasing sample size and increasing number of items lead to higher accuracy; medium-range parameters were estimated more precisely than extreme ones; and the accuracy was higher in homogeneous populations. The minimum-BIC method leads to almost perfect results and is more reliable than AIC-based model selection. The results are compared to findings by Li, Cohen, Kim, and Cho (2009) and practical guidelines are provided. PMID:21675964
Mazerolle, M.J.
2006-01-01
In ecology, researchers frequently use observational studies to explain a given pattern, such as the number of individuals in a habitat patch, with a large number of explanatory (i.e., independent) variables. To elucidate such relationships, ecologists have long relied on hypothesis testing to include or exclude variables in regression models, although the conclusions often depend on the approach used (e.g., forward, backward, stepwise selection). Though better tools have surfaced in the mid 1970's, they are still underutilized in certain fields, particularly in herpetology. This is the case of the Akaike information criterion (AIC) which is remarkably superior in model selection (i.e., variable selection) than hypothesis-based approaches. It is simple to compute and easy to understand, but more importantly, for a given data set, it provides a measure of the strength of evidence for each model that represents a plausible biological hypothesis relative to the entire set of models considered. Using this approach, one can then compute a weighted average of the estimate and standard error for any given variable of interest across all the models considered. This procedure, termed model-averaging or multimodel inference, yields precise and robust estimates. In this paper, I illustrate the use of the AIC in model selection and inference, as well as the interpretation of results analysed in this framework with two real herpetological data sets. The AIC and measures derived from it is should be routinely adopted by herpetologists. ?? Koninklijke Brill NV 2006.
Model selection bias and Freedman's paradox
Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.
2010-01-01
In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.
ERIC Educational Resources Information Center
Chapman, Rebekah; Buckley, Lisa; Sheehan, Mary
2011-01-01
The Extended Adolescent Injury Checklist (E-AIC), a self-report measure of injury based on the model of the Adolescent Injury Checklist (AIC), was developed for use in the evaluation of school-based interventions. The three stages of this development involved focus groups with adolescents and consultations with medical staff, pilot testing of the…
Akaike information criterion to select well-fit resist models
NASA Astrophysics Data System (ADS)
Burbine, Andrew; Fryer, David; Sturtevant, John
2015-03-01
In the field of model design and selection, there is always a risk that a model is over-fit to the data used to train the model. A model is well suited when it describes the physical system and not the stochastic behavior of the particular data collected. K-fold cross validation is a method to check this potential over-fitting to the data by calibrating with k-number of folds in the data, typically between 4 and 10. Model training is a computationally expensive operation, however, and given a wide choice of candidate models, calibrating each one repeatedly becomes prohibitively time consuming. Akaike information criterion (AIC) is an information-theoretic approach to model selection based on the maximized log-likelihood for a given model that only needs a single calibration per model. It is used in this study to demonstrate model ranking and selection among compact resist modelforms that have various numbers and types of terms to describe photoresist behavior. It is shown that there is a good correspondence of AIC to K-fold cross validation in selecting the best modelform, and it is further shown that over-fitting is, in most cases, not indicated. In modelforms with more than 40 fitting parameters, the size of the calibration data set benefits from additional parameters, statistically validating the model complexity.
Autonomic Intelligent Cyber Sensor (AICS) Version 1.0.1
2015-03-01
The Autonomic Intelligent Cyber Sensor (AICS) provides cyber security and industrial network state awareness for Ethernet based control network implementations. The AICS utilizes collaborative mechanisms based on Autonomic Research and a Service Oriented Architecture (SOA) to: 1) identify anomalous network traffic; 2) discover network entity information; 3) deploy deceptive virtual hosts; and 4) implement self-configuring modules. AICS achieves these goals by dynamically reacting to the industrial human-digital ecosystem in which it resides. Information is transported internally and externally on a standards based, flexible two-level communication structure.
Autonomic Intelligent Cyber Sensor (AICS) Version 1.0.1
Energy Science and Technology Software Center (ESTSC)
2015-03-01
The Autonomic Intelligent Cyber Sensor (AICS) provides cyber security and industrial network state awareness for Ethernet based control network implementations. The AICS utilizes collaborative mechanisms based on Autonomic Research and a Service Oriented Architecture (SOA) to: 1) identify anomalous network traffic; 2) discover network entity information; 3) deploy deceptive virtual hosts; and 4) implement self-configuring modules. AICS achieves these goals by dynamically reacting to the industrial human-digital ecosystem in which it resides. Information is transportedmore » internally and externally on a standards based, flexible two-level communication structure.« less
Model weights and the foundations of multimodel inference
Link, W.A.; Barker, R.J.
2006-01-01
Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike?s information criterion) as a tool for model selection and as a basis for model averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection are naturally linked, and in which the performance of AIC-based tools is naturally evaluated. Prior model weights implicitly associated with the use of AIC are seen to highly favor complex models: in some cases, all but the most highly parameterized models in the model set are virtually ignored a priori. We suggest the usefulness of the weighted BIC (Bayesian information criterion) as a computationally simple alternative to AIC, based on explicit selection of prior model probabilities rather than acceptance of default priors associated with AIC. We note, however, that both procedures are only approximate to the use of exact Bayes factors. We discuss and illustrate technical difficulties associated with Bayes factors, and suggest approaches to avoiding these difficulties in the context of model selection for a logistic regression. Our example highlights the predisposition of AIC weighting to favor complex models and suggests a need for caution in using the BIC for computing approximate posterior model weights.
Towards a Model Selection Rule for Quantum State Tomography
NASA Astrophysics Data System (ADS)
Scholten, Travis; Blume-Kohout, Robin
Quantum tomography on large and/or complex systems will rely heavily on model selection techniques, which permit on-the-fly selection of small efficient statistical models (e.g. small Hilbert spaces) that accurately fit the data. Many model selection tools, such as hypothesis testing or Akaike's AIC, rely implicitly or explicitly on the Wilks Theorem, which predicts the behavior of the loglikelihood ratio statistic (LLRS) used to choose between models. We used Monte Carlo simulations to study the behavior of the LLRS in quantum state tomography, and found that it disagrees dramatically with Wilks' prediction. We propose a simple explanation for this behavior; namely, that boundaries (in state space and between models) play a significant role in determining the distribution of the LLRS. The resulting distribution is very complex, depending strongly both on the true state and the nature of the data. We consider a simplified model that neglects anistropy in the Fisher information, derive an almost analytic prediction for the mean value of the LLRS, and compare it to numerical experiments. While our simplified model outperforms the Wilks Theorem, it still does not predict the LLRS accurately, implying that alternative methods may be necessary for tomographic model selection. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE.
Thomas, D.L.; Johnson, D.; Griffith, B.
2006-01-01
Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a
Quantitative Rheological Model Selection
NASA Astrophysics Data System (ADS)
Freund, Jonathan; Ewoldt, Randy
2014-11-01
The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.
Mission science value-cost savings from the Advanced Imaging Communication System (AICS)
NASA Technical Reports Server (NTRS)
Rice, R. F.
1984-01-01
An Advanced Imaging Communication System (AICS) was proposed in the mid-1970s as an alternative to the Voyager data/communication system architecture. The AICS achieved virtually error free communication with little loss in the downlink data rate by concatenating a powerful Reed-Solomon block code with the Voyager convolutionally coded, Viterbi decoded downlink channel. The clean channel allowed AICS sophisticated adaptive data compression techniques. Both Voyager and the Galileo mission have implemented AICS components, and the concatenated channel itself is heading for international standardization. An analysis that assigns a dollar value/cost savings to AICS mission performance gains is presented. A conservative value or savings of $3 million for Voyager, $4.5 million for Galileo, and as much as $7 to 9.5 million per mission for future projects such as the proposed Mariner Mar 2 series is shown.
Double point source W-phase inversion: Real-time implementation and automated model selection
NASA Astrophysics Data System (ADS)
Nealy, Jennifer L.; Hayes, Gavin P.
2015-12-01
Rapid and accurate characterization of an earthquake source is an extremely important and ever evolving field of research. Within this field, source inversion of the W-phase has recently been shown to be an effective technique, which can be efficiently implemented in real-time. An extension to the W-phase source inversion is presented in which two point sources are derived to better characterize complex earthquakes. A single source inversion followed by a double point source inversion with centroid locations fixed at the single source solution location can be efficiently run as part of earthquake monitoring network operational procedures. In order to determine the most appropriate solution, i.e., whether an earthquake is most appropriately described by a single source or a double source, an Akaike information criterion (AIC) test is performed. Analyses of all earthquakes of magnitude 7.5 and greater occurring since January 2000 were performed with extended analyses of the September 29, 2009 magnitude 8.1 Samoa earthquake and the April 19, 2014 magnitude 7.5 Papua New Guinea earthquake. The AIC test is shown to be able to accurately select the most appropriate model and the selected W-phase inversion is shown to yield reliable solutions that match published analyses of the same events.
Double point source W-phase inversion: Real-time implementation and automated model selection
Nealy, Jennifer; Hayes, Gavin
2015-01-01
Rapid and accurate characterization of an earthquake source is an extremely important and ever evolving field of research. Within this field, source inversion of the W-phase has recently been shown to be an effective technique, which can be efficiently implemented in real-time. An extension to the W-phase source inversion is presented in which two point sources are derived to better characterize complex earthquakes. A single source inversion followed by a double point source inversion with centroid locations fixed at the single source solution location can be efficiently run as part of earthquake monitoring network operational procedures. In order to determine the most appropriate solution, i.e., whether an earthquake is most appropriately described by a single source or a double source, an Akaike information criterion (AIC) test is performed. Analyses of all earthquakes of magnitude 7.5 and greater occurring since January 2000 were performed with extended analyses of the September 29, 2009 magnitude 8.1 Samoa earthquake and the April 19, 2014 magnitude 7.5 Papua New Guinea earthquake. The AIC test is shown to be able to accurately select the most appropriate model and the selected W-phase inversion is shown to yield reliable solutions that match published analyses of the same events.
Tightening the Noose on LMXB Formation of MSPs: Need for AIC ?
NASA Astrophysics Data System (ADS)
Grindlay, J. E.; Yi, I.
1997-12-01
The origin of millisecond pulsars (MSPs) remains an outstanding problem despite the early and considerable evidence that they are the descendents of neutron stars spun up by accretion in low mass x-ray binaries (LMXBs). The route to MSPs from LMXBs may pass through the high luminosity Z-source LMXBs but is (severely) limited by the very limited population (and apparent birth rate) of Z-sources available. The more numerous x-ray bursters, the Atoll sources, are likely to (still) be short in numbers or birth rate but are now also found to be likely inefficient in the spin-up torques they can provide: the accretion in these relatively low accretion rate systems is likely dominated by an advection dominated flow in which matter accretes onto the NS via sub-Keplerian flows which then transfer correspondingly less angular momentum to the NS. We investigate the implications of the possible ADAF flows in low luminosity NS-LMXBs and find it is unlikely they can produce MSPs. The standard model can still be allowed if most NS-LMXBs are quiescent and undergo transient-like outbursts similar to the soft x-ray transients (which mostly contain black holes). However, apart from Cen X-4 and Aql X-1, few such systems have been found and the SXTs appear instead to be significantly deficient in NS systems. Direct production of MSPs by the accretion induced collapse (AIC) of white dwarfs has been previously suggested to solve the MSP vs. LMXB birth rate problem. We re-examine AIC models in light of the new constraints on direct LMXB production and the additional difficulty imposed by ADAF flows and constraints on SXT populations and derive constraints on the progenitor WD spin and magnetic fields.
Selecting among competing models of electro-optic, infrared camera system range performance
Nichols, Jonathan M.; Hines, James E.; Nichols, James D.
2013-01-01
Range performance is often the key requirement around which electro-optical and infrared camera systems are designed. This work presents an objective framework for evaluating competing range performance models. Model selection based on the Akaike’s Information Criterion (AIC) is presented for the type of data collected during a typical human observer and target identification experiment. These methods are then demonstrated on observer responses to both visible and infrared imagery in which one of three maritime targets was placed at various ranges. We compare the performance of a number of different models, including those appearing previously in the literature. We conclude that our model-based approach offers substantial improvements over the traditional approach to inference, including increased precision and the ability to make predictions for some distances other than the specific set for which experimental trials were conducted.
Individual Influence on Model Selection
ERIC Educational Resources Information Center
Sterba, Sonya K.; Pek, Jolynn
2012-01-01
Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…
AN/AIC-22(V) Intercommunications Set (ICS) fiber optic link engineering analysis report
NASA Astrophysics Data System (ADS)
Minter, Richard; Blocksom, Roland; Ling, Christopher
1990-08-01
Electromagnetic interference (EMI) problems constitute a serious threat to operational Navy aircraft systems. The application of fiber optic technology is a potential solution to these problems. EMI reported problems in the P-3 patrol aircraft AN/AIC-22(V) Intercommunications System (ICS) were selected from an EMI problem database for investigation and possible application of fiber optic technology. A proof-of-concept experiment was performed to demonstrate the level of EMI immunity of fiber optics when used in an ICS. A full duplex single channel fiber optic audio link was designed and assembled from modified government furnished equipment (GFE) previously used in another Navy fiber optic application. The link was taken to the Naval Air Test Center (NATC) Patuxent River, Maryland and temporarily installed in a Naval Research Laboratory (NRL) P-3A aircraft for a side-by-side comparison test with the installed ICS. With regards to noise reduction, the fiber optic link provided a qualitative improvement over the conventional ICS. In an effort to obtain a quantitative measure of comparison, audio frequency range both with and without operation of the aircraft VHF and UHF radio transmitters.
NASA Astrophysics Data System (ADS)
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-01-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible. PMID:25745272
Selecting a distributional assumption for modelling relative densities of benthic macroinvertebrates
Gray, B.R.
2005-01-01
The selection of a distributional assumption suitable for modelling macroinvertebrate density data is typically challenging. Macroinvertebrate data often exhibit substantially larger variances than expected under a standard count assumption, that of the Poisson distribution. Such overdispersion may derive from multiple sources, including heterogeneity of habitat (historically and spatially), differing life histories for organisms collected within a single collection in space and time, and autocorrelation. Taken to extreme, heterogeneity of habitat may be argued to explain the frequent large proportions of zero observations in macroinvertebrate data. Sampling locations may consist of habitats defined qualitatively as either suitable or unsuitable. The former category may yield random or stochastic zeroes and the latter structural zeroes. Heterogeneity among counts may be accommodated by treating the count mean itself as a random variable, while extra zeroes may be accommodated using zero-modified count assumptions, including zero-inflated and two-stage (or hurdle) approaches. These and linear assumptions (following log- and square root-transformations) were evaluated using 9 years of mayfly density data from a 52 km, ninth-order reach of the Upper Mississippi River (n = 959). The data exhibited substantial overdispersion relative to that expected under a Poisson assumption (i.e. variance:mean ratio = 23 ??? 1), and 43% of the sampling locations yielded zero mayflies. Based on the Akaike Information Criterion (AIC), count models were improved most by treating the count mean as a random variable (via a Poisson-gamma distributional assumption) and secondarily by zero modification (i.e. improvements in AIC values = 9184 units and 47-48 units, respectively). Zeroes were underestimated by the Poisson, log-transform and square root-transform models, slightly by the standard negative binomial model but not by the zero-modified models (61%, 24%, 32%, 7%, and 0%, respectively
ERIC Educational Resources Information Center
Bogiages, Christopher A.; Lotter, Christine
2011-01-01
In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…
Test procedures, AN/AIC-27 system and component units. [for space shuttle
NASA Technical Reports Server (NTRS)
Reiff, F. H.
1975-01-01
The AN/AIC-27 (v) intercommunication system is a 30-channel audio distribution which consists of: air crew station units, maintenance station units, and a central control unit. A test procedure for each of the above units and also a test procedure for the system are presented. The intent of the test is to provide data for use in shuttle audio subsystem design.
Regularization Parameter Selections via Generalized Information Criterion
Zhang, Yiyun; Li, Runze; Tsai, Chih-Ling
2009-01-01
We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. In this paper, we propose employing the generalized information criterion (GIC), encompassing the commonly used Akaike information criterion (AIC) and Bayesian information criterion (BIC), for selecting the regularization parameter. Our proposal makes a connection between the classical variable selection criteria and the regularization parameter selections for the nonconcave penalized likelihood approaches. We show that the BIC-type selector enables identification of the true model consistently, and the resulting estimator possesses the oracle property in the terminology of Fan and Li (2001). In contrast, however, the AIC-type selector tends to overfit with positive probability. We further show that the AIC-type selector is asymptotically loss efficient, while the BIC-type selector is not. Our simulation results confirm these theoretical findings, and an empirical example is presented. Some technical proofs are given in the online supplementary material. PMID:20676354
Model selection for logistic regression models
NASA Astrophysics Data System (ADS)
Duller, Christine
2012-09-01
Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.
Comparison of Two Gas Selection Methodologies: An Application of Bayesian Model Averaging
Renholds, Andrea S.; Thompson, Sandra E.; Anderson, Kevin K.; Chilton, Lawrence K.
2006-03-31
One goal of hyperspectral imagery analysis is the detection and characterization of plumes. Characterization includes identifying the gases in the plumes, which is a model selection problem. Two gas selection methods compared in this report are Bayesian model averaging (BMA) and minimum Akaike information criterion (AIC) stepwise regression (SR). Simulated spectral data from a three-layer radiance transfer model were used to compare the two methods. Test gases were chosen to span the types of spectra observed, which exhibit peaks ranging from broad to sharp. The size and complexity of the search libraries were varied. Background materials were chosen to either replicate a remote area of eastern Washington or feature many common background materials. For many cases, BMA and SR performed the detection task comparably in terms of the receiver operating characteristic curves. For some gases, BMA performed better than SR when the size and complexity of the search library increased. This is encouraging because we expect improved BMA performance upon incorporation of prior information on background materials and gases.
Miyazawa, Sanzo
2011-01-01
Background Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Results Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. Conclusions/Significance The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences. PMID:21445250
Multidimensional Rasch Model Information-Based Fit Index Accuracy
ERIC Educational Resources Information Center
Harrell-Williams, Leigh M.; Wolfe, Edward W.
2013-01-01
Most research on confirmatory factor analysis using information-based fit indices (Akaike information criterion [AIC], Bayesian information criteria [BIC], bias-corrected AIC [AICc], and consistent AIC [CAIC]) has used a structural equation modeling framework. Minimal research has been done concerning application of these indices to item response…
Entropic criterion for model selection
NASA Astrophysics Data System (ADS)
Tseng, Chih-Yuan
2006-10-01
Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.
Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data
Xu, Lizhen; Paterson, Andrew D.; Turpin, Williams; Xu, Wei
2015-01-01
Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects. PMID:26148172
Selected Tether Applications Cost Model
NASA Technical Reports Server (NTRS)
Keeley, Michael G.
1988-01-01
Diverse cost-estimating techniques and data combined into single program. Selected Tether Applications Cost Model (STACOM 1.0) is interactive accounting software tool providing means for combining several independent cost-estimating programs into fully-integrated mathematical model capable of assessing costs, analyzing benefits, providing file-handling utilities, and putting out information in text and graphical forms to screen, printer, or plotter. Program based on Lotus 1-2-3, version 2.0. Developed to provide clear, concise traceability and visibility into methodology and rationale for estimating costs and benefits of operations of Space Station tether deployer system.
Variable selection with stepwise and best subset approaches
2016-01-01
While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786
Model selection for modified gravity.
Kitching, T D; Simpson, F; Heavens, A F; Taylor, A N
2011-12-28
In this article, we review model selection predictions for modified gravity scenarios as an explanation for the observed acceleration of the expansion history of the Universe. We present analytical procedures for calculating expected Bayesian evidence values in two cases: (i) that modified gravity is a simple parametrized extension of general relativity (GR; two nested models), such that a Bayes' factor can be calculated, and (ii) that we have a class of non-nested models where a rank-ordering of evidence values is required. We show that, in the case of a minimal modified gravity parametrization, we can expect large area photometric and spectroscopic surveys, using three-dimensional cosmic shear and baryonic acoustic oscillations, to 'decisively' distinguish modified gravity models over GR (or vice versa), with odds of ≫1:100. It is apparent that the potential discovery space for modified gravity models is large, even in a simple extension to gravity models, where Newton's constant G is allowed to vary as a function of time and length scale. On the time and length scales where dark energy dominates, it is only through large-scale cosmological experiments that we can hope to understand the nature of gravity. PMID:22084296
Shinohara, Haruka; Kumazaki, Minami; Minami, Yosuke; Ito, Yuko; Sugito, Nobuhiko; Kuranaga, Yuki; Taniguchi, Kohei; Yamada, Nami; Otsuki, Yoshinori; Naoe, Tomoki; Akao, Yukihiro
2016-02-01
In Ph-positive leukemia, imatinib brought marked clinical improvement; however, further improvement is needed to prevent relapse. Cancer cells efficiently use limited energy sources, and drugs targeting cellular metabolism improve the efficacy of therapy. In this study, we characterized the effects of novel anti-cancer fatty-acid derivative AIC-47 and imatinib, focusing on cancer-specific energy metabolism in chronic myeloid leukemia cells. AIC-47 and imatinib in combination exhibited a significant synergic cytotoxicity. Imatinib inhibited only the phosphorylation of BCR-ABL; whereas AIC-47 suppressed the expression of the protein itself. Both AIC-47 and imatinib modulated the expression of pyruvate kinase M (PKM) isoforms from PKM2 to PKM1 through the down-regulation of polypyrimidine tract-binding protein 1 (PTBP1). PTBP1 functions as alternative splicing repressor of PKM1, resulting in expression of PKM2, which is an inactive form of pyruvate kinase for the last step of glycolysis. Although inactivation of BCR-ABL by imatinib strongly suppressed glycolysis, compensatory fatty-acid oxidation (FAO) activation supported glucose-independent cell survival by up-regulating CPT1C, the rate-limiting FAO enzyme. In contrast, AIC-47 inhibited the expression of CPT1C and directly fatty-acid metabolism. These findings were also observed in the CD34(+) fraction of Ph-positive acute lymphoblastic leukemia cells. These results suggest that AIC-47 in combination with imatinib strengthened the attack on cancer energy metabolism, in terms of both glycolysis and compensatory activation of FAO. PMID:26607903
A Logistic Regression Model for Personnel Selection.
ERIC Educational Resources Information Center
Raju, Nambury S.; And Others
1991-01-01
A two-parameter logistic regression model for personnel selection is proposed. The model was tested with a database of 84,808 military enlistees. The probability of job success was related directly to trait levels, addressing such topics as selection, validity generalization, employee classification, selection bias, and utility-based fair…
IRT Model Selection Methods for Dichotomous Items
ERIC Educational Resources Information Center
Kang, Taehoon; Cohen, Allan S.
2007-01-01
Fit of the model to the data is important if the benefits of item response theory (IRT) are to be obtained. In this study, the authors compared model selection results using the likelihood ratio test, two information-based criteria, and two Bayesian methods. An example illustrated the potential for inconsistency in model selection depending on…
Model Selection Indices for Polytomous Items
ERIC Educational Resources Information Center
Kang, Taehoon; Cohen, Allan S.; Sung, Hyun-Jung
2009-01-01
This study examines the utility of four indices for use in model selection with nested and nonnested polytomous item response theory (IRT) models: a cross-validation index and three information-based indices. Four commonly used polytomous IRT models are considered: the graded response model, the generalized partial credit model, the partial credit…
The Coalescent Process in Models with Selection
Kaplan, N. L.; Darden, T.; Hudson, R. R.
1988-01-01
Statistical properties of the process describing the genealogical history of a random sample of genes are obtained for a class of population genetics models with selection. For models with selection, in contrast to models without selection, the distribution of this process, the coalescent process, depends on the distribution of the frequencies of alleles in the ancestral generations. If the ancestral frequency process can be approximated by a diffusion, then the mean and the variance of the number of segregating sites due to selectively neutral mutations in random samples can be numerically calculated. The calculations are greatly simplified if the frequencies of the alleles are tightly regulated. If the mutation rates between alleles maintained by balancing selection are low, then the number of selectively neutral segregating sites in a random sample of genes is expected to substantially exceed the number predicted under a neutral model. PMID:3066685
Model selection for anomaly detection
NASA Astrophysics Data System (ADS)
Burnaev, E.; Erofeev, P.; Smolyakov, D.
2015-12-01
Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.
An Economic Model for Selective Admissions
ERIC Educational Resources Information Center
Haglund, Alma
1978-01-01
The author presents an economic model for selective admissions to postsecondary nursing programs. Primary determinants of the admissions model are employment needs, availability of educational resources, and personal resources (ability and learning potential). As there are more applicants than resources, selective admission practices are…
Costa, L R F; Barthem, R B; Albernaz, A L; Bittencourt, M M; Villacorta-Corrêa, M A
2013-05-01
The tambaqui, Colossoma macropomum, is one of the most commercially valuable Amazonian fish species, and in the floodplains of the region, they are caught in both rivers and lakes. Most growth studies on this species to date have adjusted only one growth model, the von Bertalanffy, without considering its possible uncertainties. In this study, four different models (von Bertalanffy, Logistic, Gompertz and the general model of Schnüte-Richards) were adjusted to a data set of fish caught within lakes from the middle Solimões River. These models were adjusted by non-linear equations, using the sample size of each age class as its weight. The adjustment evaluation of each model was based on the Akaike Information Criterion (AIC), the variation of AIC between the models (Δi) and the evidence weights (wi). Both the Logistic (Δi = 0.0) and Gompertz (Δi = 1.12) models were supported by the data, but neither of them was clearly superior (wi, respectively 52.44 and 29.95%). Thus, we propose the use of an averaged-model to estimate the asymptotic length (L∞). The averaged-model, based on Logistic and Gompertz models, resulted in an estimate of L∞=90.36, indicating that the tambaqui would take approximately 25 years to reach average size. PMID:23917568
A Collaborative Model for Principal Selection.
ERIC Educational Resources Information Center
Richardson, M. D.; And Others
Although the principal is critical for the success of the school and the school district, many school districts lack a structured and systematic means for identifying and selecting principals. This paper presents a collaborative model for principal selection, which is based on a valid job description, advertisement of the position, interview…
Review and selection of unsaturated flow models
Reeves, M.; Baker, N.A.; Duguid, J.O.
1994-04-04
Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.
Model Selection with the Linear Mixed Model for Longitudinal Data
ERIC Educational Resources Information Center
Ryoo, Ji Hoon
2011-01-01
Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…
Ion selective transistor modelling for behavioural simulations.
Daniel, M; Janicki, M; Wroblewski, W; Dybko, A; Brzozka, Z; Napieralski, A
2004-01-01
Computer aided design and simulation of complex silicon microsystems oriented for environment monitoring requires efficient and accurate models of ion selective sensors, compatible with the existing behavioural simulators. This paper concerns sensors based on the back-side contact Ion Sensitive Field Effect Transistors (ISFETs). The ISFETs with silicon nitride gate are sensitive to hydrogen ion concentration. When the transistor gate is additionally covered with a special ion selective membrane, selectivity to other than hydrogen ions can be achieved. Such sensors are especially suitable for flow analysis of solutions containing various ions. The problem of ion selective sensor modelling is illustrated here on a practical example of an ammonium sensitive membrane. The membrane is investigated in the presence of some interfering ions and appropriate selectivity coefficients are determined. Then, the model of the whole sensor is created and used in subsequent electrical simulations. Providing that appropriate selectivity coefficients are known, the proposed model is applicable for any membrane, and can be straightforwardly implemented for behavioural simulation of water monitoring microsystems. The model has been already applied in a real on-line water pollution monitoring system for detection of various contaminants. PMID:15685987
Bacheler, N.M.; Hightower, J.E.; Burdick, S.M.; Paramore, L.M.; Buckel, J.A.; Pollock, K.H.
2010-01-01
Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated. ?? 2009 Elsevier B.V.
Burdick, Summer M.; Hightower, Joseph E.; Bacheler, Nathan M.; Paramore, Lee M.; Buckel, Jeffrey A.; Pollock, Kenneth H.
2010-01-01
Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated.
A Pragmatic Model for Instructional Technology Selection.
ERIC Educational Resources Information Center
Vaccare, Carmel; Sherman, Greg
2001-01-01
The 4S model uses the criteria "Simple, Stable, Scalable, and Sustainable" as a filter for selecting instructional technologies. This paper considers a social dimension that uses culture and interaction as the primary consideration in the deployment of any instructional technology within the context of the 4S model. (Author/MES)
Melody Track Selection Using Discriminative Language Model
NASA Astrophysics Data System (ADS)
Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong
In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.
Selecting model complexity in learning problems
Buescher, K.L.; Kumar, P.R.
1993-10-01
To learn (or generalize) from noisy data, one must resist the temptation to pick a model for the underlying process that overfits the data. Many existing techniques solve this problem at the expense of requiring the evaluation of an absolute, a priori measure of each model`s complexity. We present a method that does not. Instead, it uses a natural, relative measure of each model`s complexity. This method first creates a pool of ``simple`` candidate models using part of the data and then selects from among these by using the rest of the data.
Comparing Smoothing Techniques for Fitting the Nonlinear Effect of Covariate in Cox Models
Roshani, Daem; Ghaderi, Ebrahim
2016-01-01
Background and Objective: Cox model is a popular model in survival analysis, which assumes linearity of the covariate on the log hazard function, While continuous covariates can affect the hazard through more complicated nonlinear functional forms and therefore, Cox models with continuous covariates are prone to misspecification due to not fitting the correct functional form for continuous covariates. In this study, a smooth nonlinear covariate effect would be approximated by different spline functions. Material and Methods: We applied three flexible nonparametric smoothing techniques for nonlinear covariate effect in the Cox models: penalized splines, restricted cubic splines and natural splines. Akaike information criterion (AIC) and degrees of freedom were used to smoothing parameter selection in penalized splines model. The ability of nonparametric methods was evaluated to recover the true functional form of linear, quadratic and nonlinear functions, using different simulated sample sizes. Data analysis was carried out using R 2.11.0 software and significant levels were considered 0.05. Results: Based on AIC, the penalized spline method had consistently lower mean square error compared to others to selection of smoothed parameter. The same result was obtained with real data. Conclusion: Penalized spline smoothing method, with AIC to smoothing parameter selection, was more accurate in evaluate of relation between covariate and log hazard function than other methods. PMID:27041809
An Ss Model with Adverse Selection.
ERIC Educational Resources Information Center
House, Christopher L.; Leahy, John V.
2004-01-01
We present a model of the market for a used durable in which agents face fixed costs of adjustment, the magnitude of which depends on the degree of adverse selection in the secondary market. We find that, unlike typical models, the sS bands in our model contract as the variance of the shock increases. We also analyze a dynamic version of the model…
Grid selection of models of nucleotide substitution
Loureiro, Marta; Pan, Miguel; Rodríguez-Pascual, Manuel; Posada, David; Mayo, Rafael
2016-01-01
jModelTest is a Java program for the statistical selection of models of nucleotide substitution with thousands of users around the world. For large data sets, the calculations carried out by this program can be too expensive for many users. Here we describe the port of the jModeltest code for Grid computing using DRMAA. This work should facilitate the use of jModelTest on a broad scale. PMID:20543444
Automated sample plan selection for OPC modeling
NASA Astrophysics Data System (ADS)
Casati, Nathalie; Gabrani, Maria; Viswanathan, Ramya; Bayraktar, Zikri; Jaiswal, Om; DeMaris, David; Abdo, Amr Y.; Oberschmidt, James; Krause, Andreas
2014-03-01
It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers' insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.
Posterior Predictive Bayesian Phylogenetic Model Selection
Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn
2014-01-01
We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892
On spatial mutation-selection models
Kondratiev, Yuri; Kutoviy, Oleksandr E-mail: kutovyi@mit.edu; Minlos, Robert Pirogov, Sergey
2013-11-15
We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.
Tabu search model selection for SVM.
Lebrun, Gilles; Charrier, Christophe; Lezoray, Olivier; Cardot, Hubert
2008-02-01
A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times. PMID:18344220
Observability in strategic models of viability selection.
Gámez, M; Carreño, R; Kósa, A; Varga, Z
2003-10-01
Strategic models of frequency-dependent viability selection, in terms of mathematical systems theory, are considered as a dynamic observation system. Using a general sufficient condition for observability of nonlinear systems with invariant manifold, it is studied whether, observing certain phenotypic characteristics of the population, the development of its genetic state can be recovered, at least near equilibrium. PMID:14563566
Student Selection and the Special Regression Model.
ERIC Educational Resources Information Center
Deck, Dennis D.
The feasibility of constructing composite scores which will yield pretest measures having all the properties required by the special regression model is explored as an alternative to the single pretest score usually used in student selection for Elementary Secondary Education Act Title I compensatory education programs. Reading data, including…
A model for plant lighting system selection
NASA Technical Reports Server (NTRS)
Ciolkosz, D. E.; Albright, L. D.; Sager, J. C.; Langhans, R. W.
2002-01-01
A decision model is presented that compares lighting systems for a plant growth scenario and chooses the most appropriate system from a given set of possible choices. The model utilizes a Multiple Attribute Utility Theory approach, and incorporates expert input and performance simulations to calculate a utility value for each lighting system being considered. The system with the highest utility is deemed the most appropriate system. The model was applied to a greenhouse scenario, and analyses were conducted to test the model's output for validity. Parameter variation indicates that the model performed as expected. Analysis of model output indicates that differences in utility among the candidate lighting systems were sufficiently large to give confidence that the model's order of selection was valid.
Review and selection of unsaturated flow models
1993-09-10
Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.
Bayesian Model Selection for Group Studies
Stephan, Klaas Enno; Penny, Will D.; Daunizeau, Jean; Moran, Rosalyn J.; Friston, Karl J.
2009-01-01
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other
Integrative variable selection via Bayesian model uncertainty.
Quintana, M A; Conti, D V
2013-12-10
We are interested in developing integrative approaches for variable selection problems that incorporate external knowledge on a set of predictors of interest. In particular, we have developed an integrative Bayesian model uncertainty (iBMU) method, which formally incorporates multiple sources of data via a second-stage probit model on the probability that any predictor is associated with the outcome of interest. Using simulations, we demonstrate that iBMU leads to an increase in power to detect true marginal associations over more commonly used variable selection techniques, such as least absolute shrinkage and selection operator and elastic net. In addition, iBMU leads to a more efficient model search algorithm over the basic BMU method even when the predictor-level covariates are only modestly informative. The increase in power and efficiency of our method becomes more substantial as the predictor-level covariates become more informative. Finally, we demonstrate the power and flexibility of iBMU for integrating both gene structure and functional biomarker information into a candidate gene study investigating over 50 genes in the brain reward system and their role with smoking cessation from the Pharmacogenetics of Nicotine Addiction and Treatment Consortium. PMID:23824835
Aspects of model selection in multivariate analyses
Picard, R.
1982-01-01
Analysis of data sets that involve large numbers of variables usually entails some type of model fitting and data reduction. In regression problems, a fitted model that is obtained by a selection process can be difficult to evaluate because of optimism induced by the choice mechanism. Problems in areas such as discriminant analysis, calibration, and the like often lead to similar difficulties. The preceeding sections reviewed some of the general ideas behind assessment of regression-type predictors and illustrated how they can be easily incorporated into a standard data analysis.
Image Discrimination Models With Stochastic Channel Selection
NASA Technical Reports Server (NTRS)
Ahumada, Albert J., Jr.; Beard, Bettina L.; Null, Cynthia H. (Technical Monitor)
1995-01-01
Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.
Model selection for radiochromic film dosimetry
NASA Astrophysics Data System (ADS)
Méndez, I.
2015-05-01
The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to provide better results than using Micke-Mayer perturbation models. Among the models being compared, the triple-channel model with Truncated Normal perturbations, net optical density as the response and subject to the application of lateral corrections was found to be the most accurate model. The scope of this study was circumscribed by the limits under which the models were tested. In this study, the films were irradiated with megavoltage radiotherapy beams, with doses from about 20-600 cGy, entire (8 inch × 10 inch) films were scanned, the functional form of the sensitometric curves was a polynomial and the different lots were calibrated using the plane-based method.
Selection and estimation for mixed graphical models
Chen, Shizhe; Witten, Daniela M.; shojaie, Ali
2016-01-01
Summary We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies.
Linder, Joshua M; Lawler, Richard R
2012-11-01
Determining the ecological and anthropogenic factors that shape the abundance and distribution of wild primates is a critical component of primate conservation research. Such research is complicated, however, whenever the species under study are encountered infrequently, a characteristic of many taxa that are threatened with extinction. Typically, the resulting data sets based on surveys of such species will have a high frequency of zero counts which makes it difficult to determine the predictor variables that are associated with species abundance. In this study, we test various statistical models using survey data that was gathered on seven species of primate in Korup National Park, Cameroon. Predictor variables include hunting signs and aspects of habitat structure and floristic composition. Our statistical models include zero-inflated models that are tailored to deal with a high frequency of zero counts. First, using exploratory data analysis we found the most informative set of models as ranked by Δ-AIC (Akaike's information criterion). On the basis of this analysis, we used five predictor variables to construct several regression models including Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial. Total basal area of all trees, density of secondary tree species, hunting signs, and mean basal area of all trees were significant predictors of abundance in the zero-inflated models. We discuss the statistical logic behind zero-inflated models and provide an interpretation of parameter estimates. We recommend that researchers explore a variety of models when determining the factors that correlate with primate abundance. PMID:22991216
NASA Astrophysics Data System (ADS)
Taormina, Riccardo; Chau, Kwok-Wing
2015-10-01
Selecting an adequate set of inputs is a critical step for successful data-driven streamflow prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that employs Binary-coded discrete Fully Informed Particle Swarm optimization (BFIPS) and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms. A scheme is employed to encode the subset of selected inputs and ELM specifications into the binary particles, which are evolved using single objective and multi-objective BFIPS optimization (MBFIPS). The performances of these ELM-based methods are assessed using the evaluation criteria and the datasets included in the comprehensive IVS evaluation framework proposed by Galelli et al. (2014). From a comparison with 4 major IVS techniques used in their original study it emerges that the proposed methods compare very well in terms of selection accuracy. The best performers were found to be (1) a MBFIPS-ELM algorithm based on the concurrent minimization of an error function and the number of selected inputs, and (2) a BFIPS-ELM algorithm based on the minimization of a variant of the Akaike Information Criterion (AIC). The first technique is arguably the most accurate overall, and is able to reach an almost perfect specification of the optimal input subset for a partially synthetic rainfall-runoff experiment devised for the Kentucky River basin. In addition, MBFIPS-ELM allows for the determination of the relative importance of the selected inputs. On the other hand, the BFIPS-ELM is found to consistently reach high accuracy scores while being considerably faster. By extrapolating the results obtained on the IVS test-bed, it can be concluded that the proposed techniques are particularly suited for rainfall-runoff modeling applications characterized by high nonlinearity in the catchment dynamics.
Improved modeling of GPS selective availability
NASA Technical Reports Server (NTRS)
Braasch, Michael S.; Fink, Annmarie; Duffus, Keith
1994-01-01
Selective Availability (SA) represents the dominant error source for stand-alone users of the Global Positioning System (GPS). Even for DGPS, SA mandates the update rate required for a desired level of accuracy in realtime applications. As was witnessed in the recent literature, the ability to model this error source is crucial to the proper evaluation of GPS-based systems. A variety of SA models were proposed to date; however, each has its own shortcomings. Most of these models were based on limited data sets or data which were corrupted by additional error sources. A comprehensive treatment of the problem is presented. The phenomenon of SA is discussed and a technique is presented whereby both clock and orbit components of SA are identifiable. Extensive SA data sets collected from Block 2 satellites are presented. System Identification theory then is used to derive a robust model of SA from the data. This theory also allows for the statistical analysis of SA. The stationarity of SA over time and across different satellites is analyzed and its impact on the modeling problem is discussed.
Modeling selective local interactions with memory
Galante, Amanda; Levy, Doron
2012-01-01
Recently we developed a stochastic particle system describing local interactions between cyanobacteria. We focused on the common freshwater cyanobacteria Synechocystis sp., which are coccoidal bacteria that utilize group dynamics to move toward a light source, a motion referred to as phototaxis. We were particularly interested in the local interactions between cells that were located in low to medium density areas away from the front. The simulations of our stochastic particle system in 2D replicated many experimentally observed phenomena, such as the formation of aggregations and the quasi-random motion of cells. In this paper, we seek to develop a better understanding of group dynamics produced by this model. To facilitate this study, we replace the stochastic model with a system of ordinary differential equations describing the evolution of particles in 1D. Unlike many other models, our emphasis is on particles that selectively choose one of their neighbors as the preferred direction of motion. Furthermore, we incorporate memory by allowing persistence in the motion. We conduct numerical simulations which allow us to efficiently explore the space of parameters, in order to study the stability, size, and merging of aggregations. PMID:24244060
Transformation model selection by multiple hypotheses testing
NASA Astrophysics Data System (ADS)
Lehmann, Rüdiger
2014-12-01
Transformations between different geodetic reference frames are often performed such that first the transformation parameters are determined from control points. If in the first place we do not know which of the numerous transformation models is appropriate then we can set up a multiple hypotheses test. The paper extends the common method of testing transformation parameters for significance, to the case that also constraints for such parameters are tested. This provides more flexibility when setting up such a test. One can formulate a general model with a maximum number of transformation parameters and specialize it by adding constraints to those parameters, which need to be tested. The proper test statistic in a multiple test is shown to be either the extreme normalized or the extreme studentized Lagrange multiplier. They are shown to perform superior to the more intuitive test statistics derived from misclosures. It is shown how model selection by multiple hypotheses testing relates to the use of information criteria like AICc and Mallows' , which are based on an information theoretic approach. Nevertheless, whenever comparable, the results of an exemplary computation almost coincide.
Entropic Priors and Bayesian Model Selection
NASA Astrophysics Data System (ADS)
Brewer, Brendon J.; Francis, Matthew J.
2009-12-01
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual Bayesian ``Occam's Razor.'' This is illustrated with a simple example involving what Jaynes called a ``sure thing'' hypothesis. Jaynes' resolution of the situation involved introducing a large number of alternative ``sure thing'' hypotheses that were possible before we observed the data. However, in more complex situations, it may not be possible to explicitly enumerate large numbers of alternatives. The entropic priors formalism produces the desired result without modifying the hypothesis space or requiring explicit enumeration of alternatives; all that is required is a good model for the prior predictive distribution for the data. This idea is illustrated with a simple rigged-lottery example, and we outline how this idea may help to resolve a recent debate amongst cosmologists: is dark energy a cosmological constant, or has it evolved with time in some way? And how shall we decide, when the data are in?
Bayesian model selection analysis of WMAP3
Parkinson, David; Mukherjee, Pia; Liddle, Andrew R.
2006-06-15
We present a Bayesian model selection analysis of WMAP3 data using our code CosmoNest. We focus on the density perturbation spectral index n{sub S} and the tensor-to-scalar ratio r, which define the plane of slow-roll inflationary models. We find that while the Bayesian evidence supports the conclusion that n{sub S}{ne}1, the data are not yet powerful enough to do so at a strong or decisive level. If tensors are assumed absent, the current odds are approximately 8 to 1 in favor of n{sub S}{ne}1 under our assumptions, when WMAP3 data is used together with external data sets. WMAP3 data on its own is unable to distinguish between the two models. Further, inclusion of r as a parameter weakens the conclusion against the Harrison-Zel'dovich case (n{sub S}=1, r=0), albeit in a prior-dependent way. In appendices we describe the CosmoNest code in detail, noting its ability to supply posterior samples as well as to accurately compute the Bayesian evidence. We make a first public release of CosmoNest, now available at www.cosmonest.org.
Selecting a model of supersymmetry breaking mediation
AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.
2009-08-01
We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the {mu} parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.
NASA Astrophysics Data System (ADS)
Olbert, Kai; Meier, Thomas; Cristiano, Luigia
2015-04-01
A quick picking procedure is an important tool to process large datasets in seismology. Identifying phases and determining the precise onset times at seismological stations is essential not just for localization procedures but also for seismic body-wave tomography. The automated picking procedure should be fast, robust, precise and consistent. In manual processing the speed and consistency are not guaranteed and therefore unreproducible errors may be introduced, especially for large amounts of data. In this work an offline P- and S-phase picker based on an autoregressive-prediction approach is optimized and applied to different data sets. The onset time can be described as the sum of the event source time, the theoretic travel time according to a reference velocity model and a deviation from the theoretic travel time due to lateral heterogeneity or errors in the source location. With this approach the onset time at each station can be found around the theoretical travel time within a time window smaller than the maximum lateral heterogeneity. Around the theoretic travel time an autoregressive prediction error is calculated from one or several components as characteristic function of the waveform. The minimum of the Akaike-Information-Criteria of the characteristic function identifies the phase. As was shown by Küperkoch et al. (2012), the Akaike-Information-Criteria has the tendency to be too late. Therefore, an additional processing step for precise picking is needed. In the vicinity of the minimum of the Akaike-Information-Criteria a cost function is defined and used to find the optimal estimate of the arrival time. The cost function is composed of the CF and three side conditions. The idea behind the use of a cost function is to find the phase pick in the last minimum before the CF rises due to the phase onset. The final onset time is picked in the minimum of the cost function. The automatic picking procedure is applied on datasets recorded at stations of the
NASA Astrophysics Data System (ADS)
Strupczewski, Witold G.; Bogdanowich, Ewa; Debele, Sisay
2016-04-01
Under Polish climate conditions the series of Annual Maxima (AM) flows are usually a mixture of peak flows of thaw- and rainfall- originated floods. The northern, lowland regions are dominated by snowmelt floods whilst in mountainous regions the proportion of rainfall floods is predominant. In many stations the majority of AM can be of snowmelt origin, but the greatest peak flows come from rainfall floods or vice versa. In a warming climate, precipitation is less likely to occur as snowfall. A shift from a snow- towards a rain-dominated regime results in a decreasing trend in mean and standard deviations of winter peak flows whilst rainfall floods do not exhibit any trace of non-stationarity. That is why a simple form of trends (i.e. linear trends) are more difficult to identify in AM time-series than in Seasonal Maxima (SM), usually winter season time-series. Hence it is recommended to analyse trends in SM, where a trend in standard deviation strongly influences the time -dependent upper quantiles. The uncertainty associated with the extrapolation of the trend makes it necessary to apply a relationship for trend which has time derivative tending to zero, e.g. we can assume a new climate equilibrium epoch approaching, or a time horizon is limited by the validity of the trend model. For both winter and summer SM time series, at least three distributions functions with trend model in the location, scale and shape parameters are estimated by means of the GAMLSS package using the ML-techniques. The resulting trend estimates in mean and standard deviation are mutually compared to the observed trends. Then, using AIC measures as weights, a multi-model distribution is constructed for each of two seasons separately. Further, assuming a mutual independence of the seasonal maxima, an AM model with time-dependent parameters can be obtained. The use of a multi-model approach can alleviate the effects of different and often contradictory trends obtained by using and identifying
Selective experimental review of the Standard Model
Bloom, E.D.
1985-02-01
Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are ..cap alpha../sub s/, ..cap alpha../sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, M..mu.., M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta/sub 1/, theta/sub 2/, theta/sub 3/, and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant ..cap alpha../sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring ..cap alpha../sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures.
42 CFR 425.600 - Selection of risk model.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 42 Public Health 3 2012-10-01 2012-10-01 false Selection of risk model. 425.600 Section 425.600... Selection of risk model. (a) For its initial agreement period, an ACO may elect to operate under one of the following tracks: (1) Track 1. Under Track 1, the ACO operates under the one-sided model (as described...
42 CFR 425.600 - Selection of risk model.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 42 Public Health 3 2013-10-01 2013-10-01 false Selection of risk model. 425.600 Section 425.600... Selection of risk model. (a) For its initial agreement period, an ACO may elect to operate under one of the following tracks: (1) Track 1. Under Track 1, the ACO operates under the one-sided model (as described...
42 CFR 425.600 - Selection of risk model.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 42 Public Health 3 2014-10-01 2014-10-01 false Selection of risk model. 425.600 Section 425.600... Selection of risk model. (a) For its initial agreement period, an ACO may elect to operate under one of the following tracks: (1) Track 1. Under Track 1, the ACO operates under the one-sided model (as described...
Selection of Instructional Materials. A Model Policy and Rules.
ERIC Educational Resources Information Center
Bartlett, Larry D.; And Others
This model prepared by the State of Iowa Department of Public Instruction is intended to provide assistance to schools in developing their own policy and procedures for the selection of library media and text materials. A brief model statement of policy is followed by a model statement of rules which includes (1) responsibility for selection of…
Cognitive Niches: An Ecological Model of Strategy Selection
ERIC Educational Resources Information Center
Marewski, Julian N.; Schooler, Lael J.
2011-01-01
How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each…
HABITAT MODELING APPROACHES FOR RESTORATION SITE SELECTION
Numerous modeling approaches have been used to develop predictive models of species-environment and species-habitat relationships. These models have been used in conservation biology and habitat or species management, but their application to restoration efforts has been minimal...
Patterns of Neutral Diversity Under General Models of Selective Sweeps
Coop, Graham; Ralph, Peter
2012-01-01
Two major sources of stochasticity in the dynamics of neutral alleles result from resampling of finite populations (genetic drift) and the random genetic background of nearby selected alleles on which the neutral alleles are found (linked selection). There is now good evidence that linked selection plays an important role in shaping polymorphism levels in a number of species. One of the best-investigated models of linked selection is the recurrent full-sweep model, in which newly arisen selected alleles fix rapidly. However, the bulk of selected alleles that sweep into the population may not be destined for rapid fixation. Here we develop a general model of recurrent selective sweeps in a coalescent framework, one that generalizes the recurrent full-sweep model to the case where selected alleles do not sweep to fixation. We show that in a large population, only the initial rapid increase of a selected allele affects the genealogy at partially linked sites, which under fairly general assumptions are unaffected by the subsequent fate of the selected allele. We also apply the theory to a simple model to investigate the impact of recurrent partial sweeps on levels of neutral diversity and find that for a given reduction in diversity, the impact of recurrent partial sweeps on the frequency spectrum at neutral sites is determined primarily by the frequencies rapidly achieved by the selected alleles. Consequently, recurrent sweeps of selected alleles to low frequencies can have a profound effect on levels of diversity but can leave the frequency spectrum relatively unperturbed. In fact, the limiting coalescent model under a high rate of sweeps to low frequency is identical to the standard neutral model. The general model of selective sweeps we describe goes some way toward providing a more flexible framework to describe genomic patterns of diversity than is currently available. PMID:22714413
Selection of Temporal Lags When Modeling Economic and Financial Processes.
Matilla-Garcia, Mariano; Ojeda, Rina B; Marin, Manuel Ruiz
2016-10-01
This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models. PMID:27550703
Model Selection for Monitoring CO2 Plume during Sequestration
2014-12-31
The model selection method developed as part of this project mainly includes four steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models, (2) model clustering using multidimensional scaling coupled with k-mean clustering, (3) model selection using the Bayes' rule in the reduced model space, (4) model expansion using iterative resampling of the posterior models. The fourth step expresses one of the advantages of the method: it provides a built-in means of quantifying the uncertainty in predictions made with the selected models. In our application to plume monitoring, by expanding the posterior space of models, the final ensemble of representations of geological model can be used to assess the uncertainty in predicting the future displacement of the CO2 plume. The software implementation of this approach is attached here.
Model Selection for Monitoring CO2 Plume during Sequestration
Energy Science and Technology Software Center (ESTSC)
2014-12-31
The model selection method developed as part of this project mainly includes four steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models, (2) model clustering using multidimensional scaling coupled with k-mean clustering, (3) model selection using the Bayes' rule in the reduced model space, (4) model expansion using iterative resampling of the posterior models. The fourth step expresses one of the advantages of the method: it provides a built-in means ofmore » quantifying the uncertainty in predictions made with the selected models. In our application to plume monitoring, by expanding the posterior space of models, the final ensemble of representations of geological model can be used to assess the uncertainty in predicting the future displacement of the CO2 plume. The software implementation of this approach is attached here.« less
Use of generalised additive models to categorise continuous variables in clinical prediction
2013-01-01
Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically
Selection of Hydrological Model for Waterborne Release
Blanchard, A.
1999-02-03
The purpose of this report is to evaluate the two available models and determine the appropriate model for use in following waterborne release analyses. Additionally, this report will document the DB and BDB accidents to be used in the future study.
Selection of Hydrological Model for Waterborne Release
Blanchard, A.
1999-04-21
This evaluation will aid in determining the potential impacts of liquid releases to downstream populations on the Savannah River. The purpose of this report is to evaluate the two available models and determine the appropriate model for use in following waterborne release analyses. Additionally, this report will document the Design Basis and Beyond Design Basis accidents to be used in the future study.
The Multilingual Lexicon: Modelling Selection and Control
ERIC Educational Resources Information Center
de Bot, Kees
2004-01-01
In this paper an overview of research on the multilingual lexicon is presented as the basis for a model for processing multiple languages. With respect to specific issues relating to the processing of more than two languages, it is suggested that there is no need to develop a specific model for such multilingual processing, but at the same time we…
On Optimal Input Design and Model Selection for Communication Channels
Li, Yanyan; Djouadi, Seddik M; Olama, Mohammed M
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Astrophysical Model Selection in Gravitational Wave Astronomy
NASA Technical Reports Server (NTRS)
Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.
2012-01-01
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.
Bayesian model selection for LISA pathfinder
NASA Astrophysics Data System (ADS)
Karnesis, Nikolaos; Nofrarias, Miquel; Sopuerta, Carlos F.; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; McNamara, Paul W.; Plagnol, Eric; Vitale, Stefano
2014-03-01
The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment onboard the LPF. These models are used for simulations, but, more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes factor between two competing models. In our analysis, we use three main different methods to estimate it: the reversible jump Markov chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments in which the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored.
Methods for model selection in applied science and engineering.
Field, Richard V., Jr.
2004-10-01
Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be
Pathophysiological Progression Model for Selected Toxicological Endpoints
The existing continuum paradigms are effective models to organize toxicological data associated with endpoints used in human health assessments. A compendium of endpoints characterized along a pathophysiological continuum would serve to: weigh the relative importance of effects o...
Deviance statistics in model fit and selection in ROC studies
NASA Astrophysics Data System (ADS)
Lei, Tianhu; Bae, K. Ty
2013-03-01
A general non-linear regression model-based Bayesian inference approach is used in our ROC (Receiver Operating Characteristics) study. In the sampling of posterior distribution, two prior models - continuous Gaussian and discrete categorical - are used for the scale parameter. How to judge Goodness-of-Fit (GOF) of each model and how to criticize these two models, Deviance statistics and Deviance information criterion (DIC) are adopted to address these problems. Model fit and model selection focus on the adequacy of models. Judging model adequacy is essentially measuring agreement of model and observations. Deviance statistics and DIC provide overall measures on model fit and selection. In order to investigate model fit at each category of observations, we find that the cumulative, exponential contributions from individual observations to Deviance statistics are good estimates of FPF (false positive fraction) and TPF (true positive fraction) on which the ROC curve is based. This finding further leads to a new measure for model fit, called FPF-TPF distance, which is an Euclidean distance defined on FPF-TPF space. It combines both local and global fitting. Deviance statistics and FPFTPF distance are shown to be consistent and in good agreement. Theoretical derivation and numerical simulations for this new method for model fit and model selection of ROC data analysis are included. Keywords: General non-linear regression model, Bayesian Inference, Markov Chain Monte Carlo (MCMC) method, Goodness-of-Fit (GOF), Model selection, Deviance statistics, Deviance information criterion (DIC), Continuous conjugate prior, Discrete categorical prior. ∗
Python Program to Select HII Region Models
NASA Astrophysics Data System (ADS)
Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.
2016-01-01
HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.
Gerretzen, Jan; Szymańska, Ewa; Bart, Jacob; Davies, Antony N; van Manen, Henk-Jan; van den Heuvel, Edwin R; Jansen, Jeroen J; Buydens, Lutgarde M C
2016-09-28
The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of
The Genealogy of Samples in Models with Selection
Neuhauser, C.; Krone, S. M.
1997-01-01
We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models, DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case. PMID:9071604
Modeling Selective Intergranular Oxidation of Binary Alloys
Xu, Zhijie; Li, Dongsheng; Schreiber, Daniel K.; Rosso, Kevin M.; Bruemmer, Stephen M.
2015-01-07
Intergranular attack of alloys under hydrothermal conditions is a complex problem that depends on metal and oxygen transport kinetics via solid-state and channel-like pathways to an advancing oxidation front. Experiments reveal very different rates of intergranular attack and minor element depletion distances ahead of the oxidation front for nickel-based binary alloys depending on the minor element. For example, a significant Cr depletion up to 9 µm ahead of grain boundary crack tips were documented for Ni-5Cr binary alloy, in contrast to relatively moderate Al depletion for Ni-5Al (~100s of nm). We present a mathematical kinetics model that adapts Wagner’s model for thick film growth to intergranular attack of binary alloys. The transport coefficients of elements O, Ni, Cr, and Al in bulk alloys and along grain boundaries were estimated from the literature. For planar surface oxidation, a critical concentration of the minor element can be determined from the model where the oxide of minor element becomes dominant over the major element. This generic model for simple grain boundary oxidation can predict oxidation penetration velocities and minor element depletion distances ahead of the advancing front that are comparable to experimental data. The significant distance of depletion of Cr in Ni-5Cr in contrast to the localized Al depletion in Ni-5Al can be explained by the model due to the combination of the relatively faster diffusion of Cr along the grain boundary and slower diffusion in bulk grains, relative to Al.
Selection of Hydrological Model for Waterborne Release
Blanchard, A.
1999-04-21
Following a request from the States of South Carolina and Georgia, downstream radiological consequences from postulated accidental aqueous releases at the three Savannah River Site nonreactor nuclear facilities will be examined. This evaluation will aid in determining the potential impacts of liquid releases to downstream populations on the Savannah River. The purpose of this report is to evaluate the two available models and determine the appropriate model for use in following waterborne release analyses. Additionally, this report will document the accidents to be used in the future study.
Modeling HIV-1 Drug Resistance as Episodic Directional Selection
Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L.; Scheffler, Konrad
2012-01-01
The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance. PMID:22589711
Modeling selective intergranular oxidation of binary alloys.
Xu, Zhijie; Li, Dongsheng; Schreiber, Daniel K; Rosso, Kevin M; Bruemmer, Stephen M
2015-01-01
Intergranular attack of alloys under hydrothermal conditions is a complex problem that depends on metal and oxygen transport kinetics via solid-state and channel-like pathways to an advancing oxidation front. Experiments reveal very different rates of intergranular attack and minor element depletion distances ahead of the oxidation front for nickel-based binary alloys depending on the minor element. For example, a significant Cr depletion up to 9 μm ahead of grain boundary crack tips was documented for Ni-5Cr binary alloy, in contrast to relatively moderate Al depletion for Ni-5Al (∼100 s of nm). We present a mathematical kinetics model that adapts Wagner's model for thick film growth to intergranular attack of binary alloys. The transport coefficients of elements O, Ni, Cr, and Al in bulk alloys and along grain boundaries were estimated from the literature. For planar surface oxidation, a critical concentration of the minor element can be determined from the model where the oxide of minor element becomes dominant over the major element. This generic model for simple grain boundary oxidation can predict oxidation penetration velocities and minor element depletion distances ahead of the advancing front that are comparable to experimental data. The significant distance of depletion of Cr in Ni-5Cr in contrast to the localized Al depletion in Ni-5Al can be explained by the model due to the combination of the relatively faster diffusion of Cr along the grain boundary and slower diffusion in bulk grains, relative to Al. PMID:25573575
Golbon, Reza; Ogutu, Joseph Ochieng; Cotter, Marc; Sauerborn, Joachim
2015-12-01
Linear mixed models were developed and used to predict rubber (Hevea brasiliensis) yield based on meteorological conditions to which rubber trees had been exposed for periods ranging from 1 day to 2 months prior to tapping events. Predictors included a range of moving averages of meteorological covariates spanning different windows of time before the date of the tapping events. Serial autocorrelation in the latex yield measurements was accounted for using random effects and a spatial generalization of the autoregressive error covariance structure suited to data sampled at irregular time intervals. Information theoretics, specifically the Akaike information criterion (AIC), AIC corrected for small sample size (AICc), and Akaike weights, was used to select models with the greatest strength of support in the data from a set of competing candidate models. The predictive performance of the selected best model was evaluated using both leave-one-out cross-validation (LOOCV) and an independent test set. Moving averages of precipitation, minimum and maximum temperature, and maximum relative humidity with a 30-day lead period were identified as the best yield predictors. Prediction accuracy expressed in terms of the percentage of predictions within a measurement error of 5 g for cross-validation and also for the test dataset was above 99 %. PMID:25824122
NASA Astrophysics Data System (ADS)
Golbon, Reza; Ogutu, Joseph Ochieng; Cotter, Marc; Sauerborn, Joachim
2015-12-01
Linear mixed models were developed and used to predict rubber ( Hevea brasiliensis) yield based on meteorological conditions to which rubber trees had been exposed for periods ranging from 1 day to 2 months prior to tapping events. Predictors included a range of moving averages of meteorological covariates spanning different windows of time before the date of the tapping events. Serial autocorrelation in the latex yield measurements was accounted for using random effects and a spatial generalization of the autoregressive error covariance structure suited to data sampled at irregular time intervals. Information theoretics, specifically the Akaike information criterion (AIC), AIC corrected for small sample size (AICc), and Akaike weights, was used to select models with the greatest strength of support in the data from a set of competing candidate models. The predictive performance of the selected best model was evaluated using both leave-one-out cross-validation (LOOCV) and an independent test set. Moving averages of precipitation, minimum and maximum temperature, and maximum relative humidity with a 30-day lead period were identified as the best yield predictors. Prediction accuracy expressed in terms of the percentage of predictions within a measurement error of 5 g for cross-validation and also for the test dataset was above 99 %.
Remedial action selection using groundwater modeling
Haddad, B.I.; Parish, G.B.; Hauge, L.
1996-12-31
An environmental investigation uncovered petroleum contamination at a gasoline station in southern Wisconsin. The site was located in part of the ancestral Rock River valley in Rock County, Wisconsin where the valley is filled with sands and gravels. Groundwater pump tests were conducted for determination of aquifer properties needed to plan a remediation system; the results were indicative of a very high hydraulic conductivity. The site hydrogeology was modeled using the U.S. Geological Survey`s groundwater model, Modflow. The calibrated model was used to determine the number, pumping rate, and configuration of recovery wells to remediate the site. The most effective configuration was three wells pumping at 303 liters per minute (1/m) (80 gallons per minute (gpm)), producing a total pumping rate of 908 l/m (240 gpm). Treating 908 l/min (240 gpm) or 1,308,240 liters per day (345,600 gallons per day) constituted a significant volume to be treated and discharged. It was estimated that pumping for the two year remediation would cost $375,000 while the air sparging would cost $200,000. The recommended remedial system consisted of eight air sparging wells and four vapor recovery laterals. The Wisconsin Department of Natural Resources (WDNR) approved the remedial action plan in March, 1993. After 11 months of effective operation the concentrations of removed VOCs had decreased by 94 percent and groundwater sampling indicated no detectable concentrations of gasoline contaminants. Groundwater modeling was an effective technique to determine the economic feasibility of a groundwater remedial alternative.
flankr: An R package implementing computational models of attentional selectivity.
Grange, James A
2016-06-01
The Eriksen flanker task (Eriksen and Eriksen, Perception & Psychophysics, 16, 143-149, 1974) is a classic test in cognitive psychology of visual selective attention. Two recent computational models have formalised the dynamics of the apparent increasing attentional selectivity during stimulus processing, but with very different theoretical underpinnings: The shrinking spotlight (SSP) model (White et al., Cognitive Psychology, 210-238, 2011) assumes attentional selectivity improves in a gradual, continuous manner; the dual stage two phase (DSTP) model (Hübner et al., Psychological Review, 759-784, 2010) assumes attentional selectivity changes from a low- to a high-mode of selectivity at a discrete time-point. This paper presents an R package-flankr-that instantiates both computational models. flankr allows the user to simulate data from both models, and to fit each model to human data. flankr provides statistics of the goodness-of-fit to human data, allowing users to engage in competitive model comparison of the DSTP and the SSP models on their own data. It is hoped that the utility of flankr lies in allowing more researchers to engage in the important issue of the dynamics of attentional selectivity. PMID:26174713
Selecting Research Collections for Digitization: Applying the Harvard Model.
ERIC Educational Resources Information Center
Brancolini, Kristine R.
2000-01-01
Librarians at Harvard University have written the most comprehensive guide to selecting research collections for digitization. This article applies the Harvard Model to a digitization project at Indiana University in order to evaluate the appropriateness of the model for use at another institution and to adapt the model to local needs. (Contains 7…
An Evaluation of Some Models for Culture-Fair Selection.
ERIC Educational Resources Information Center
Petersen, Nancy S.; Novick, Melvin R.
Models proposed by Cleary, Thorndike, Cole, Linn, Einhorn and Bass, Darlington, and Gross and Su for analyzing bias in the use of tests in a selection strategy are surveyed. Several additional models are also introduced. The purpose is to describe, compare, contrast, and evaluate these models while extracting such useful ideas as may be found in…
A Model for Investigating Predictive Validity at Highly Selective Institutions.
ERIC Educational Resources Information Center
Gross, Alan L.; And Others
A statistical model for investigating predictive validity at highly selective institutions is described. When the selection ratio is small, one must typically deal with a data set containing relatively large amounts of missing data on both criterion and predictor variables. Standard statistical approaches are based on the strong assumption that…
A Conditional Logit Model of Collegiate Major Selection.
ERIC Educational Resources Information Center
Milley, Donald J.; Bee, Richard H.
1982-01-01
Hypothesizes a conditional logit model of decision making to explain collegiate major selection. Results suggest a link between student environment and preference structure and preference structures and student major selection. Suggests findings are limited by use of a largely commuter student population. (KMF)
Augmented Self-Modeling as an Intervention for Selective Mutism
ERIC Educational Resources Information Center
Kehle, Thomas J.; Bray, Melissa A.; Byer-Alcorace, Gabriel F.; Theodore, Lea A.; Kovac, Lisa M.
2012-01-01
Selective mutism is a rare disorder that is difficult to treat. It is often associated with oppositional defiant behavior, particularly in the home setting, social phobia, and, at times, autism spectrum disorder characteristics. The augmented self-modeling treatment has been relatively successful in promoting rapid diminishment of selective mutism…
A Working Model of Natural Selection Illustrated by Table Tennis
ERIC Educational Resources Information Center
Dinc, Muhittin; Kilic, Selda; Aladag, Caner
2013-01-01
Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…
Determinants of wood thrush nest success: A multi-scale, model selection approach
Driscoll, M.J.L.; Donovan, T.; Mickey, R.; Howard, A.; Fleming, K.K.
2005-01-01
We collected data on 212 wood thrush (Hylocichla mustelina) nests in central New York from 1998 to 2000 to determine the factors that most strongly influence nest success. We used an information-theoretic approach to assess and rank 9 models that examined the relationship between nest success (i.e., the probability that a nest would successfully fledge at least 1 wood thrush offspring) and habitat conditions at different spatial scales. We found that 4 variables were significant predictors of nesting success for wood thrushes: (1) total core habitat within 5 km of a study site, (2) distance to forest-field edge, (3) total forest cover within 5 km of the study site, and (4) density and variation in diameter of trees and shrubs surrounding the nest. The coefficients of these predictors were all positive. Of the 9 models evaluated, amount of core habitat in the 5-km landscape was the best-fit model, but the vegetation structure model (i.e., the density of trees and stems surrounding a nest) was also supported by the data. Based on AIC weights, enhancement of core area is likely to be a more effective management option than any other habitat-management options explored in this study. Bootstrap analysis generally confirmed these results; core and vegetation structure models were ranked 1, 2, or 3 in over 50% of 1,000 bootstrap trials. However, bootstrap results did not point to a decisive model, which suggests that multiple habitat factors are influencing wood thrush nesting success. Due to model uncertainty, we used a model averaging approach to predict the success or failure of each nest in our dataset. This averaged model was able to correctly predict 61.1% of nest outcomes.
Ecohydrological model parameter selection for stream health evaluation.
Woznicki, Sean A; Nejadhashemi, A Pouyan; Ross, Dennis M; Zhang, Zhen; Wang, Lizhu; Esfahanian, Abdol-Hossein
2015-04-01
Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1-3 and orders 4-6), k-means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k-means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our
Forecasting Tuberculosis Incidence in Iran Using Box-Jenkins Models
Moosazadeh, Mahmood; Nasehi, Mahshid; Bahrampour, Abbas; Khanjani, Narges; Sharafi, Saeed; Ahmadi, Shanaz
2014-01-01
Background: Predicting the incidence of tuberculosis (TB) plays an important role in planning health control strategies for the future, developing intervention programs and allocating resources. Objectives: The present longitudinal study estimated the incidence of tuberculosis in 2014 using Box-Jenkins methods. Materials and Methods: Monthly data of tuberculosis cases recorded in the surveillance system of Iran tuberculosis control program from 2005 till 2011 was used. Data was reviewed regarding normality, variance equality and stationary conditions. The parameters p, d and q and P, D and Q were determined, and different models were examined. Based on the lowest levels of AIC and BIC, the most suitable model was selected among the models whose overall adequacy was confirmed. Results: During 84 months, 63568 TB patients were recorded. The average was 756.8 (SD = 11.9) TB cases a month. SARIMA (0,1,1)(0,1,1)12 with the lowest level of AIC (12.78) was selected as the most adequate model for prediction. It was predicted that the total nationwide TB cases for 2014 will be about 16.75 per 100,000 people. Conclusions: Regarding the cyclic pattern of TB recorded cases, Box-Jenkins and SARIMA models are suitable for predicting its prevalence in future. Moreover, prediction results show an increasing trend of TB cases in Iran. PMID:25031852
Development, Selection, and Validation of Tumor Growth Models
NASA Astrophysics Data System (ADS)
Shahmoradi, Amir; Lima, Ernesto; Oden, J. Tinsley
In recent years, a multitude of different mathematical approaches have been taken to develop multiscale models of solid tumor growth. Prime successful examples include the lattice-based, agent-based (off-lattice), and phase-field approaches, or a hybrid of these models applied to multiple scales of tumor, from subcellular to tissue level. Of overriding importance is the predictive power of these models, particularly in the presence of uncertainties. This presentation describes our attempt at developing lattice-based, agent-based and phase-field models of tumor growth and assessing their predictive power through new adaptive algorithms for model selection and model validation embodied in the Occam Plausibility Algorithm (OPAL), that brings together model calibration, determination of sensitivities of outputs to parameter variances, and calculation of model plausibilities for model selection. Institute for Computational Engineering and Sciences.
Robust Decision-making Applied to Model Selection
Hemez, Francois M.
2012-08-06
The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.
A guide to Bayesian model selection for ecologists
Hooten, Mevin B.; Hobbs, N.T.
2015-01-01
The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
Selected aspects of modelling monetary transmission mechanism by BVAR model
NASA Astrophysics Data System (ADS)
Vaněk, Tomáš; Dobešová, Anna; Hampel, David
2013-10-01
In this paper we use the BVAR model with the specifically defined prior to evaluate data including high-lag dependencies. The results are compared to both restricted and common VAR model. The data depicts the monetary transmission mechanism in the Czech Republic and Slovakia from January 2002 to February 2013. The results point to the inadequacy of the common VAR model. The restricted VAR model and the BVAR model appear to be similar in the sense of impulse responses.
Multicriteria framework for selecting a process modelling language
NASA Astrophysics Data System (ADS)
Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel
2016-01-01
The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.
Monthly streamflow prediction in the Volta Basin of West Africa: A SISO NARMAX polynomial modelling
NASA Astrophysics Data System (ADS)
Amisigo, B. A.; van de Giesen, N.; Rogers, C.; Andah, W. E. I.; Friesen, J.
Single-input-single-output (SISO) non-linear system identification techniques were employed to model monthly catchment runoff at selected gauging sites in the Volta Basin of West Africa. NARMAX (Non-linear Autoregressive Moving Average with eXogenous Input) polynomial models were fitted to basin monthly rainfall and gauging station runoff data for each of the selected sites and used to predict monthly runoff at the sites. An error reduction ratio (ERR) algorithm was used to order regressors for various combinations of input, output and noise lags (various model structures) and the significant regressors for each model selected by applying an Akaike Information Criterion (AIC) to independent rainfall-runoff validation series. Model parameters were estimated from the Matlab REGRESS function (an orthogonal least squares method). In each case, the sub-model without noise terms was fitted first followed by a fitting of the noise model. The coefficient of determination ( R-squared), the Nash-Sutcliffe Efficiency criterion (NSE) and the F statistic for the estimation (training) series were used to evaluate the significance of fit of each model to this series while model selection from the range of models fitted for each gauging site was done by examining the NSEs and the AICs of the validation series. Monthly runoff predictions from the selected models were very good, and the polynomial models appeared to have captured a good part of the rainfall-runoff non-linearity. The results indicate that the NARMAX modelling framework is suitable for monthly river runoff prediction in the Volta Basin. The several good models made available by the NARMAX modelling framework could be useful in the selection of model structures that also provide insights into the physical behaviour of the catchment rainfall-runoff system.
Modeling Selection and Extinction Mechanisms of Biological Systems
NASA Astrophysics Data System (ADS)
Amirjanov, Adil
In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.
Modeling quality attributes and metrics for web service selection
NASA Astrophysics Data System (ADS)
Oskooei, Meysam Ahmadi; Daud, Salwani binti Mohd; Chua, Fang-Fang
2014-06-01
Since the service-oriented architecture (SOA) has been designed to develop the system as a distributed application, the service selection has become a vital aspect of service-oriented computing (SOC). Selecting the appropriate web service with respect to quality of service (QoS) through using mathematical solution for optimization of problem turns the service selection problem into a common concern for service users. Nowadays, number of web services that provide the same functionality is increased and selection of services from a set of alternatives which differ in quality parameters can be difficult for service consumers. In this paper, a new model for QoS attributes and metrics is proposed to provide a suitable solution for optimizing web service selection and composition with low complexity.
IT vendor selection model by using structural equation model & analytical hierarchy process
NASA Astrophysics Data System (ADS)
Maitra, Sarit; Dominic, P. D. D.
2012-11-01
Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.
Dynamic selection of models for a ventilator-management advisor.
Rutledge, G. W.
1993-01-01
A ventilator-management advisor (VMA) is a computer program that monitors patients who are treated with a mechanical ventilator. A VMA implements a patient-specific physiologic model to interpret patient data and to predict the effects of alternative control settings for the ventilator. Because a VMA evaluates its physiologic model repeatedly during each cycle of data interpretation, highly complex models may require more computation time than is available in this time-critical application. On the other hand, less complex models may be inaccurate if they are unable to represent a patient's physiologic abnormalities. For each patient, a VMA should select a model that balances the tradeoff of prediction accuracy and computation-time complexity. I present a method to select models that are at an appropriate level of detail for time-constrained decision tasks. The method is based on a local search in a graph of models (GoM) for a model that maximizes the tradeoff of computation-time complexity and prediction accuracy. For each model under consideration, a belief network computes a probability of model adequacy given the qualitative prior information, and the goodness of fit of the model to the data provides a measure of the conditional probability of adequacy given the quantitative observations. I apply this method to the problem of model selection for a VMA. I describe an implementation of a graph of physiologic models that range in complexity from VentPlan, a simple model with 3 compartments, to VentSim, a multicompartment model with detailed airway, circulation and mechanical ventilator components.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8130492
Model Selection in Historical Research Using Approximate Bayesian Computation
Rubio-Campillo, Xavier
2016-01-01
Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. Case Study This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester’s laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Impact Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence. PMID:26730953
Robust model selection and the statistical classification of languages
NASA Astrophysics Data System (ADS)
García, J. E.; González-López, V. A.; Viola, M. L. L.
2012-10-01
In this paper we address the problem of model selection for the set of finite memory stochastic processes with finite alphabet, when the data is contaminated. We consider m independent samples, with more than half of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We devise a model selection procedure such that for a sample size large enough, the selected process is the one with law Q. Our model selection strategy is based on estimating relative entropies to select a subset of samples that are realizations of the same law. Although the procedure is valid for any family of finite order Markov models, we will focus on the family of variable length Markov chain models, which include the fixed order Markov chain model family. We define the asymptotic breakdown point (ABDP) for a model selection procedure, and we show the ABDP for our procedure. This means that if the proportion of contaminated samples is smaller than the ABDP, then, as the sample size grows our procedure selects a model for the process with law Q. We also use our procedure in a setting where we have one sample conformed by the concatenation of sub-samples of two or more stochastic processes, with most of the subsamples having law Q. We conducted a simulation study. In the application section we address the question of the statistical classification of languages according to their rhythmic features using speech samples. This is an important open problem in phonology. A persistent difficulty on this problem is that the speech samples correspond to several sentences produced by diverse speakers, corresponding to a mixture of distributions. The usual procedure to deal with this problem has been to choose a subset of the original sample which seems to best represent each language. The selection is made by listening to the samples. In our application we use the full dataset without any preselection of samples. We apply our robust methodology estimating
Bayesian Nonlinear Model Selection for Gene Regulatory Networks
Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran
2015-01-01
Summary Gene regulatory networks represent the regulatory relationships between genes and their products and are important for exploring and defining the underlying biological processes of cellular systems. We develop a novel framework to recover the structure of nonlinear gene regulatory networks using semiparametric spline-based directed acyclic graphical models. Our use of splines allows the model to have both flexibility in capturing nonlinear dependencies as well as control of overfitting via shrinkage, using mixed model representations of penalized splines. We propose a novel discrete mixture prior on the smoothing parameter of the splines that allows for simultaneous selection of both linear and nonlinear functional relationships as well as inducing sparsity in the edge selection. Using simulation studies, we demonstrate the superior performance of our methods in comparison with several existing approaches in terms of network reconstruction and functional selection. We apply our methods to a gene expression dataset in glioblastoma multiforme, which reveals several interesting and biologically relevant nonlinear relationships. PMID:25854759
Ternès, Nils; Rotolo, Federico; Michiels, Stefan
2016-07-10
Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26970107
Uncertain programming models for portfolio selection with uncertain returns
NASA Astrophysics Data System (ADS)
Zhang, Bo; Peng, Jin; Li, Shengguo
2015-10-01
In an indeterminacy economic environment, experts' knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.
The E-MS Algorithm: Model Selection with Incomplete Data
Jiang, Jiming; Nguyen, Thuan; Rao, J. Sunil
2014-01-01
We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al. 2008). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains. PMID:26783375
Fixation probability in a two-locus intersexual selection model.
Durand, Guillermo; Lessard, Sabin
2016-06-01
We study a two-locus model of intersexual selection in a finite haploid population reproducing according to a discrete-time Moran model with a trait locus expressed in males and a preference locus expressed in females. We show that the probability of ultimate fixation of a single mutant allele for a male ornament introduced at random at the trait locus given any initial frequency state at the preference locus is increased by weak intersexual selection and recombination, weak or strong. Moreover, this probability exceeds the initial frequency of the mutant allele even in the case of a costly male ornament if intersexual selection is not too weak. On the other hand, the probability of ultimate fixation of a single mutant allele for a female preference towards a male ornament introduced at random at the preference locus is increased by weak intersexual selection and weak recombination if the female preference is not costly, and is strong enough in the case of a costly male ornament. The analysis relies on an extension of the ancestral recombination-selection graph for samples of haplotypes to take into account events of intersexual selection, while the symbolic calculation of the fixation probabilities is made possible in a reasonable time by an optimizing algorithm. PMID:27059474
Variable selection in strong hierarchical semiparametric models for longitudinal data
Zeng, Xianbin; Ma, Shuangge; Qin, Yichen; Li, Yang
2015-01-01
In this paper, we consider the variable selection problem in semiparametric additive partially linear models for longitudinal data. Our goal is to identify relevant main effects and corresponding interactions associated with the response variable. Meanwhile, we enforce the strong hierarchical restriction on the model, that is, an interaction can be included in the model only if both the associated main effects are included. Based on B-splines basis approximation for the nonparametric components, we propose an iterative estimation procedure for the model by penalizing the likelihood with a partial group minimax concave penalty (MCP), and use BIC to select the tuning parameter. To further improve the estimation efficiency, we specify the working covariance matrix by maximum likelihood estimation. Simulation studies indicate that the proposed method tends to consistently select the true model and works efficiently in estimation and prediction with finite samples, especially when the true model obeys the strong hierarchy. Finally, the China Stock Market data are fitted with the proposed model to illustrate its effectiveness. PMID:27076867
A model of selective masking in chromatic detection.
Shepard, Timothy G; Swanson, Emily A; McCarthy, Comfrey L; Eskew, Rhea T
2016-07-01
Narrowly tuned, selective noise masking of chromatic detection has been taken as evidence for the existence of a large number of color mechanisms (i.e., higher order color mechanisms). Here we replicate earlier observations of selective masking of tests in the (L,M) plane of cone space when the noise is placed near the corners of the detection contour. We used unipolar Gaussian blob tests with three different noise color directions, and we show that there are substantial asymmetries in the detection contours-asymmetries that would have been missed with bipolar tests such as Gabor patches. We develop a new chromatic detection model, which is based on probability summation of linear cone combinations, and incorporates a linear contrast energy versus noise power relationship that predicts how the sensitivity of these mechanisms changes with noise contrast and chromaticity. With only six unipolar color mechanisms (the same number as the cardinal model), the new model accounts for the threshold contours across the different noise conditions, including the asymmetries and the selective effects of the noises. The key for producing selective noise masking in the (L,M) plane is having more than two mechanisms with opposed L- and M-cone inputs, in which case selective masking can be produced without large numbers of color mechanisms. PMID:27442723
Model systems, taxonomic bias, and sexual selection: beyond Drosophila.
Zuk, Marlene; Garcia-Gonzalez, Francisco; Herberstein, Marie Elisabeth; Simmons, Leigh W
2014-01-01
Although model systems are useful in entomology, allowing generalizations based on a few well-known species, they also have drawbacks. It can be difficult to know how far to generalize from information in a few species: Are all flies like Drosophila? The use of model systems is particularly problematic in studying sexual selection, where variability among taxa is key to the evolution of different behaviors. A bias toward the use of a few insect species, particularly from the genus Drosophila, is evident in the sexual selection and sexual conflict literature over the past several decades, although the diversity of study organisms has increased more recently. As the number of model systems used to study sexual conflict increased, support for the idea that sexual interactions resulted in harm to females decreased. Future work should choose model systems thoughtfully, combining well-known species with those that can add to the variation that allows us to make more meaningful generalizations. PMID:24160422
Model-based sensor location selection for helicopter gearbox monitoring
NASA Technical Reports Server (NTRS)
Jammu, Vinay B.; Wang, Keming; Danai, Kourosh; Lewicki, David G.
1996-01-01
A new methodology is introduced to quantify the significance of accelerometer locations for fault diagnosis of helicopter gearboxes. The basis for this methodology is an influence model which represents the effect of various component faults on accelerometer readings. Based on this model, a set of selection indices are defined to characterize the diagnosability of each component, the coverage of each accelerometer, and the relative redundancy between the accelerometers. The effectiveness of these indices is evaluated experimentally by measurement-fault data obtained from an OH-58A main rotor gearbox. These data are used to obtain a ranking of individual accelerometers according to their significance in diagnosis. Comparison between the experimentally obtained rankings and those obtained from the selection indices indicates that the proposed methodology offers a systematic means for accelerometer location selection.
Towards a Personalized Task Selection Model with Shared Instructional Control
ERIC Educational Resources Information Center
Corbalan, Gemma; Kester, Liesbeth; Van Merrienboer, Jeroen J. G.
2006-01-01
Modern education emphasizes the need to flexibly personalize learning tasks to individual learners. This article discusses a personalized task-selection model with shared instructional control based on two current tendencies for the dynamic sequencing of learning tasks: (1) personalization by an instructional agent which makes sequencing decisions…
Measures and limits of models of fixation selection.
Wilming, Niklas; Betz, Torsten; Kietzmann, Tim C; König, Peter
2011-01-01
Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection. We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced. PMID:21931638
Selecting best-fit models for estimating the body mass from 3D data of the human calcaneus.
Jung, Go-Un; Lee, U-Young; Kim, Dong-Ho; Kwak, Dai-Soon; Ahn, Yong-Woo; Han, Seung-Ho; Kim, Yi-Suk
2016-05-01
Body mass (BM) estimation could facilitate the interpretation of skeletal materials in terms of the individual's body size and physique in forensic anthropology. However, few metric studies have tried to estimate BM by focusing on prominent biomechanical properties of the calcaneus. The purpose of this study was to prepare best-fit models for estimating BM from the 3D human calcaneus by two major linear regression analysis (the heuristic statistical and all-possible-regressions techniques) and validate the models through predicted residual sum of squares (PRESS) statistics. A metric analysis was conducted based on 70 human calcaneus samples (29 males and 41 females) taken from 3D models in the Digital Korean Database and 10 variables were measured for each sample. Three best-fit models were postulated by F-statistics, Mallows' Cp, and Akaike information criterion (AIC) and Bayes information criterion (BIC) for each available candidate models. Finally, the most accurate regression model yields lowest %SEE and 0.843 of R(2). Through the application of leave-one-out cross validation, the predictive power was indicated a high level of validation accuracy. This study also confirms that the equations for estimating BM using 3D models of human calcaneus will be helpful to establish identification in forensic cases with consistent reliability. PMID:26970867
How Many Separable Sources? Model Selection In Independent Components Analysis
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988
Noise Level Estimation for Model Selection in Kernel PCA Denoising.
Varon, Carolina; Alzate, Carlos; Suykens, Johan A K
2015-11-01
One of the main challenges in unsupervised learning is to find suitable values for the model parameters. In kernel principal component analysis (kPCA), for example, these are the number of components, the kernel, and its parameters. This paper presents a model selection criterion based on distance distributions (MDDs). This criterion can be used to find the number of components and the σ(2) parameter of radial basis function kernels by means of spectral comparison between information and noise. The noise content is estimated from the statistical moments of the distribution of distances in the original dataset. This allows for a type of randomization of the dataset, without actually having to permute the data points or generate artificial datasets. After comparing the eigenvalues computed from the estimated noise with the ones from the input dataset, information is retained and maximized by a set of model parameters. In addition to the model selection criterion, this paper proposes a modification to the fixed-size method and uses the incomplete Cholesky factorization, both of which are used to solve kPCA in large-scale applications. These two approaches, together with the model selection MDD, were tested in toy examples and real life applications, and it is shown that they outperform other known algorithms. PMID:25608316
Model selection as a science driver for dark energy surveys
NASA Astrophysics Data System (ADS)
Mukherjee, Pia; Parkinson, David; Corasaniti, Pier Stefano; Liddle, Andrew R.; Kunz, Martin
2006-07-01
A key science goal of upcoming dark energy surveys is to seek time-evolution of the dark energy. This problem is one of model selection, where the aim is to differentiate between cosmological models with different numbers of parameters. However, the power of these surveys is traditionally assessed by estimating their ability to constrain parameters, which is a different statistical problem. In this paper, we use Bayesian model selection techniques, specifically forecasting of the Bayes factors, to compare the abilities of different proposed surveys in discovering dark energy evolution. We consider six experiments - supernova luminosity measurements by the Supernova Legacy Survey, SNAP, JEDI and ALPACA, and baryon acoustic oscillation measurements by WFMOS and JEDI - and use Bayes factor plots to compare their statistical constraining power. The concept of Bayes factor forecasting has much broader applicability than dark energy surveys.
Modeling Selective Availability of the NAVSTAR Global Positioning System
NASA Technical Reports Server (NTRS)
Braasch, Michael
1990-01-01
As the development of the NAVSTAR Global Positioning System (GPS) continues, there will increasingly be the need for a software centered signal model. This model must accurately generate the observed pseudorange which would typically be encountered. The observed pseudorange varies from the true geometric (slant) range due to range measurement errors. Errors in range measurement stem from a variety of hardware and environment factors. These errors are classified as either deterministic or random and, where appropriate, their models are summarized. Of particular interest is the model for Selective Availability which is derived from actual GPS data. The procedure for the determination of this model, known as the System Identification Theory, is briefly outlined. The synthesis of these error sources into the final signal model is given along with simulation results.
Input Variable Selection for Hydrologic Modeling Using Anns
NASA Astrophysics Data System (ADS)
Ganti, R.; Jain, A.
2011-12-01
The use of artificial neural network (ANN) models in water resources applications has grown considerably over the last couple of decades. In learning problems, where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. Hence, in the ANN modeling methodology, one of the approaches that has received little attention, is the selection of appropriate model inputs. In the past, different methods have been used for identifying and eliminating these input variables. Normally, linear methods of Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) have been adopted. For nonlinear physical systems e.g. hydrological systems, model inputs selected based on the linear correlation analysis among input and output variables cannot assure to capture the non-linearity in the system. In the present study, two of the non-linear methods have been explored for the Input Variable Selection (IVS). The linear method employing ACF and PACF is also used for comparison purposes. The first non-linear method utilizes a measure of the Mutual Information Criterion (MIC) to characterize the dependence between a potential model input and the output, which is a step wise input selection procedure. The second non-linear method is improvement over the first method which eliminates redundant inputs based on a partial measure of mutual information criterion (PMIC), which is also a step wise procedure. Further, the number of input variables to be considered for the development of ANN model was determined using the Principal Component Analysis (PCA), which previously used to be done by trial and error approach. The daily river flow data derived from Godavari River Basin @ Polavaram, Andhra Pradesh, India, and the daily average
Discriminative Feature Selection via Multiclass Variable Memory Markov Model
NASA Astrophysics Data System (ADS)
Slonim, Noam; Bejerano, Gill; Fine, Shai; Tishby, Naftali
2003-12-01
We propose a novel feature selection method based on a variable memory Markov (VMM) model. The VMM was originally proposed as a generative model trying to preserve the original source statistics from training data. We extend this technique to simultaneously handle several sources, and further apply a new criterion to prune out nondiscriminative features out of the model. This results in a multiclass discriminative VMM (DVMM), which is highly efficient, scaling linearly with data size. Moreover, we suggest a natural scheme to sort the remaining features based on their discriminative power with respect to the sources at hand. We demonstrate the utility of our method for text and protein classification tasks.
Supplier Selection in Virtual Enterprise Model of Manufacturing Supply Network
NASA Astrophysics Data System (ADS)
Kaihara, Toshiya; Opadiji, Jayeola F.
The market-based approach to manufacturing supply network planning focuses on the competitive attitudes of various enterprises in the network to generate plans that seek to maximize the throughput of the network. It is this competitive behaviour of the member units that we explore in proposing a solution model for a supplier selection problem in convergent manufacturing supply networks. We present a formulation of autonomous units of the network as trading agents in a virtual enterprise network interacting to deliver value to market consumers and discuss the effect of internal and external trading parameters on the selection of suppliers by enterprise units.
Inference for blocked randomization under a selection bias model.
Kennes, Lieven N; Rosenberger, William F; Hilgers, Ralf-Dieter
2015-12-01
We provide an asymptotic test to analyze randomized clinical trials that may be subject to selection bias. For normally distributed responses, and under permuted block randomization, we derive a likelihood ratio test of the treatment effect under a selection bias model. A likelihood ratio test of the presence of selection bias arises from the same formulation. We prove that the test is asymptotically chi-square on one degree of freedom. These results correlate well with the likelihood ratio test of Ivanova et al. (2005, Statistics in Medicine 24, 1537-1546) for binary responses, for which they established by simulation that the asymptotic distribution is chi-square. Simulations also show that the test is robust to departures from normality and under another randomization procedure. We illustrate the test by reanalyzing a clinical trial on retinal detachment. PMID:26099068
Broken selection rule in the quantum Rabi model
Forn-Díaz, P.; Romero, G.; Harmans, C. J. P. M.; Solano, E.; Mooij, J. E.
2016-01-01
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling strength. We report the spectroscopic observation of a resonant transition that breaks a selection rule in the quantum Rabi model, implemented using an LC resonator and an artificial atom, a superconducting qubit. The eigenstates of the system consist of a superposition of bare qubit-resonator states with a relative sign. When the qubit-resonator coupling strength is negligible compared to their own frequencies, the matrix element between excited eigenstates of different sign is very small in presence of a resonator drive, establishing a sign-preserving selection rule. Here, our qubit-resonator system operates in the ultrastrong coupling regime, where the coupling strength is 10% of the resonator frequency, allowing sign-changing transitions to be activated and, therefore, detected. This work shows that sign-changing transitions are an unambiguous, distinctive signature of systems operating in the ultrastrong coupling regime of the quantum Rabi model. These results pave the way to further studies of sign-preserving selection rules in multiqubit and multiphoton models. PMID:27273346
Broken selection rule in the quantum Rabi model
NASA Astrophysics Data System (ADS)
Forn-Díaz, P.; Romero, G.; Harmans, C. J. P. M.; Solano, E.; Mooij, J. E.
2016-06-01
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling strength. We report the spectroscopic observation of a resonant transition that breaks a selection rule in the quantum Rabi model, implemented using an LC resonator and an artificial atom, a superconducting qubit. The eigenstates of the system consist of a superposition of bare qubit-resonator states with a relative sign. When the qubit-resonator coupling strength is negligible compared to their own frequencies, the matrix element between excited eigenstates of different sign is very small in presence of a resonator drive, establishing a sign-preserving selection rule. Here, our qubit-resonator system operates in the ultrastrong coupling regime, where the coupling strength is 10% of the resonator frequency, allowing sign-changing transitions to be activated and, therefore, detected. This work shows that sign-changing transitions are an unambiguous, distinctive signature of systems operating in the ultrastrong coupling regime of the quantum Rabi model. These results pave the way to further studies of sign-preserving selection rules in multiqubit and multiphoton models.
Broken selection rule in the quantum Rabi model.
Forn-Díaz, P; Romero, G; Harmans, C J P M; Solano, E; Mooij, J E
2016-01-01
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling strength. We report the spectroscopic observation of a resonant transition that breaks a selection rule in the quantum Rabi model, implemented using an LC resonator and an artificial atom, a superconducting qubit. The eigenstates of the system consist of a superposition of bare qubit-resonator states with a relative sign. When the qubit-resonator coupling strength is negligible compared to their own frequencies, the matrix element between excited eigenstates of different sign is very small in presence of a resonator drive, establishing a sign-preserving selection rule. Here, our qubit-resonator system operates in the ultrastrong coupling regime, where the coupling strength is 10% of the resonator frequency, allowing sign-changing transitions to be activated and, therefore, detected. This work shows that sign-changing transitions are an unambiguous, distinctive signature of systems operating in the ultrastrong coupling regime of the quantum Rabi model. These results pave the way to further studies of sign-preserving selection rules in multiqubit and multiphoton models. PMID:27273346
Stationary solutions for metapopulation Moran models with mutation and selection.
Constable, George W A; McKane, Alan J
2015-03-01
We construct an individual-based metapopulation model of population genetics featuring migration, mutation, selection, and genetic drift. In the case of a single "island," the model reduces to the Moran model. Using the diffusion approximation and time-scale separation arguments, an effective one-variable description of the model is developed. The effective description bears similarities to the well-mixed Moran model with effective parameters that depend on the network structure and island sizes, and it is amenable to analysis. Predictions from the reduced theory match the results from stochastic simulations across a range of parameters. The nature of the fast-variable elimination technique we adopt is further studied by applying it to a linear system, where it provides a precise description of the slow dynamics in the limit of large time-scale separation. PMID:25871148
Selection of Models for Ingestion Pathway and Relocation Radii Determination
Blanchard, A.
1998-12-17
The distance at which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models were considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities.
Selection of Models for Ingestion Pathway and Relocation
Blanchard, A.; Thompson, J.M.
1998-11-01
The area in which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models are considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities. The most recent Food and Drug Administration Derived Intervention Levels (August 1998) are adopted as evaluation guidelines for ingestion pathways.
Selection of Models for Ingestion Pathway and Relocation
Blanchard, A.; Thompson, J.M.
1999-02-01
The area in which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models are considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities. The most recent Food and Drug Administration Derived Intervention Levels (August 1998) are adopted as evaluation guidelines for ingestion pathways.
Model-based rational strategy for chromatographic resin selection.
Nfor, Beckley K; Zuluaga, Diego S; Verheijen, Peter J T; Verhaert, Peter D E M; van der Wielen, Luuk A M; Ottens, Marcel
2011-01-01
A model-based rational strategy for the selection of chromatographic resins is presented. The main question being addressed is that of selecting the most optimal chromatographic resin from a few promising alternatives. The methodology starts with chromatographic modeling,parameters acquisition, and model validation, followed by model-based optimization of the chromatographic separation for the resins of interest. Finally, the resins are rationally evaluated based on their optimized operating conditions and performance metrics such as product purity, yield, concentration, throughput, productivity, and cost. Resin evaluation proceeds by two main approaches. In the first approach, Pareto frontiers from multi-objective optimization of conflicting objectives are overlaid for different resins, enabling direct visualization and comparison of resin performances based on the feasible solution space. The second approach involves the transformation of the resin performances into weighted resin scores, enabling the simultaneous consideration of multiple performance metrics and the setting of priorities. The proposed model-based resin selection strategy was illustrated by evaluating three mixed mode adsorbents (ADH, PPA, and HEA) for the separation of a ternary mixture of bovine serum albumin, ovalbumin, and amyloglucosidase. In order of decreasing weighted resin score or performance, the top three resins for this separation were ADH [PPA[HEA. The proposed model-based approach could be a suitable alternative to column scouting during process development, the main strengths being that minimal experimentation is required and resins are evaluated under their ideal working conditions, enabling a fair comparison. This work also demonstrates the application of column modeling and optimization to mixed mode chromatography. PMID:22238769
Multiobjective optimization for model selection in kernel methods in regression.
You, Di; Benitez-Quiroz, Carlos Fabian; Martinez, Aleix M
2014-10-01
Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-versus-variance tradeoff. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a tradeoff between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition, and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared with methods in the state of the art. PMID:25291740
Multiobjective Optimization for Model Selection in Kernel Methods in Regression
You, Di; Benitez-Quiroz, C. Fabian; Martinez, Aleix M.
2016-01-01
Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-vs-variance trade-off. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a trade-off between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared to methods in the state of the art. PMID:25291740
Modeling selective pressures on phytoplankton in the global ocean.
Bragg, Jason G; Dutkiewicz, Stephanie; Jahn, Oliver; Follows, Michael J; Chisholm, Sallie W
2010-01-01
Our view of marine microbes is transforming, as culture-independent methods facilitate rapid characterization of microbial diversity. It is difficult to assimilate this information into our understanding of marine microbe ecology and evolution, because their distributions, traits, and genomes are shaped by forces that are complex and dynamic. Here we incorporate diverse forces--physical, biogeochemical, ecological, and mutational--into a global ocean model to study selective pressures on a simple trait in a widely distributed lineage of picophytoplankton: the nitrogen use abilities of Synechococcus and Prochlorococcus cyanobacteria. Some Prochlorococcus ecotypes have lost the ability to use nitrate, whereas their close relatives, marine Synechococcus, typically retain it. We impose mutations for the loss of nitrogen use abilities in modeled picophytoplankton, and ask: in which parts of the ocean are mutants most disadvantaged by losing the ability to use nitrate, and in which parts are they least disadvantaged? Our model predicts that this selective disadvantage is smallest for picophytoplankton that live in tropical regions where Prochlorococcus are abundant in the real ocean. Conversely, the selective disadvantage of losing the ability to use nitrate is larger for modeled picophytoplankton that live at higher latitudes, where Synechococcus are abundant. In regions where we expect Prochlorococcus and Synechococcus populations to cycle seasonally in the real ocean, we find that model ecotypes with seasonal population dynamics similar to Prochlorococcus are less disadvantaged by losing the ability to use nitrate than model ecotypes with seasonal population dynamics similar to Synechococcus. The model predictions for the selective advantage associated with nitrate use are broadly consistent with the distribution of this ability among marine picocyanobacteria, and at finer scales, can provide insights into interactions between temporally varying ocean processes and
The Coalescent Process in Models with Selection and Recombination
Hudson, R. R.; Kaplan, N. L.
1988-01-01
The statistical properties of the process describing the genealogical history of a random sample of genes at a selectively neutral locus which is linked to a locus at which natural selection operates are investigated. It is found that the equations describing this process are simple modifications of the equations describing the process assuming that the two loci are completely linked. Thus, the statistical properties of the genealogical process for a random sample at a neutral locus linked to a locus with selection follow from the results obtained for the selected locus. Sequence data from the alcohol dehydrogenase (Adh) region of Drosophila melanogaster are examined and compared to predictions based on the theory. It is found that the spatial distribution of nucleotide differences between Fast and Slow alleles of Adh is very similar to the spatial distribution predicted if balancing selection operates to maintain the allozyme variation at the Adh locus. The spatial distribution of nucleotide differences between different Slow alleles of Adh do not match the predictions of this simple model very well. PMID:3147214
Space-Time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues.
Hossain, M M; Lawson, A B; Cai, B; Choi, J; Liu, J; Kirby, R S
2014-03-01
With the growing popularity of spatial mixture models in cluster analysis, model selection criteria have become an established tool in the search for parsimony. However, the label-switching problem is often inherent in Bayesian implementation of mixture models and a variety of relabeling algorithms have been proposed. We use a space-time mixture of Poisson regression models with homogeneous covariate effects to illustrate that the best model selected by using model selection criteria does not always support the model that is chosen by the optimal relabeling algorithm. The results are illustrated for real and simulated datasets. The objective is to make the reader aware that if the purpose of statistical modeling is to identify clusters, applying a relabeling algorithm to the model with the best fit may not generate the optimal relabeling. PMID:25221430
Space-Time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues
Hossain, M.M.; Lawson, A.B.; Cai, B.; Choi, J.; Liu, J.; Kirby, R. S.
2014-01-01
With the growing popularity of spatial mixture models in cluster analysis, model selection criteria have become an established tool in the search for parsimony. However, the label-switching problem is often inherent in Bayesian implementation of mixture models and a variety of relabeling algorithms have been proposed. We use a space-time mixture of Poisson regression models with homogeneous covariate effects to illustrate that the best model selected by using model selection criteria does not always support the model that is chosen by the optimal relabeling algorithm. The results are illustrated for real and simulated datasets. The objective is to make the reader aware that if the purpose of statistical modeling is to identify clusters, applying a relabeling algorithm to the model with the best fit may not generate the optimal relabeling. PMID:25221430
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
ERIC Educational Resources Information Center
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Modeling selective attention using a neuromorphic analog VLSI device.
Indiveri, G
2000-12-01
Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features. PMID:11112258
Modeling Selective Elimination of Quiescent Cancer Cells from Bone Marrow
Cavnar, Stephen P.; Rickelmann, Andrew D.; Meguiar, Kaille F.; Xiao, Annie; Dosch, Joseph; Leung, Brendan M.; Cai Lesher-Perez, Sasha; Chitta, Shashank; Luker, Kathryn E.; Takayama, Shuichi; Luker, Gary D.
2015-01-01
Patients with many types of malignancy commonly harbor quiescent disseminated tumor cells in bone marrow. These cells frequently resist chemotherapy and may persist for years before proliferating as recurrent metastases. To test for compounds that eliminate quiescent cancer cells, we established a new 384-well 3D spheroid model in which small numbers of cancer cells reversibly arrest in G1/G0 phase of the cell cycle when cultured with bone marrow stromal cells. Using dual-color bioluminescence imaging to selectively quantify viability of cancer and stromal cells in the same spheroid, we identified single compounds and combination treatments that preferentially eliminated quiescent breast cancer cells but not stromal cells. A treatment combination effective against malignant cells in spheroids also eliminated breast cancer cells from bone marrow in a mouse xenograft model. This research establishes a novel screening platform for therapies that selectively target quiescent tumor cells, facilitating identification of new drugs to prevent recurrent cancer. PMID:26408255
Parameter Estimation and Model Selection in Computational Biology
Lillacci, Gabriele; Khammash, Mustafa
2010-01-01
A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262
Model validation and selection based on inverse fuzzy arithmetic
NASA Astrophysics Data System (ADS)
Haag, Thomas; Carvajal González, Sergio; Hanss, Michael
2012-10-01
In this work, a method for the validation of models in general, and the selection of the most appropriate model in particular, is presented. As an industrially relevant example, a Finite Element (FE) model of a brake pad is investigated and identified with particular respect to uncertainties. The identification is based on inverse fuzzy arithmetic and consists of two stages. In the first stage, the eigenfrequencies of the brake pad are considered, and for three different material models, a set of fuzzy-valued parameters is identified on the basis of measurement values. Based on these identified parameters and a resimulation of the system with these parameters, a model validation is performed which takes into account both the model uncertainties and the output uncertainties. In the second stage, the most appropriate material model is used in the FE model for the computation of frequency response functions between excitation point and three measurement points. Again, the parameters of the model are identified on the basis of three corresponding measurement signals and a resimulation is conducted.
The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection
ERIC Educational Resources Information Center
Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.
2013-01-01
Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low…
Bayesian model selection applied to artificial neural networks used for water resources modeling
NASA Astrophysics Data System (ADS)
Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
2008-04-01
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.
The hierarchical sparse selection model of visual crowding
Chaney, Wesley; Fischer, Jason; Whitney, David
2014-01-01
Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable – destroyed due to over-integration in early stage visual processing – recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the “gist” of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding—the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed. PMID:25309360
Selecting Meteorological Input for the Global Modeling Initiative Assessments
NASA Technical Reports Server (NTRS)
Strahan, Susan; Douglass, Anne; Prather, Michael; Coy, Larry; Hall, Tim; Rasch, Phil; Sparling, Lynn
1999-01-01
The Global Modeling Initiative (GMI) science team has developed a three dimensional chemistry and transport model (CTM) to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. An important goal of the GMI is to test modules for numerical transport, photochemical integration, and model dynamics within a common framework. This work is focussed on the dependence of the overall assessment on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere were available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS-DAS), and the Goddard Institute for Space Studies general circulation model (GISS-2'). Objective criteria were established by the GMI team to evaluate which of these three data sets provided the best representation of trace gases in the stratosphere today. Tracer experiments were devised to test various aspects of model transport. Stratospheric measurements of long-lived trace gases were selected as a test of the CTM transport. This presentation describes the criteria used in grading the meteorological fields and the resulting choice of wind fields to be used in the GMI assessment. This type of objective model evaluation will lead to a higher level of confidence in these assessments. We suggest that the diagnostic tests shown here be used to augment traditional general circulation model evaluation methods.
Whelan, Simon; Allen, James E; Blackburne, Benjamin P; Talavera, David
2015-01-01
Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons. A major limitation of existing model selection approaches is that they can only compare models acting upon a single type of data. Here, we extend model selection to allow comparisons between models describing different types of data by introducing the idea of adapter functions, which project aggregated models onto the originally observed sequence data. These projections are implemented in the program ModelOMatic and used to perform model selection on 3722 families from the PANDIT database, 68 genes from an arthropod phylogenomic data set, and 248 genes from a vertebrate phylogenomic data set. For the PANDIT and arthropod data, we find that amino acid models are selected for the overwhelming majority of alignments; with progressively smaller numbers of alignments selecting codon and nucleotide models, and no families selecting RY-based models. In contrast, nearly all alignments from the vertebrate data set select codon-based models. The sequence divergence, the number of sequences, and the degree of selection acting upon the protein sequences may contribute to explaining this variation in model selection. Our ModelOMatic program is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines. ModelOMatic is available at https://code.google.com/p/modelomatic/. PMID:25209223
Model selection and inference for censored lifetime medical expenditures.
Johnson, Brent A; Long, Qi; Huang, Yijian; Chansky, Kari; Redman, Mary
2016-09-01
Identifying factors associated with increased medical cost is important for many micro- and macro-institutions, including the national economy and public health, insurers and the insured. However, assembling comprehensive national databases that include both the cost and individual-level predictors can prove challenging. Alternatively, one can use data from smaller studies with the understanding that conclusions drawn from such analyses may be limited to the participant population. At the same time, smaller clinical studies have limited follow-up and lifetime medical cost may not be fully observed for all study participants. In this context, we develop new model selection methods and inference procedures for secondary analyses of clinical trial data when lifetime medical cost is subject to induced censoring. Our model selection methods extend a theory of penalized estimating function to a calibration regression estimator tailored for this data type. Next, we develop a novel inference procedure for the unpenalized regression estimator using perturbation and resampling theory. Then, we extend this resampling plan to accommodate regularized coefficient estimation of censored lifetime medical cost and develop postselection inference procedures for the final model. Our methods are motivated by data from Southwest Oncology Group Protocol 9509, a clinical trial of patients with advanced nonsmall cell lung cancer, and our models of lifetime medical cost are specific to this population. But the methods presented in this article are built on rather general techniques and could be applied to larger databases as those data become available. PMID:26689300
UQ-Guided Selection of Physical Parameterizations in Climate Models
NASA Astrophysics Data System (ADS)
Lucas, D. D.; Debusschere, B.; Ghan, S.; Rosa, D.; Bulaevskaya, V.; Anderson, G. J.; Chowdhary, K.; Qian, Y.; Lin, G.; Larson, V. E.; Zhang, G. J.; Randall, D. A.
2015-12-01
Given two or more parameterizations that represent the same physical process in a climate model, scientists are sometimes faced with difficult decisions about which scheme to choose for their simulations and analysis. These decisions are often based on subjective criteria, such as "which scheme is easier to use, is computationally less expensive, or produces results that look better?" Uncertainty quantification (UQ) and model selection methods can be used to objectively rank the performance of different physical parameterizations by increasing the preference for schemes that fit observational data better, while at the same time penalizing schemes that are overly complex or have excessive degrees-of-freedom. Following these principles, we are developing a perturbed-parameter UQ framework to assist in the selection of parameterizations for a climate model. Preliminary results will be presented on the application of the framework to assess the performance of two alternate schemes for simulating tropical deep convection (CLUBB-SILHS and ZM-trigmem) in the U.S. Dept. of Energy's ACME climate model. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, is supported by the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC), and is released as LLNL-ABS-675799.
Selection of Representative Models for Decision Analysis Under Uncertainty
NASA Astrophysics Data System (ADS)
Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.
2016-03-01
The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.
Strategy selection: An introduction to the modeling challenge.
Marewski, Julian N; Link, Daniela
2014-01-01
Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost-benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved. WIREs Cogn Sci 2014, 5:39-59. doi: 10.1002/wcs.1265 For further resources related to this article, please visit the WIREs website. PMID:26304296
Variable selection method for the identification of epistatic models.
Holzinger, Emily Rose; Szymczak, Silke; Dasgupta, Abhijit; Malley, James; Li, Qing; Bailey-Wilson, Joan E
2015-01-01
Standard analysis methods for genome wide association studies (GWAS) are not robust to complex disease models, such as interactions between variables with small main effects. These types of effects likely contribute to the heritability of complex human traits. Machine learning methods that are capable of identifying interactions, such as Random Forests (RF), are an alternative analysis approach. One caveat to RF is that there is no standardized method of selecting variables so that false positives are reduced while retaining adequate power. To this end, we have developed a novel variable selection method called relative recurrency variable importance metric (r2VIM). This method incorporates recurrency and variance estimation to assist in optimal threshold selection. For this study, we specifically address how this method performs in data with almost completely epistatic effects (i.e. no marginal effects). Our results show that with appropriate parameter settings, r2VIM can identify interaction effects when the marginal effects are virtually nonexistent. It also outperforms logistic regression, which has essentially no power under this type of model when the number of potential features (genetic variants) is large. (All Supplementary Data can be found here: http://research.nhgri.nih.gov/manuscripts/Bailey-Wilson/r2VIM_epi/). PMID:25592581
VARIABLE SELECTION METHOD FOR THE IDENTIFICATION OF EPISTATIC MODELS
HOLZINGER, EMILY ROSE; SZYMCZAK, SILKE; DASGUPTA, ABHIJIT; MALLEY, JAMES; LI, QING; BAILEY-WILSON, JOAN E.
2014-01-01
Standard analysis methods for genome wide association studies (GWAS) are not robust to complex disease models, such as interactions between variables with small main effects. These types of effects likely contribute to the heritability of complex human traits. Machine learning methods that are capable of identifying interactions, such as Random Forests (RF), are an alternative analysis approach. One caveat to RF is that there is no standardized method of selecting variables so that false positives are reduced while retaining adequate power. To this end, we have developed a novel variable selection method called relative recurrency variable importance metric (r2VIM). This method incorporates recurrency and variance estimation to assist in optimal threshold selection. For this study, we specifically address how this method performs in data with almost completely epistatic effects (i.e. no marginal effects). Our results show that with appropriate parameter settings, r2VIM can identify interaction effects when the marginal effects are virtually nonexistent. It also outperforms logistic regression, which has essentially no power under this type of model when the number of potential features (genetic variants) is large. (All Supplementary Data can be found here: http://research.nhgri.nih.gov/manuscripts/Bailey-Wilson/r2VIM_epi/). PMID:25592581
Multilevel selection in a resource-based model
NASA Astrophysics Data System (ADS)
Ferreira, Fernando Fagundes; Campos, Paulo R. A.
2013-07-01
In the present work we investigate the emergence of cooperation in a multilevel selection model that assumes limiting resources. Following the work by R. J. Requejo and J. Camacho [Phys. Rev. Lett.0031-900710.1103/PhysRevLett.108.038701 108, 038701 (2012)], the interaction among individuals is initially ruled by a prisoner's dilemma (PD) game. The payoff matrix may change, influenced by the resource availability, and hence may also evolve to a non-PD game. Furthermore, one assumes that the population is divided into groups, whose local dynamics is driven by the payoff matrix, whereas an intergroup competition results from the nonuniformity of the growth rate of groups. We study the probability that a single cooperator can invade and establish in a population initially dominated by defectors. Cooperation is strongly favored when group sizes are small. We observe the existence of a critical group size beyond which cooperation becomes counterselected. Although the critical size depends on the parameters of the model, it is seen that a saturation value for the critical group size is achieved. The results conform to the thought that the evolutionary history of life repeatedly involved transitions from smaller selective units to larger selective units.
Selecting global climate models for regional climate change studies
Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.
2009-01-01
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652
A Dual-Stage Two-Phase Model of Selective Attention
ERIC Educational Resources Information Center
Hubner, Ronald; Steinhauser, Marco; Lehle, Carola
2010-01-01
The dual-stage two-phase (DSTP) model is introduced as a formal and general model of selective attention that includes both an early and a late stage of stimulus selection. Whereas at the early stage information is selected by perceptual filters whose selectivity is relatively limited, at the late stage stimuli are selected more efficiently on a…
Automation of Endmember Pixel Selection in SEBAL/METRIC Model
NASA Astrophysics Data System (ADS)
Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.
2015-12-01
The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.
Selection Strategies for Social Influence in the Threshold Model
NASA Astrophysics Data System (ADS)
Karampourniotis, Panagiotis; Szymanski, Boleslaw; Korniss, Gyorgy
The ubiquity of online social networks makes the study of social influence extremely significant for its applications to marketing, politics and security. Maximizing the spread of influence by strategically selecting nodes as initiators of a new opinion or trend is a challenging problem. We study the performance of various strategies for selection of large fractions of initiators on a classical social influence model, the Threshold model (TM). Under the TM, a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. The strategies we study are of two kinds: strategies based solely on the initial network structure (Degree-rank, Dominating Sets, PageRank etc.) and strategies that take into account the change of the states of the nodes during the evolution of the cascade, e.g. the greedy algorithm. We find that the performance of these strategies depends largely on both the network structure properties, e.g. the assortativity, and the distribution of the thresholds assigned to the nodes. We conclude that the optimal strategy needs to combine the network specifics and the model specific parameters to identify the most influential spreaders. Supported in part by ARL NS-CTA, ARO, and ONR.
Selection Experiments in the Penna Model for Biological Aging
NASA Astrophysics Data System (ADS)
Medeiros, G.; Idiart, M. A.; de Almeida, R. M. C.
We consider the Penna model for biological aging to investigate correlations between early fertility and late life survival rates in populations at equilibrium. We consider inherited initial reproduction ages together with a reproduction cost translated in a probability that mother and offspring die at birth, depending on the mother age. For convenient sets of parameters, the equilibrated populations present genetic variability in what regards both genetically programmed death age and initial reproduction age. In the asexual Penna model, a negative correlation between early life fertility and late life survival rates naturally emerges in the stationary solutions. In the sexual Penna model, selection experiments are performed where individuals are sorted by initial reproduction age from the equilibrated populations and the separated populations are evolved independently. After a transient, a negative correlation between early fertility and late age survival rates also emerges in the sense that populations that start reproducing earlier present smaller average genetically programmed death age. These effects appear due to the age structure of populations in the steady state solution of the evolution equations. We claim that the same demographic effects may be playing an important role in selection experiments in the laboratory.
Analysis improves selection of rheological model for slurries
Moftah, K. )
1993-10-25
The use of a statistical index of determination can help select a fluid model to describe the rheology of oil well cement slurries. The closer the index is to unity, the better the particular model will describe the actual fluid behavior. Table 1 lists a computer program written in Quick Basic to calculate rheological parameters and an index of determination for the Bingham plastic and power law models. The points used for the calculation of the rheological parameters can be selected from the data set. The skipped points can then be introduced and the calculations continued, not restarted, to obtain the parameters for the full set of data. The two sets of results are then compared for the decision to include or exclude the added points in the regression. The program also calculates the apparent viscosity to help determine where turbulence or high gross error occurred. In addition, the program calculates the confidence interval of the rheological parameters for a 90% level of confidence.
Selection of models to calculate the LLW source term
Sullivan, T.M. )
1991-10-01
Performance assessment of a LLW disposal facility begins with an estimation of the rate at which radionuclides migrate out of the facility (i.e., the source term). The focus of this work is to develop a methodology for calculating the source term. In general, the source term is influenced by the radionuclide inventory, the wasteforms and containers used to dispose of the inventory, and the physical processes that lead to release from the facility (fluid flow, container degradation, wasteform leaching, and radionuclide transport). In turn, many of these physical processes are influenced by the design of the disposal facility (e.g., infiltration of water). The complexity of the problem and the absence of appropriate data prevent development of an entirely mechanistic representation of radionuclide release from a disposal facility. Typically, a number of assumptions, based on knowledge of the disposal system, are used to simplify the problem. This document provides a brief overview of disposal practices and reviews existing source term models as background for selecting appropriate models for estimating the source term. The selection rationale and the mathematical details of the models are presented. Finally, guidance is presented for combining the inventory data with appropriate mechanisms describing release from the disposal facility. 44 refs., 6 figs., 1 tab.
A Successive Selection Method for finite element model updating
NASA Astrophysics Data System (ADS)
Gou, Baiyong; Zhang, Weijie; Lu, Qiuhai; Wang, Bo
2016-03-01
Finite Element (FE) model can be updated effectively and efficiently by using the Response Surface Method (RSM). However, it often involves performance trade-offs such as high computational cost for better accuracy or loss of efficiency for lots of design parameter updates. This paper proposes a Successive Selection Method (SSM), which is based on the linear Response Surface (RS) function and orthogonal design. SSM rewrites the linear RS function into a number of linear equations to adjust the Design of Experiment (DOE) after every FE calculation. SSM aims to interpret the implicit information provided by the FE analysis, to locate the Design of Experiment (DOE) points more quickly and accurately, and thereby to alleviate the computational burden. This paper introduces the SSM and its application, describes the solution steps of point selection for DOE in detail, and analyzes SSM's high efficiency and accuracy in the FE model updating. A numerical example of a simply supported beam and a practical example of a vehicle brake disc show that the SSM can provide higher speed and precision in FE model updating for engineering problems than traditional RSM.
Continuum model for chiral induced spin selectivity in helical molecules
Medina, Ernesto; González-Arraga, Luis A.; Finkelstein-Shapiro, Daniel; Mujica, Vladimiro; Berche, Bertrand
2015-05-21
A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.
Development of Solar Drying Model for Selected Cambodian Fish Species
Hubackova, Anna; Kucerova, Iva; Chrun, Rithy; Chaloupkova, Petra; Banout, Jan
2014-01-01
A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h−1. Based on coefficient of determination (R2), chi-square (χ2) test, and root-mean-square error (RMSE), the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing. PMID:25250381
Development of solar drying model for selected Cambodian fish species.
Hubackova, Anna; Kucerova, Iva; Chrun, Rithy; Chaloupkova, Petra; Banout, Jan
2014-01-01
A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6 °C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg · h(-1). Based on coefficient of determination (R(2)), chi-square (χ(2)) test, and root-mean-square error (RMSE), the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing. PMID:25250381
Variable selection for semiparametric mixed models in longitudinal studies.
Ni, Xiao; Zhang, Daowen; Zhang, Hao Helen
2010-03-01
We propose a double-penalized likelihood approach for simultaneous model selection and estimation in semiparametric mixed models for longitudinal data. Two types of penalties are jointly imposed on the ordinary log-likelihood: the roughness penalty on the nonparametric baseline function and a nonconcave shrinkage penalty on linear coefficients to achieve model sparsity. Compared to existing estimation equation based approaches, our procedure provides valid inference for data with missing at random, and will be more efficient if the specified model is correct. Another advantage of the new procedure is its easy computation for both regression components and variance parameters. We show that the double-penalized problem can be conveniently reformulated into a linear mixed model framework, so that existing software can be directly used to implement our method. For the purpose of model inference, we derive both frequentist and Bayesian variance estimation for estimated parametric and nonparametric components. Simulation is used to evaluate and compare the performance of our method to the existing ones. We then apply the new method to a real data set from a lactation study. PMID:19397585
A qualitative model structure sensitivity analysis method to support model selection
NASA Astrophysics Data System (ADS)
Van Hoey, S.; Seuntjens, P.; van der Kwast, J.; Nopens, I.
2014-11-01
The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria.
Evaluation of Model Fit in Cognitive Diagnosis Models
ERIC Educational Resources Information Center
Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin
2016-01-01
Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…
Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J
2015-03-01
We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models. PMID:25287611
A Neuronal Network Model for Pitch Selectivity and Representation
Huang, Chengcheng; Rinzel, John
2016-01-01
Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions. PMID:27378900
BUILDING ROBUST APPEARANCE MODELS USING ON-LINE FEATURE SELECTION
PORTER, REID B.; LOVELAND, ROHAN; ROSTEN, ED
2007-01-29
In many tracking applications, adapting the target appearance model over time can improve performance. This approach is most popular in high frame rate video applications where latent variables, related to the objects appearance (e.g., orientation and pose), vary slowly from one frame to the next. In these cases the appearance model and the tracking system are tightly integrated, and latent variables are often included as part of the tracking system's dynamic model. In this paper we describe our efforts to track cars in low frame rate data (1 frame/second) acquired from a highly unstable airborne platform. Due to the low frame rate, and poor image quality, the appearance of a particular vehicle varies greatly from one frame to the next. This leads us to a different problem: how can we build the best appearance model from all instances of a vehicle we have seen so far. The best appearance model should maximize the future performance of the tracking system, and maximize the chances of reacquiring the vehicle once it leaves the field of view. We propose an online feature selection approach to this problem and investigate the performance and computational trade-offs with a real-world dataset.
Stochastic group selection model for the evolution of altruism
NASA Astrophysics Data System (ADS)
Silva, Ana T. C.; Fontanari, J. F.
We study numerically and analytically a stochastic group selection model in which a population of asexually reproducing individuals, each of which can be either altruist or non-altruist, is subdivided into M reproductively isolated groups (demes) of size N. The cost associated with being altruistic is modelled by assigning the fitness 1- τ, with τ∈[0,1], to the altruists and the fitness 1 to the non-altruists. In the case that the altruistic disadvantage τ is not too large, we show that the finite M fluctuations are small and practically do not alter the deterministic results obtained for M→∞. However, for large τ these fluctuations greatly increase the instability of the altruistic demes to mutations. These results may be relevant to the dynamics of parasite-host systems and, in particular, to explain the importance of mutation in the evolution of parasite virulence.
Radial Domany-Kinzel models with mutation and selection
NASA Astrophysics Data System (ADS)
Lavrentovich, Maxim O.; Korolev, Kirill S.; Nelson, David R.
2013-01-01
We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R0 expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t*=R0/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island.
Modeling drivers' speed selection as a trade-off behavior.
Tarko, Andrew P
2009-05-01
This paper proposes a new model of driver-preferred speeds derived from the assumption that drivers trade-off a portion of their safety for a time gain. The risk of receiving a ticket for speeding is also considered. A trip disutility concept is selected to combine the three components of speed choice (safety, time, and enforcement). The perceived crash risk and speed enforcement are considered as speed deterrents while the perceived value of a time gain is considered as a speed enticement. According to this concept, speeds that minimize the perceived trip disutility are preferred by drivers. The modeled trade-off behavior does not have to be fully rational since it is affected by drivers' preferences and their ability to perceive the risk. As such, the proposed framework follows the concept of bound rationality. The attractiveness of the model lies in its parameters being estimable with the observed preferred speeds and then interpretable as the factors of risk perception, the subjective value of time, and the perceived risk of speed enforcement. The proposed method may successfully supplement behavioral studies based on a driver survey. The study focuses on four-lane rural and suburban roads in Indiana, USA. The behavior of two types of drivers (trucks and cars) is modeled. The selection of test sites was such that the roads and other local characteristics varied across the studied sites while the population of drivers could be assumed as the same. The density of intersections, land development along the road, and the presence of sidewalks were the identified prominent risk perception factors. Another interesting finding is that the speed limit seems to encourage slow drivers to drive faster and fast drivers to drive slower. PMID:19393813
Radial Domany-Kinzel models with mutation and selection.
Lavrentovich, Maxim O; Korolev, Kirill S; Nelson, David R
2013-01-01
We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. "Inflation" at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R(0) expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t(*)=R(0)/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island. PMID:23410279
Technology Transfer Automated Retrieval System (TEKTRAN)
Selection for disease resistance is a contemporary topic with developing approaches for genetic improvement. Merging the sciences of genetic selection and epidemiology is essential to identify selection schemes to enhance disease resistance. Epidemiological models can identify theoretical opportuni...
Bayesian Model Selection with Network Based Diffusion Analysis.
Whalen, Andrew; Hoppitt, William J E
2016-01-01
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed. PMID:27092089
Percolation model for selective dissolution of multi-component glasses
Kale, R.P.; Brinker, C.J.
1995-03-01
A percolation model is developed which accounts for most known features of the process of porous glass membrane preparation by selective dissolution of multi-component glasses. The model is founded within tile framework of the classical percolation theory, wherein the components of a glass are represented by random sites on a suitable lattice. Computer simulation is used to mirror the generation of a porous structure during the dissolution process, reproducing many of the features associated with the phenomenon. Simulation results evaluate the effect of the initial composition of the glass on the kinetics of the leaching process as well as the morphology of the generated porous structure. The percolation model establishes the porous structure as a percolating cluster of unreachable constituents in the glass. The simulation algorithm incorporates removal of both, the accessible leachable components in the glass as well as the independent clusters of unreachable components not attached to the percolating cluster. The dissolution process thus becomes limited by the conventional site percolation thresholds of the unreachable components (which restricts the formation of the porous network), as well as the leachable components (which restricts the accessibility of the solvating medium into the glass). The simulation results delineate the range of compositional variations for successful porous glass preparation and predict the variation of porosity, surface area, dissolution rates and effluent composition with initial composition and time. Results compared well with experimental studies and improved upon similar models attempted in die past.
Bayesian Model Selection with Network Based Diffusion Analysis
Whalen, Andrew; Hoppitt, William J. E.
2016-01-01
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed. PMID:27092089
COUNCIL FOR REGULATORY ENVIRONMENTAL MODELING (CREM) PILOT WATER QUALITY MODEL SELECTION TOOL
EPA's Council for Regulatory Environmental Modeling (CREM) is currently supporting the development of a pilot model selection tool that is intended to help the states and the regions implement the total maximum daily load (TMDL) program. This tool will be implemented within the ...
Impact of selected troposphere models on Precise Point Positioning convergence
NASA Astrophysics Data System (ADS)
Kalita, Jakub; Rzepecka, Zofia
2016-04-01
The Precise Point Positioning (PPP) absolute method is currently intensively investigated in order to reach fast convergence time. Among various sources that influence the convergence of the PPP, the tropospheric delay is one of the most important. Numerous models of tropospheric delay are developed and applied to PPP processing. However, with rare exceptions, the quality of those models does not allow fixing the zenith path delay tropospheric parameter, leaving difference between nominal and final value to the estimation process. Here we present comparison of several PPP result sets, each of which based on different troposphere model. The respective nominal values are adopted from models: VMF1, GPT2w, MOPS and ZERO-WET. The PPP solution admitted as reference is based on the final troposphere product from the International GNSS Service (IGS). The VMF1 mapping function was used for all processing variants in order to provide capability to compare impact of applied nominal values. The worst case initiates zenith wet delay with zero value (ZERO-WET). Impact from all possible models for tropospheric nominal values should fit inside both IGS and ZERO-WET border variants. The analysis is based on data from seven IGS stations located in mid-latitude European region from year 2014. For the purpose of this study several days with the most active troposphere were selected for each of the station. All the PPP solutions were determined using gLAB open-source software, with the Kalman filter implemented independently by the authors of this work. The processing was performed on 1 hour slices of observation data. In addition to the analysis of the output processing files, the presented study contains detailed analysis of the tropospheric conditions for the selected data. The overall results show that for the height component the VMF1 model outperforms GPT2w and MOPS by 35-40% and ZERO-WET variant by 150%. In most of the cases all solutions converge to the same values during first
On model selections for repeated measurement data in clinical studies.
Zou, Baiming; Jin, Bo; Koch, Gary G; Zhou, Haibo; Borst, Stephen E; Menon, Sandeep; Shuster, Jonathan J
2015-05-10
Repeated measurement designs have been widely used in various randomized controlled trials for evaluating long-term intervention efficacies. For some clinical trials, the primary research question is how to compare two treatments at a fixed time, using a t-test. Although simple, robust, and convenient, this type of analysis fails to utilize a large amount of collected information. Alternatively, the mixed-effects model is commonly used for repeated measurement data. It models all available data jointly and allows explicit assessment of the overall treatment effects across the entire time spectrum. In this paper, we propose an analytic strategy for longitudinal clinical trial data where the mixed-effects model is coupled with a model selection scheme. The proposed test statistics not only make full use of all available data but also utilize the information from the optimal model deemed for the data. The performance of the proposed method under various setups, including different data missing mechanisms, is evaluated via extensive Monte Carlo simulations. Our numerical results demonstrate that the proposed analytic procedure is more powerful than the t-test when the primary interest is to test for the treatment effect at the last time point. Simulations also reveal that the proposed method outperforms the usual mixed-effects model for testing the overall treatment effects across time. In addition, the proposed framework is more robust and flexible in dealing with missing data compared with several competing methods. The utility of the proposed method is demonstrated by analyzing a clinical trial on the cognitive effect of testosterone in geriatric men with low baseline testosterone levels. PMID:25645442
A Model for Selection of Eyespots on Butterfly Wings
Sekimura, Toshio; Venkataraman, Chandrasekhar; Madzvamuse, Anotida
2015-01-01
Unsolved Problem The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins). A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not. Key Idea and Model We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed) boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus
Improving permafrost distribution modelling using feature selection algorithms
NASA Astrophysics Data System (ADS)
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
Multiphysics modeling of selective laser sintering/melting
NASA Astrophysics Data System (ADS)
Ganeriwala, Rishi Kumar
A significant percentage of total global employment is due to the manufacturing industry. However, manufacturing also accounts for nearly 20% of total energy usage in the United States according to the EIA. In fact, manufacturing accounted for 90% of industrial energy consumption and 84% of industry carbon dioxide emissions in 2002. Clearly, advances in manufacturing technology and efficiency are necessary to curb emissions and help society as a whole. Additive manufacturing (AM) refers to a relatively recent group of manufacturing technologies whereby one can 3D print parts, which has the potential to significantly reduce waste, reconfigure the supply chain, and generally disrupt the whole manufacturing industry. Selective laser sintering/melting (SLS/SLM) is one type of AM technology with the distinct advantage of being able to 3D print metals and rapidly produce net shape parts with complicated geometries. In SLS/SLM parts are built up layer-by-layer out of powder particles, which are selectively sintered/melted via a laser. However, in order to produce defect-free parts of sufficient strength, the process parameters (laser power, scan speed, layer thickness, powder size, etc.) must be carefully optimized. Obviously, these process parameters will vary depending on material, part geometry, and desired final part characteristics. Running experiments to optimize these parameters is costly, energy intensive, and extremely material specific. Thus a computational model of this process would be highly valuable. In this work a three dimensional, reduced order, coupled discrete element - finite difference model is presented for simulating the deposition and subsequent laser heating of a layer of powder particles sitting on top of a substrate. Validation is provided and parameter studies are conducted showing the ability of this model to help determine appropriate process parameters and an optimal powder size distribution for a given material. Next, thermal stresses upon
Hyperopt: a Python library for model selection and hyperparameter optimization
NASA Astrophysics Data System (ADS)
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Estimating a dynamic model of sex selection in China.
Ebenstein, Avraham
2011-05-01
High ratios of males to females in China, which have historically concerned researchers (Sen 1990), have increased in the wake of China's one-child policy, which began in 1979. Chinese policymakers are currently attempting to correct the imbalance in the sex ratio through initiatives that provide financial compensation to parents with daughters. Other scholars have advocated a relaxation of the one-child policy to allow more parents to have a son without engaging in sex selection. In this article, I present a model of fertility choice when parents have access to a sex-selection technology and face a mandated fertility limit. By exploiting variation in fines levied in China for unsanctioned births, I estimate the relative price of a son and daughter for mothers observed in China's census data (1982-2000). I find that a couple's first son is worth 1.42 years of income more than a first daughter, and the premium is highest among less-educated mothers and families engaged in agriculture. Simulations indicate that a subsidy of 1 year of income to families without a son would reduce the number of "missing girls" by 67% but impose an annual cost of 1.8% of Chinese gross domestic product (GDP). Alternatively, a three-child policy would reduce the number of "missing girls" by 56% but increase the fertility rate by 35%. PMID:21594735
Model catalysis by size-selected cluster deposition
Anderson, Scott
2015-11-20
This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.
A clonal selection algorithm model for daily rainfall data prediction.
Noor Rodi, N S; Malek, M A; Ismail, Amelia Ritahani; Ting, Sie Chun; Tang, Chao-Wei
2014-01-01
This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction. PMID:25429452
Analytical Modelling Of Milling For Tool Design And Selection
Fontaine, M.; Devillez, A.; Dudzinski, D.
2007-05-17
This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools.
Model of selective growth of III-V nanowires
NASA Astrophysics Data System (ADS)
Dubrovskii, V. G.
2015-12-01
A kinetic model of growth of nanowires of III-V semiconductor compounds (including nitride ones) in the absence of metal catalyst is proposed; these conditions correspond to the methods of selective epitaxy or self-induced growth. A stationary solution for the nanowire growth rate is obtained, which indicates that the growth can be limited by not only the kinetics of III-group element with allowance for the surface diffusion (as was suggested earlier), but also the flow of the V-group element. Different modes are characterized by radically different dependences of the growth rate on the nanowire radius. Under arsenicenriched conditions, a typical dependence with a maximum and decay at large radii (limited by the gallium adatom diffusion) is observed. Under gallium-enriched conditions, there is a transition to the growth rate that is practically independent of the radius and linearly increases with an increase in the arsenic flow.
[Model of the selective calcium channel of characean algae].
Lunevskiĭ, V Z; Zherelova, O M; Aleksandrov, A A; Vinokurov, M G; Berestovskiĭ, G N
1980-01-01
The present work was intended to further investigate the selective filter of calcium channel on both a cell membrane and reconstructed channels. For the studies on cell membranes, an inhibitor of chloride channels was chosen (ethacrynic acid) to pass currents only through the calcium channels. On both the cells and reconstructed channels, permeability of ions of different crystal radii and valencies was investigated. The obtained results suggest that the channel represents a wide water pore with a diameter larger than 8 A into which ions go together with the nearest water shell. The values of the maximal currents are given by electrostatic interaction of the ions with the anion center of the channel. A phenomenological two-barrier model of the channel is given which describes the movement of all the ions studied. PMID:6251921
Bayesian Model Selection in 'Big Data' Spectral Analysis
NASA Astrophysics Data System (ADS)
Fischer, Travis C.; Crenshaw, D. Michael; Baron, Fabien; Kloppenborg, Brian K.; Pope, Crystal L.
2015-01-01
As IFU observations and large spectral surveys continue to become more prevalent, the handling of thousands of spectra has become common place. Astronomers look at objects with increasingly complex emission-linestructures, so establishing a method that will easily allow for multiple-component analysis of these features in an automated fashion would be of great use to the community. Already used in exoplanet detection and interferometric image reconstruction, we present a new application of Bayesian model selection in `big data' spectral analysis. With this technique, the fitting of multiple emission-line components in an automated fashion while simultaneously determining the correct number of components in each spectrum streamlines the line measurements for a large number of spectra into a single process.
ModelMage: a tool for automatic model generation, selection and management.
Flöttmann, Max; Schaber, Jörg; Hoops, Stephan; Klipp, Edda; Mendes, Pedro
2008-01-01
Mathematical modeling of biological systems usually involves implementing, simulating, and discriminating several candidate models that represent alternative hypotheses. Generating and managing these candidate models is a tedious and difficult task and can easily lead to errors. ModelMage is a tool that facilitates management of candidate models. It is designed for the easy and rapid development, generation, simulation, and discrimination of candidate models. The main idea of the program is to automatically create a defined set of model alternatives from a single master model. The user provides only one SBML-model and a set of directives from which the candidate models are created by leaving out species, modifiers or reactions. After generating models the software can automatically fit all these models to the data and provides a ranking for model selection, in case data is available. In contrast to other model generation programs, ModelMage aims at generating only a limited set of models that the user can precisely define. ModelMage uses COPASI as a simulation and optimization engine. Thus, all simulation and optimization features of COPASI are readily incorporated. ModelMage can be downloaded from http://sysbio.molgen.mpg.de/modelmage and is distributed as free software. PMID:19425122
Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study
Sukumar, Sreenivas R; Nutaro, James J
2012-01-01
This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.
NASA Astrophysics Data System (ADS)
Surfleet, Christopher G.; Tullos, Desirèe; Chang, Heejun; Jung, Il-Won
2012-09-01
SummaryA wide variety of approaches to hydrologic (rainfall-runoff) modeling of river basins confounds our ability to select, develop, and interpret models, particularly in the evaluation of prediction uncertainty associated with climate change assessment. To inform the model selection process, we characterized and compared three structurally-distinct approaches and spatial scales of parameterization to modeling catchment hydrology: a large-scale approach (using the VIC model; 671,000 km2 area), a basin-scale approach (using the PRMS model; 29,700 km2 area), and a site-specific approach (the GSFLOW model; 4700 km2 area) forced by the same future climate estimates. For each approach, we present measures of fit to historic observations and predictions of future response, as well as estimates of model parameter uncertainty, when available. While the site-specific approach generally had the best fit to historic measurements, the performance of the model approaches varied. The site-specific approach generated the best fit at unregulated sites, the large scale approach performed best just downstream of flood control projects, and model performance varied at the farthest downstream sites where streamflow regulation is mitigated to some extent by unregulated tributaries and water diversions. These results illustrate how selection of a modeling approach and interpretation of climate change projections require (a) appropriate parameterization of the models for climate and hydrologic processes governing runoff generation in the area under study, (b) understanding and justifying the assumptions and limitations of the model, and (c) estimates of uncertainty associated with the modeling approach.
Projection- vs. selection-based model reduction of complex hydro-ecological models
NASA Astrophysics Data System (ADS)
Galelli, S.; Giuliani, M.; Castelletti, A.; Alsahaf, A.
2014-12-01
Projection-based model reduction is one of the most popular approaches used for the identification of reduced-order models (emulators). It is based on the idea of sampling from the original model various values, or snapshots, of the state variables, and then using these snapshots in a projection scheme to find a lower-dimensional subspace that captures the majority of the variation of the original model. The model is then projected onto this subspace and solved, yielding a computationally efficient emulator. Yet, this approach may unnecessarily increase the complexity of the emulator, especially when only a few state variables of the original model are relevant with respect to the output of interest. This is the case of complex hydro-ecological models, which typically account for a variety of water quality processes. On the other hand, selection-based model reduction uses the information contained in the snapshots to select the state variables of the original model that are relevant with respect to the emulator's output, thus allowing for model reduction. This provides a better trade-off between fidelity and model complexity, since the irrelevant and redundant state variables are excluded from the model reduction process. In this work we address these issues by presenting an exhaustive experimental comparison between two popular projection- and selection-based methods, namely Proper Orthogonal Decomposition (POD) and Dynamic Emulation Modelling (DEMo). The comparison is performed on the reduction of DYRESM-CAEDYM, a 1D hydro-ecological model used to describe the in-reservoir water quality conditions of Tono Dam, an artificial reservoir located in western Japan. Experiments on two different output variables (i.e. chlorophyll-a concentration and release water temperature) show that DEMo allows obtaining the same fidelity as POD while reducing the number of state variables in the emulator.
Lai, Tze Leung; Shih, Mei-Chiung; Wong, Samuel P
2006-02-01
By combining Laplace's approximation and Monte Carlo methods to evaluate multiple integrals, this paper develops a new approach to estimation in nonlinear mixed effects models that are widely used in population pharmacokinetics and pharmacodynamics. Estimation here involves not only estimating the model parameters from Phase I and II studies but also using the fitted model to estimate the concentration versus time curve or the drug effects of a subject who has covariate information but sparse measurements. Because of its computational tractability, the proposed approach can model the covariate effects nonparametrically by using (i) regression splines or neural networks as basis functions and (ii) AIC or BIC for model selection. Its computational and statistical advantages are illustrated in simulation studies and in Phase I trials. PMID:16402288
Bayesian predictive modeling for genomic based personalized treatment selection.
Ma, Junsheng; Stingo, Francesco C; Hobbs, Brian P
2016-06-01
Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches. PMID:26575856
NASA Astrophysics Data System (ADS)
Wentworth, Mami Tonoe
Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification
Connallon, Tim; Clark, Andrew G.
2012-01-01
Antagonistically selected alleles -- those with opposing fitness effects between sexes, environments, or fitness components -- represent an important component of additive genetic variance in fitness-related traits, with stably balanced polymorphisms often hypothesized to contribute to observed quantitative genetic variation. Balancing selection hypotheses imply that intermediate-frequency alleles disproportionately contribute to genetic variance of life history traits and fitness. Such alleles may also associate with population genetic footprints of recent selection, including reduced genetic diversity and inflated linkage disequilibrium at linked, neutral sites. Here, we compare the evolutionary dynamics of different balancing selection models, and characterize the evolutionary timescale and hitchhiking effects of partial selective sweeps generated under antagonistic versus non-antagonistic (e.g., overdominant and frequency-dependent selection) processes. We show that that the evolutionary timescales of partial sweeps tend to be much longer, and hitchhiking effects are drastically weaker, under scenarios of antagonistic selection. These results predict an interesting mismatch between molecular population genetic and quantitative genetic patterns of variation. Balanced, antagonistically selected alleles are expected to contribute more to additive genetic variance for fitness than alleles maintained by classic, non-antagonistic mechanisms. Nevertheless, classical mechanisms of balancing selection are much more likely to generate strong population genetic signatures of recent balancing selection. PMID:23461340
Model-based fault detection and identification with online aerodynamic model structure selection
NASA Astrophysics Data System (ADS)
Lombaerts, T.
2013-12-01
This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modification of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identification (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on a simulation data set.
Estimating seabed scattering mechanisms via Bayesian model selection.
Steininger, Gavin; Dosso, Stan E; Holland, Charles W; Dettmer, Jan
2014-10-01
A quantitative inversion procedure is developed and applied to determine the dominant scattering mechanism (surface roughness and/or volume scattering) from seabed scattering-strength data. The classification system is based on trans-dimensional Bayesian inversion with the deviance information criterion used to select the dominant scattering mechanism. Scattering is modeled using first-order perturbation theory as due to one of three mechanisms: Interface scattering from a rough seafloor, volume scattering from a heterogeneous sediment layer, or mixed scattering combining both interface and volume scattering. The classification system is applied to six simulated test cases where it correctly identifies the true dominant scattering mechanism as having greater support from the data in five cases; the remaining case is indecisive. The approach is also applied to measured backscatter-strength data where volume scattering is determined as the dominant scattering mechanism. Comparison of inversion results with core data indicates the method yields both a reasonable volume heterogeneity size distribution and a good estimate of the sub-bottom depths at which scatterers occur. PMID:25324059