Congruence analysis of geodetic networks - hypothesis tests versus model selection by information criteria

NASA Astrophysics Data System (ADS)

Lehmann, Rüdiger; Lösler, Michael

2017-12-01

Geodetic deformation analysis can be interpreted as a model selection problem. The null model indicates that no deformation has occurred. It is opposed to a number of alternative models, which stipulate different deformation patterns. A common way to select the right model is the usage of a statistical hypothesis test. However, since we have to test a series of deformation patterns, this must be a multiple test. As an alternative solution for the test problem, we propose the p-value approach. Another approach arises from information theory. Here, the Akaike information criterion (AIC) or some alternative is used to select an appropriate model for a given set of observations. Both approaches are discussed and applied to two test scenarios: A synthetic levelling network and the Delft test data set. It is demonstrated that they work but behave differently, sometimes even producing different results. Hypothesis tests are well-established in geodesy, but may suffer from an unfavourable choice of the decision error rates. The multiple test also suffers from statistical dependencies between the test statistics, which are neglected. Both problems are overcome by applying information criterions like AIC.

How Well Can We Detect Lineage-Specific Diversification-Rate Shifts? A Simulation Study of Sequential AIC Methods

PubMed Central

May, Michael R.; Moore, Brian R.

2016-01-01

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical methods for detecting shifts in the rate of lineage diversification across the branches of phylogenic trees. One of the most frequently used methods, MEDUSA, explores a set of diversification-rate models, where each model assigns branches of the phylogeny to a set of diversification-rate categories. Each model is first fit to the data, and the Akaike information criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is uncharacterized, which is a concern in light of: (1) the poor performance of the AIC as a means of choosing among models in other phylogenetic contexts; (2) the ad hoc algorithm used to visit diversification models, and; (3) errors that we reveal in the likelihood function used to fit diversification models to the phylogenetic data. Here, we perform an extensive simulation study demonstrating that MEDUSA (1) has a high false-discovery rate (on average, spurious diversification-rate shifts are identified ≈30% of the time), and (2) provides biased estimates of diversification-rate parameters. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers—in order to clarify whether these methods can make reliable inferences from empirical datasets—and to theoretical biologists—in order to clarify the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification. [Akaike information criterion; extinction; lineage-specific diversification rates; phylogenetic model selection; speciation.] PMID:27037081

Accurate identification of microseismic P- and S-phase arrivals using the multi-step AIC algorithm

NASA Astrophysics Data System (ADS)

Zhu, Mengbo; Wang, Liguan; Liu, Xiaoming; Zhao, Jiaxuan; Peng, Ping'an

2018-03-01

Identification of P- and S-phase arrivals is the primary work in microseismic monitoring. In this study, a new multi-step AIC algorithm is proposed. This algorithm consists of P- and S-phase arrival pickers (P-picker and S-picker). The P-picker contains three steps: in step 1, a preliminary P-phase arrival window is determined by the waveform peak. Then a preliminary P-pick is identified using the AIC algorithm. Finally, the P-phase arrival window is narrowed based on the above P-pick. Thus the P-phase arrival can be identified accurately by using the AIC algorithm again. The S-picker contains five steps: in step 1, a narrow S-phase arrival window is determined based on the P-pick and the AIC curve of amplitude biquadratic time-series. In step 2, the S-picker automatically judges whether the S-phase arrival is clear to identify. In step 3 and 4, the AIC extreme points are extracted, and the relationship between the local minimum and the S-phase arrival is researched. In step 5, the S-phase arrival is picked based on the maximum probability criterion. To evaluate of the proposed algorithm, a P- and S-picks classification criterion is also established based on a source location numerical simulation. The field data tests show a considerable improvement of the multi-step AIC algorithm in comparison with the manual picks and the original AIC algorithm. Furthermore, the technique is independent of the kind of SNR. Even in the poor-quality signal group which the SNRs are below 5, the effective picking rates (the corresponding location error is <15 m) of P- and S-phase arrivals are still up to 80.9% and 76.4% respectively.

How Well Can We Detect Lineage-Specific Diversification-Rate Shifts? A Simulation Study of Sequential AIC Methods.

PubMed

May, Michael R; Moore, Brian R

2016-11-01

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical methods for detecting shifts in the rate of lineage diversification across the branches of phylogenic trees. One of the most frequently used methods, MEDUSA, explores a set of diversification-rate models, where each model assigns branches of the phylogeny to a set of diversification-rate categories. Each model is first fit to the data, and the Akaike information criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is uncharacterized, which is a concern in light of: (1) the poor performance of the AIC as a means of choosing among models in other phylogenetic contexts; (2) the ad hoc algorithm used to visit diversification models, and; (3) errors that we reveal in the likelihood function used to fit diversification models to the phylogenetic data. Here, we perform an extensive simulation study demonstrating that MEDUSA (1) has a high false-discovery rate (on average, spurious diversification-rate shifts are identified [Formula: see text] of the time), and (2) provides biased estimates of diversification-rate parameters. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers-in order to clarify whether these methods can make reliable inferences from empirical datasets-and to theoretical biologists-in order to clarify the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification. [Akaike information criterion; extinction; lineage-specific diversification rates; phylogenetic model selection; speciation.]. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

The Impact of Various Class-Distinction Features on Model Selection in the Mixture Rasch Model

ERIC Educational Resources Information Center

Choi, In-Hee; Paek, Insu; Cho, Sun-Joo

2017-01-01

The purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations. The simulation study…

Exploring Several Methods of Groundwater Model Selection

NASA Astrophysics Data System (ADS)

Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar

2017-04-01

Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).

Selecting a distributional assumption for modelling relative densities of benthic macroinvertebrates

USGS Publications Warehouse

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

Double point source W-phase inversion: Real-time implementation and automated model selection

USGS Publications Warehouse

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.

Prediction of thoracic injury severity in frontal impacts by selected anatomical morphomic variables through model-averaged logistic regression approach.

PubMed

Zhang, Peng; Parenteau, Chantal; Wang, Lu; Holcombe, Sven; Kohoyda-Inglis, Carla; Sullivan, June; Wang, Stewart

2013-11-01

This study resulted in a model-averaging methodology that predicts crash injury risk using vehicle, demographic, and morphomic variables and assesses the importance of individual predictors. The effectiveness of this methodology was illustrated through analysis of occupant chest injuries in frontal vehicle crashes. The crash data were obtained from the International Center for Automotive Medicine (ICAM) database for calendar year 1996 to 2012. The morphomic data are quantitative measurements of variations in human body 3-dimensional anatomy. Morphomics are obtained from imaging records. In this study, morphomics were obtained from chest, abdomen, and spine CT using novel patented algorithms. A NASS-trained crash investigator with over thirty years of experience collected the in-depth crash data. There were 226 cases available with occupants involved in frontal crashes and morphomic measurements. Only cases with complete recorded data were retained for statistical analysis. Logistic regression models were fitted using all possible configurations of vehicle, demographic, and morphomic variables. Different models were ranked by the Akaike Information Criteria (AIC). An averaged logistic regression model approach was used due to the limited sample size relative to the number of variables. This approach is helpful when addressing variable selection, building prediction models, and assessing the importance of individual variables. The final predictive results were developed using this approach, based on the top 100 models in the AIC ranking. Model-averaging minimized model uncertainty, decreased the overall prediction variance, and provided an approach to evaluating the importance of individual variables. There were 17 variables investigated: four vehicle, four demographic, and nine morphomic. More than 130,000 logistic models were investigated in total. The models were characterized into four scenarios to assess individual variable contribution to injury risk. Scenario

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

USGS Publications Warehouse

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

AIC-based diffraction stacking for local earthquake locations at the Sumatran Fault (Indonesia)

NASA Astrophysics Data System (ADS)

Hendriyana, Andri; Bauer, Klaus; Muksin, Umar; Weber, Michael

2018-05-01

We present a new workflow for the localization of seismic events which is based on a diffraction stacking approach. In order to address the effects from complex source radiation patterns, we suggest to compute diffraction stacking from a characteristic function (CF) instead of stacking the original waveform data. A new CF, which is called in the following mAIC (modified from Akaike Information Criterion) is proposed. We demonstrate that both P- and S-wave onsets can be detected accurately. To avoid cross-talk between P and S waves due to inaccurate velocity models, we separate the P and S waves from the mAIC function by making use of polarization attributes. Then, the final image function is represented by the largest eigenvalue as a result of the covariance analysis between P- and S-image functions. Results from synthetic experiments show that the proposed diffraction stacking provides reliable results. The workflow of the diffraction stacking method was finally applied to local earthquake data from Sumatra, Indonesia. Recordings from a temporary network of 42 stations deployed for nine months around the Tarutung pull-apart basin were analysed. The seismic event locations resulting from the diffraction stacking method align along a segment of the Sumatran Fault. A more complex distribution of seismicity is imaged within and around the Tarutung basin. Two lineaments striking N-S were found in the centre of the Tarutung basin which support independent results from structural geology.

Vector autoregressive model approach for forecasting outflow cash in Central Java

NASA Astrophysics Data System (ADS)

hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita

2018-05-01

Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.

Variable selection with stepwise and best subset approaches

PubMed Central

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

Injuries across adolescence: an investigation using the extended adolescent injury checklist (E-AIC).

PubMed

Chapman, Rebekah; Buckley, Lisa; Sheehan, Mary

2011-08-01

Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except school-based males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC.

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data.

PubMed

Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P

2007-02-08

Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori

Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

NASA Astrophysics Data System (ADS)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Selecting among competing models of electro-optic, infrared camera system range performance

USGS Publications Warehouse

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.

Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence

PubMed Central

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

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling

NASA Astrophysics Data System (ADS)

Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.

2017-04-01

Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data

PubMed Central

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

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.

PubMed

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.

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

PubMed Central

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-01-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent’s non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent’s method is not suitable for ROKU. PMID:19936074

Comparing simple respiration models for eddy flux and dynamic chamber data

Treesearch

Andrew D. Richardson; Bobby H. Braswell; David Y. Hollinger; Prabir Burman; Eric A. Davidson; Robert S. Evans; Lawrence B. Flanagan; J. William Munger; Kathleen Savage; Shawn P. Urbanski; Steven C. Wofsy

2006-01-01

Selection of an appropriate model for respiration (R) is important for accurate gap-filling of CO2 flux data, and for partitioning measurements of net ecosystem exchange (NEE) to respiration and gross ecosystem exchange (GEE). Using cross-validation methods and a version of Akaike's Information Criterion (AIC), we evaluate a wide range of...

Using generalized linear models to estimate selectivity from short-term recoveries of tagged red drum Sciaenops ocellatus: Effects of gear, fate, and regulation period

USGS Publications Warehouse

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.

Testing and selection of cosmological models with (1+z){sup 6} corrections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szydlowski, Marek; Marc Kac Complex Systems Research Centre, Jagiellonian University, ul. Reymonta 4, 30-059 Cracow; Godlowski, Wlodzimierz

2008-02-15

In the paper we check whether the contribution of (-)(1+z){sup 6} type in the Friedmann equation can be tested. We consider some astronomical tests to constrain the density parameters in such models. We describe different interpretations of such an additional term: geometric effects of loop quantum cosmology, effects of braneworld cosmological models, nonstandard cosmological models in metric-affine gravity, and models with spinning fluid. Kinematical (or geometrical) tests based on null geodesics are insufficient to separate individual matter components when they behave like perfect fluid and scale in the same way. Still, it is possible to measure their overall effect. Wemore » use recent measurements of the coordinate distances from the Fanaroff-Riley type IIb radio galaxy data, supernovae type Ia data, baryon oscillation peak and cosmic microwave background radiation observations to obtain stronger bounds for the contribution of the type considered. We demonstrate that, while {rho}{sup 2} corrections are very small, they can be tested by astronomical observations--at least in principle. Bayesian criteria of model selection (the Bayesian factor, AIC, and BIC) are used to check if additional parameters are detectable in the present epoch. As it turns out, the {lambda}CDM model is favored over the bouncing model driven by loop quantum effects. Or, in other words, the bounds obtained from cosmography are very weak, and from the point of view of the present data this model is indistinguishable from the {lambda}CDM one.« less

Genome-wide heterogeneity of nucleotide substitution model fit.

PubMed

Arbiza, Leonardo; Patricio, Mateus; Dopazo, Hernán; Posada, David

2011-01-01

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

Performance of soil particle-size distribution models for describing deposited soils adjacent to constructed dams in the China Loess Plateau

NASA Astrophysics Data System (ADS)

Zhao, Pei; Shao, Ming-an; Horton, Robert

2011-02-01

Soil particle-size distributions (PSD) have been used to estimate soil hydraulic properties. Various parametric PSD models have been proposed to describe the soil PSD from sparse experimental data. It is important to determine which PSD model best represents specific soils. Fourteen PSD models were examined in order to determine the best model for representing the deposited soils adjacent to dams in the China Loess Plateau; these were: Skaggs (S-1, S-2, and S-3), fractal (FR), Jaky (J), Lima and Silva (LS), Morgan (M), Gompertz (G), logarithm (L), exponential (E), log-exponential (LE), Weibull (W), van Genuchten type (VG) as well as Fredlund (F) models. Four-hundred and eighty samples were obtained from soils deposited in the Liudaogou catchment. The coefficient of determination (R 2), the Akaike's information criterion (AIC), and the modified AIC (mAIC) were used. Based upon R 2 and AIC, the three- and four-parameter models were both good at describing the PSDs of deposited soils, and the LE, FR, and E models were the poorest. However, the mAIC in conjunction with R 2 and AIC results indicated that the W model was optimum for describing PSD of the deposited soils for emphasizing the effect of parameter number. This analysis was also helpful for finding out which model is the best one. Our results are applicable to the China Loess Plateau.

Using generalized linear models to estimate selectivity from short-term recoveries of tagged red drum Sciaenops ocellatus: Effects of gear, fate, and regulation period

USGS Publications Warehouse

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.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

PubMed Central

Marias, Kostas; Lambregts, Doenja M. J.; Nikiforaki, Katerina; van Heeswijk, Miriam M.; Bakers, Frans C. H.; Beets-Tan, Regina G. H.

2017-01-01

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

PubMed

Manikis, Georgios C; Marias, Kostas; Lambregts, Doenja M J; Nikiforaki, Katerina; van Heeswijk, Miriam M; Bakers, Frans C H; Beets-Tan, Regina G H; Papanikolaou, Nikolaos

2017-01-01

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion

PubMed Central

Larose, Chantal; Harel, Ofer; Kordas, Katarzyna; Dey, Dipak K.

2016-01-01

Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC. PMID:27695391

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

PubMed

Mathieu, Amélie; Vidal, Tiphaine; Jullien, Alexandra; Wu, QiongLi; Chambon, Camille; Bayol, Benoit; Cournède, Paul-Henry

2018-06-19

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

Effect of correlation on covariate selection in linear and nonlinear mixed effect models.

PubMed

Bonate, Peter L

2017-01-01

The effect of correlation among covariates on covariate selection was examined with linear and nonlinear mixed effect models. Demographic covariates were extracted from the National Health and Nutrition Examination Survey III database. Concentration-time profiles were Monte Carlo simulated where only one covariate affected apparent oral clearance (CL/F). A series of univariate covariate population pharmacokinetic models was fit to the data and compared with the reduced model without covariate. The "best" covariate was identified using either the likelihood ratio test statistic or AIC. Weight and body surface area (calculated using Gehan and George equation, 1970) were highly correlated (r = 0.98). Body surface area was often selected as a better covariate than weight, sometimes as high as 1 in 5 times, when weight was the covariate used in the data generating mechanism. In a second simulation, parent drug concentration and three metabolites were simulated from a thorough QT study and used as covariates in a series of univariate linear mixed effects models of ddQTc interval prolongation. The covariate with the largest significant LRT statistic was deemed the "best" predictor. When the metabolite was formation-rate limited and only parent concentrations affected ddQTc intervals the metabolite was chosen as a better predictor as often as 1 in 5 times depending on the slope of the relationship between parent concentrations and ddQTc intervals. A correlated covariate can be chosen as being a better predictor than another covariate in a linear or nonlinear population analysis by sheer correlation These results explain why for the same drug different covariates may be identified in different analyses. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Toward objective image quality metrics: the AIC Eval Program of the JPEG

NASA Astrophysics Data System (ADS)

Richter, Thomas; Larabi, Chaker

2008-08-01

Objective quality assessment of lossy image compression codecs is an important part of the recent call of the JPEG for Advanced Image Coding. The target of the AIC ad-hoc group is twofold: First, to receive state-of-the-art still image codecs and to propose suitable technology for standardization; and second, to study objective image quality metrics to evaluate the performance of such codes. Even tthough the performance of an objective metric is defined by how well it predicts the outcome of a subjective assessment, one can also study the usefulness of a metric in a non-traditional way indirectly, namely by measuring the subjective quality improvement of a codec that has been optimized for a specific objective metric. This approach shall be demonstrated here on the recently proposed HDPhoto format14 introduced by Microsoft and a SSIM-tuned17 version of it by one of the authors. We compare these two implementations with JPEG1 in two variations and a visual and PSNR optimal JPEG200013 implementation. To this end, we use subjective and objective tests based on the multiscale SSIM and a new DCT based metric.

The question of nonlinearity in the dose-response relation between particulate matter air pollution and mortality: can Akaike's Information Criterion be trusted to take the right turn?

PubMed

Roberts, Steven; Martin, Michael A

2006-12-15

The shape of the dose-response relation between particulate matter air pollution and mortality is crucial for public health assessment, and departures of this relation from linearity could have important regulatory consequences. A number of investigators have studied the shape of the particulate matter-mortality dose-response relation and concluded that the relation could be adequately described by a linear model. Some of these researchers examined the hypothesis of linearity by comparing Akaike's Information Criterion (AIC) values obtained under linear, piecewise linear, and spline alternative models. However, at the current time, the efficacy of the AIC in this context has not been assessed. The authors investigated AIC as a means of comparing competing dose-response models, using data from Cook County, Illinois, for the period 1987-2000. They found that if nonlinearities exist, the AIC is not always successful in detecting them. In a number of the scenarios considered, AIC was equivocal, picking the correct simulated dose-response model about half of the time. These findings suggest that further research into the shape of the dose-response relation using alternative model selection criteria may be warranted.

A study on the use and modeling of geographical information system for combating forest crimes: an assessment of crimes in the eastern Mediterranean forests.

PubMed

Pak, Mehmet; Gülci, Sercan; Okumuş, Arif

2018-01-06

This study focuses on the geo-statistical assessment of spatial estimation models in forest crimes. Used widely in the assessment of crime and crime-dependent variables, geographic information system (GIS) helps the detection of forest crimes in rural regions. In this study, forest crimes (forest encroachment, illegal use, illegal timber logging, etc.) are assessed holistically and modeling was performed with ten different independent variables in GIS environment. The research areas are three Forest Enterprise Chiefs (Baskonus, Cinarpinar, and Hartlap) affiliated to Kahramanmaras Forest Regional Directorate in Kahramanmaras. An estimation model was designed using ordinary least squares (OLS) and geographically weighted regression (GWR) methods, which are often used in spatial association. Three different models were proposed in order to increase the accuracy of the estimation model. The use of variables with a variance inflation factor (VIF) value of lower than 7.5 in Model I and lower than 4 in Model II and dependent variables with significant robust probability values in Model III are associated with forest crimes. Afterwards, the model with the lowest corrected Akaike Information Criterion (AIC c ), and the highest R 2 value was selected as the comparison criterion. Consequently, Model III proved to be more accurate compared to other models. For Model III, while AIC c was 328,491 and R 2 was 0.634 for OLS-3 model, AIC c was 318,489 and R 2 was 0.741 for GWR-3 model. In this respect, the uses of GIS for combating forest crimes provide different scenarios and tangible information that will help take political and strategic measures.

Selective anti-herpesvirus agents.

PubMed

De Clercq, Erik

2013-01-23

This review article focuses on the anti-herpesvirus agents effective against herpes simplex virus, varicella-zoster virus and cytomegalovirus, which have either been licensed for clinical use (idoxuridine, trifluridine, brivudin, acyclovir, valaciclovir, valganciclovir, famciclovir and foscarnet) or are under clinical development (CMX001 [the hexadecyloxypropyl prodrug of cidofovir], the helicase-primase inhibitor BAY 57-1293 [now referred to as AIC316], FV-100 [the valine ester of Cf 1743] and the terminase inhibitor letermovir [AIC246]).

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

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

Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

PubMed

Habibi, Danial; Rafiei, Mohammad; Chehrei, Ali; Shayan, Zahra; Tafaqodi, Soheil

2018-03-27

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. Creative Commons Attribution License

Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.

PubMed

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. Copyright © 2016 John Wiley & Sons, Ltd.

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),…

Longitudinal associations between dental caries increment and risk factors in late childhood and adolescence.

PubMed

Curtis, Alexandra M; VanBuren, John; Cavanaugh, Joseph E; Warren, John J; Marshall, Teresa A; Levy, Steven M

2018-05-12

To assess longitudinal associations between permanent tooth caries increment and both modifiable and non-modifiable risk factors, using best subsets model selection. The Iowa Fluoride Study has followed a birth cohort with standardized caries exams without radiographs of the permanent dentition conducted at about ages 9, 13, and 17 years. Questionnaires were sent semi-annually to assess fluoride exposures and intakes, select food and beverage intakes, and tooth brushing frequency. Exposure variables were averaged over ages 7-9, 11-13, and 15-17, reflecting exposure 2 years prior to the caries exam. Longitudinal models were used to relate period-specific averaged exposures and demographic variables to adjusted decayed and filled surface increments (ADJCI) (n = 392). The Akaike Information Criterion (AIC) was used to assess optimal explanatory variable combinations. From birth to age 9, 9-13, and 13-17 years, 24, 30, and 55 percent of subjects had positive permanent ADJCI, respectively. Ten models had AIC values within two units of the lowest AIC model and were deemed optimal based on AIC. Younger age, being male, higher mother's education, and higher brushing frequency were associated with lower caries increment in all 10 models, while milk intake was included in 3 of 10 models. Higher milk intakes were slightly associated with lower ADJCI. With the exception of brushing frequency, modifiable risk factors under study were not significantly associated with ADJCI. When possible, researchers should consider presenting multiple models if fit criteria cannot discern among a group of optimal models. © 2018 American Association of Public Health Dentistry.

Cautions regarding the fitting and interpretation of survival curves: examples from NICE single technology appraisals of drugs for cancer.

PubMed

Connock, Martin; Hyde, Chris; Moore, David

2011-10-01

The UK National Institute for Health and Clinical Excellence (NICE) has used its Single Technology Appraisal (STA) programme to assess several drugs for cancer. Typically, the evidence submitted by the manufacturer comes from one short-term randomized controlled trial (RCT) demonstrating improvement in overall survival and/or in delay of disease progression, and these are the pre-eminent drivers of cost effectiveness. We draw attention to key issues encountered in assessing the quality and rigour of the manufacturers' modelling of overall survival and disease progression. Our examples are two recent STAs: sorafenib (Nexavar®) for advanced hepatocellular carcinoma, and azacitidine (Vidaza®) for higher-risk myelodysplastic syndromes (MDS). The choice of parametric model had a large effect on the predicted treatment-dependent survival gain. Logarithmic models (log-Normal and log-logistic) delivered double the survival advantage that was derived from Weibull models. Both submissions selected the logarithmic fits for their base-case economic analyses and justified selection solely on Akaike Information Criterion (AIC) scores. AIC scores in the azacitidine submission failed to match the choice of the log-logistic over Weibull or exponential models, and the modelled survival in the intervention arm lacked face validity. AIC scores for sorafenib models favoured log-Normal fits; however, since there is no statistical method for comparing AIC scores, and differences may be trivial, it is generally advised that the plausibility of competing models should be tested against external data and explored in diagnostic plots. Function fitting to observed data should not be a mechanical process validated by a single crude indicator (AIC). Projective models should show clear plausibility for the patients concerned and should be consistent with other published information. Multiple rather than single parametric functions should be explored and tested with diagnostic plots. When

B-spline parameterization of the dielectric function and information criteria: the craft of non-overfitting

NASA Astrophysics Data System (ADS)

Likhachev, Dmitriy V.

2017-06-01

Johs and Hale developed the Kramers-Kronig consistent B-spline formulation for the dielectric function modeling in spectroscopic ellipsometry data analysis. In this article we use popular Akaike, corrected Akaike and Bayesian Information Criteria (AIC, AICc and BIC, respectively) to determine an optimal number of knots for B-spline model. These criteria allow finding a compromise between under- and overfitting of experimental data since they penalize for increasing number of knots and select representation which achieves the best fit with minimal number of knots. Proposed approach provides objective and practical guidance, as opposite to empirically driven or "gut feeling" decisions, for selecting the right number of knots for B-spline models in spectroscopic ellipsometry. AIC, AICc and BIC selection criteria work remarkably well as we demonstrated in several real-data applications. This approach formalizes selection of the optimal knot number and may be useful in practical perspective of spectroscopic ellipsometry data analysis.

Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion

PubMed Central

Wheeler, David C.; Hickson, DeMarc A.; Waller, Lance A.

2010-01-01

Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data. PMID:21243121

Thermal signature identification system (TheSIS): a spread spectrum temperature cycling method

NASA Astrophysics Data System (ADS)

Merritt, Scott

2015-03-01

NASA GSFC's Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.

Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

NASA Astrophysics Data System (ADS)

Miralha, L.; Kim, D.

2017-12-01

Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0

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.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

PubMed

Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W

2017-05-01

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors. © 2016, The American College of Clinical Pharmacology.

Adaptive interference cancel filter for evoked potential using high-order cumulants.

PubMed

Lin, Bor-Shyh; Lin, Bor-Shing; Chong, Fok-Ching; Lai, Feipei

2004-01-01

This paper is to present evoked potential (EP) processing using adaptive interference cancel (AIC) filter with second and high order cumulants. In conventional ensemble averaging method, people have to conduct repetitively experiments to record the required data. Recently, the use of AIC structure with second statistics in processing EP has proved more efficiency than traditional averaging method, but it is sensitive to both of the reference signal statistics and the choice of step size. Thus, we proposed higher order statistics-based AIC method to improve these disadvantages. This study was experimented in somatosensory EP corrupted with EEG. Gradient type algorithm is used in AIC method. Comparisons with AIC filter on second, third, fourth order statistics are also presented in this paper. We observed that AIC filter with third order statistics has better convergent performance for EP processing and is not sensitive to the selection of step size and reference input.

Does the choice of nucleotide substitution models matter topologically?

PubMed

Hoff, Michael; Orf, Stefan; Riehm, Benedikt; Darriba, Diego; Stamatakis, Alexandros

2016-03-24

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies. We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study. We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.

Selection for the best ETS (error, trend, seasonal) model to forecast weather in the Aceh Besar District

NASA Astrophysics Data System (ADS)

Amora Jofipasi, Chesilia; Miftahuddin; Hizir

2018-05-01

Weather is a phenomenon that occurs in certain areas that indicate a change in natural activity. Weather can be predicted using data in previous periods over a period. The purpose of this study is to get the best ETS model to predict the weather in Aceh Besar. The ETS model is a time series univariate forecasting method; its use focuses on trend and seasonal components. The data used are air temperature, dew point, sea level pressure, station pressure, visibility, wind speed, and sea surface temperature from January 2006 to December 2016. Based on AIC, AICc and BIC the smallest values obtained the conclusion that the ETS (M, N, A) is used to predict air temperature, and sea surface temperature, ETS (A, N, A) is used to predict dew point, sea level pressure and station pressure, ETS (A, A, N) is used to predict visibility, and ETS (A, N, N) is used to predict wind speed.

An Interoceptive Predictive Coding Model of Conscious Presence

PubMed Central

Seth, Anil K.; Suzuki, Keisuke; Critchley, Hugo D.

2011-01-01

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness. PMID:22291673

Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries.

PubMed

Abrahamowicz, Michal; Bartlett, Gillian; Tamblyn, Robyn; du Berger, Roxane

2006-04-01

Accurate assessment of medication impact requires modeling cumulative effects of exposure duration and dose; however, postmarketing studies usually represent medication exposure by baseline or current use only. We propose new methods for modeling various aspects of medication use history and employment of them to assess the adverse effects of selected benzodiazepines. Time-dependent measures of cumulative dose or duration of use, with weighting of past exposures by recency, were proposed. These measures were then included in alternative versions of the multivariable Cox model to analyze the risk of fall related injuries among the elderly new users of three benzodiazepines (nitrazepam, temazepam, and flurazepam) in Quebec. Akaike's information criterion (AIC) was used to select the most predictive model for a given benzodiazepine. The best-fitting model included a combination of cumulative duration and current dose for temazepam, and cumulative dose for flurazepam and nitrazepam, with different weighting functions. The window of clinically relevant exposure was shorter for flurazepam than for the two other products. Careful modeling of the medication exposure history may enhance our understanding of the mechanisms underlying their adverse effects.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

NASA Astrophysics Data System (ADS)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

A Selective Review of Group Selection in High-Dimensional Models

PubMed Central

Huang, Jian; Breheny, Patrick; Ma, Shuangge

2013-01-01

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study. PMID:24174707

Spotted Towhee population dynamics in a riparian restoration context

Treesearch

Stacy L. Small; Frank R., III Thompson; Geoffery R. Geupel; John Faaborg

2007-01-01

We investigated factors at multiple scales that might influence nest predation risk for Spotted Towhees (Pipilo maculates) along the Sacramento River, California, within the context of large-scale riparian habitat restoration. We used the logistic-exposure method and Akaike's information criterion (AIC) for model selection to compare predator...

Anterior Insular Cortex and Emotional Awareness

PubMed Central

Gu, Xiaosi; Hof, Patrick R.; Friston, Karl J.; Fan, Jin

2014-01-01

This paper reviews the foundation for a role of the human anterior insular cortex (AIC) in emotional awareness, defined as the conscious experience of emotions. We first introduce the neuroanatomical features of AIC and existing findings on emotional awareness. Using empathy, the awareness and understanding of other people’s emotional states, as a test case, we then present evidence to demonstrate: 1) AIC and anterior cingulate cortex (ACC) are commonly coactivated as revealed by a meta-analysis, 2) AIC is functionally dissociable from ACC, 3) AIC integrates stimulus-driven and top-down information, and 4) AIC is necessary for emotional awareness. We propose a model in which AIC serves two major functions: integrating bottom-up interoceptive signals with top-down predictions to generate a current awareness state and providing descending predictions to visceral systems that provide a point of reference for autonomic reflexes. We argue that AIC is critical and necessary for emotional awareness. PMID:23749500

A Primer for Model Selection: The Decisive Role of Model Complexity

NASA Astrophysics Data System (ADS)

Höge, Marvin; Wöhling, Thomas; Nowak, Wolfgang

2018-03-01

Selecting a "best" model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the "best" trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

An Investigation of State-Space Model Fidelity for SSME Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2008-01-01

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.

The Role of the Anterior Insula in Adolescent Decision Making

PubMed Central

Smith, Ashley R.; Steinberg, Laurence; Chein, Jason

2017-01-01

Much recent research on adolescent decision making has sought to characterize the neurobiological mechanisms that underlie the proclivity of adolescents to engage in risky behavior. One class of influential neurodevelopmental models focuses on the asynchronous development of neural systems, particularly those responsible for self-regulation and reward seeking. While this work has largely focused on the development of prefrontal (self-regulation) and striatal (reward processing) circuitry, the present article explores the significance of a different region, the anterior insular cortex (AIC), in adolescent decision making. Although the AIC is known for its role as a cognitive-emotional hub, and is included in some models of adult self-regulation and reward seeking, the importance of the AIC and its maturation in adolescent risk taking has not been extensively explored. In this article we discuss evidence on AIC development, and consider how age-related differences in AIC engagement may contribute to heightened risk taking during adolescence. Based on this review, we propose a model in which the engagement of adolescents in risk taking may be linked in part to the maturation of the AIC and its connectivity to the broader brain networks in which it participates. PMID:24853135

A quantitative model of optimal data selection in Wason's selection task.

PubMed

Hattori, Masasi

2002-10-01

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology

NASA Astrophysics Data System (ADS)

Tomasi, G.; Kimberley, S.; Rosso, L.; Aboagye, E.; Turkheimer, F.

2012-04-01

In positron emission tomography (PET) studies involving organs different from the brain, ignoring the metabolite contribution to the tissue time-activity curves (TAC), as in the standard single-input (SI) models, may compromise the accuracy of the estimated parameters. We employed here double-input (DI) compartmental modeling (CM), previously used for [11C]thymidine, and a novel DI spectral analysis (SA) approach on the tracers 5-[18F]fluorouracil (5-[18F]FU) and [18F]fluorothymidine ([18F]FLT). CM and SA were performed initially with a SI approach using the parent plasma TAC as an input function. These methods were then employed using a DI approach with the metabolite plasma TAC as an additional input function. Regions of interest (ROIs) corresponding to healthy liver, kidneys and liver metastases for 5-[18F]FU and to tumor, vertebra and liver for [18F]FLT were analyzed. For 5-[18F]FU, the improvement of the fit quality with the DI approaches was remarkable; in CM, the Akaike information criterion (AIC) always selected the DI over the SI model. Volume of distribution estimates obtained with DI CM and DI SA were in excellent agreement, for both parent 5-[18F]FU (R2 = 0.91) and metabolite [18F]FBAL (R2 = 0.99). For [18F]FLT, the DI methods provided notable improvements but less substantial than for 5-[18F]FU due to the lower rate of metabolism of [18F]FLT. On the basis of the AIC values, agreement between [18F]FLT Ki estimated with the SI and DI models was good (R2 = 0.75) for the ROIs where the metabolite contribution was negligible, indicating that the additional input did not bias the parent tracer only-related estimates. When the AIC suggested a substantial contribution of the metabolite [18F]FLT-glucuronide, on the other hand, the change in the parent tracer only-related parameters was significant (R2 = 0.33 for Ki). Our results indicated that improvements of DI over SI approaches can range from moderate to substantial and are more significant for tracers with

Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology.

PubMed

Tomasi, G; Kimberley, S; Rosso, L; Aboagye, E; Turkheimer, F

2012-04-07

In positron emission tomography (PET) studies involving organs different from the brain, ignoring the metabolite contribution to the tissue time-activity curves (TAC), as in the standard single-input (SI) models, may compromise the accuracy of the estimated parameters. We employed here double-input (DI) compartmental modeling (CM), previously used for [¹¹C]thymidine, and a novel DI spectral analysis (SA) approach on the tracers 5-[¹⁸F]fluorouracil (5-[¹⁸F]FU) and [¹⁸F]fluorothymidine ([¹⁸F]FLT). CM and SA were performed initially with a SI approach using the parent plasma TAC as an input function. These methods were then employed using a DI approach with the metabolite plasma TAC as an additional input function. Regions of interest (ROIs) corresponding to healthy liver, kidneys and liver metastases for 5-[¹⁸F]FU and to tumor, vertebra and liver for [¹⁸F]FLT were analyzed. For 5-[¹⁸F]FU, the improvement of the fit quality with the DI approaches was remarkable; in CM, the Akaike information criterion (AIC) always selected the DI over the SI model. Volume of distribution estimates obtained with DI CM and DI SA were in excellent agreement, for both parent 5-[¹⁸F]FU (R(2) = 0.91) and metabolite [¹⁸F]FBAL (R(2) = 0.99). For [¹⁸F]FLT, the DI methods provided notable improvements but less substantial than for 5-[¹⁸F]FU due to the lower rate of metabolism of [¹⁸F]FLT. On the basis of the AIC values, agreement between [¹⁸F]FLT K(i) estimated with the SI and DI models was good (R² = 0.75) for the ROIs where the metabolite contribution was negligible, indicating that the additional input did not bias the parent tracer only-related estimates. When the AIC suggested a substantial contribution of the metabolite [¹⁸F]FLT-glucuronide, on the other hand, the change in the parent tracer only-related parameters was significant (R² = 0.33 for K(i)). Our results indicated that improvements of DI over SI approaches can range from moderate to

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

USGS Publications Warehouse

Row, Jeffrey R.; Knick, Steven T.; Oyler-McCance, Sara J.; Lougheed, Stephen C.; Fedy, Bradley C.

2017-01-01

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.

Bayes factors and multimodel inference

USGS Publications Warehouse

Link, W.A.; Barker, R.J.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Identification and synthetic modeling of factors affecting American black duck populations

USGS Publications Warehouse

Conroy, Michael J.; Miller, Mark W.; Hines, James E.

2002-01-01

AIC weight (0.70) predicted black duck age ratios increasing as a function of decreasing mallard abundance and increasing acreage of breeding habitat; all models considered involved negative density dependence for black ducks. The survival model with highest AIC weight (0.51) predicted nonharvest survival increasing as a function of increasing acreage of wintering habitat and decreasing harvest rates (additive mortality); models involving compensatory mortality effects received ≈0.12 total weight, vs. 0.88 for additive models. We used the combined model, together with our historical data set, to perform a series of 1-year population forecasts, similar to those that might be performed under adaptive management. Initial model forecasts over-predicted observed breeding populations by ≈25%. Least-squares calibration reduced the bias to ≈0.5% under prediction. After calibration, model-averaged predictions over the 16 alternative models (4 reproduction × 4 survival, weighted by AIC model weights) explained 67% of the variation in annual breeding population abundance for black ducks, suggesting that it might have utility as a predictive tool in adaptive management. We investigated the effects of statistical uncertainty in parameter values on predicted population growth rates for the combined annual model, via sensitivity analyses. Parameter sensitivity varied in relation to the parameter values over the estimated confidence intervals, and in relation to harvest rates and mallard abundance. Forecasts of black duck abundance were extremely sensitive to variation in parameter values for the coefficients for breeding and wintering habitat effects. Model-averaged forecasts of black duck abundance were also sensitive to changes in harvest rate and mallard abundance, with rapid declines in black duck abundance predicted for a range of harvest rates and mallard abundance higher than current levels of either factor, but easily envisaged, particularly given current rates of

Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence.

PubMed

Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

2008-04-01

This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data.

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Discrimination of numerical proportions: A comparison of binomial and Gaussian models.

PubMed

Raidvee, Aire; Lember, Jüri; Allik, Jüri

2017-01-01

Observers discriminated the numerical proportion of two sets of elements (N = 9, 13, 33, and 65) that differed either by color or orientation. According to the standard Thurstonian approach, the accuracy of proportion discrimination is determined by irreducible noise in the nervous system that stochastically transforms the number of presented visual elements onto a continuum of psychological states representing numerosity. As an alternative to this customary approach, we propose a Thurstonian-binomial model, which assumes discrete perceptual states, each of which is associated with a certain visual element. It is shown that the probability β with which each visual element can be noticed and registered by the perceptual system can explain data of numerical proportion discrimination at least as well as the continuous Thurstonian-Gaussian model, and better, if the greater parsimony of the Thurstonian-binomial model is taken into account using AIC model selection. We conclude that Gaussian and binomial models represent two different fundamental principles-internal noise vs. using only a fraction of available information-which are both plausible descriptions of visual perception.

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this

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.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River, Brazil).

PubMed

Rocha, R R A; Thomaz, S M; Carvalho, P; Gomes, L C

2009-06-01

The need for prediction is widely recognized in limnology. In this study, data from 25 lakes of the Upper Paraná River floodplain were used to build models to predict chlorophyll-a and dissolved oxygen concentrations. Akaike's information criterion (AIC) was used as a criterion for model selection. Models were validated with independent data obtained in the same lakes in 2001. Predictor variables that significantly explained chlorophyll-a concentration were pH, electrical conductivity, total seston (positive correlation) and nitrate (negative correlation). This model explained 52% of chlorophyll variability. Variables that significantly explained dissolved oxygen concentration were pH, lake area and nitrate (all positive correlations); water temperature and electrical conductivity were negatively correlated with oxygen. This model explained 54% of oxygen variability. Validation with independent data showed that both models had the potential to predict algal biomass and dissolved oxygen concentration in these lakes. These findings suggest that multiple regression models are valuable and practical tools for understanding the dynamics of ecosystems and that predictive limnology may still be considered a powerful approach in aquatic ecology.

The Coalescent Process in Models with Selection

PubMed Central

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

Bayesian Model Selection under Time Constraints

NASA Astrophysics Data System (ADS)

Hoege, M.; Nowak, W.; Illman, W. A.

2017-12-01

Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

Modeling Dark Energy Through AN Ising Fluid with Network Interactions

NASA Astrophysics Data System (ADS)

Luongo, Orlando; Tommasini, Damiano

2014-12-01

We show that the dark energy (DE) effects can be modeled by using an Ising perfect fluid with network interactions, whose low redshift equation of state (EoS), i.e. ω0, becomes ω0 = -1 as in the ΛCDM model. In our picture, DE is characterized by a barotropic fluid on a lattice in the equilibrium configuration. Thus, mimicking the spin interaction by replacing the spin variable with an occupational number, the pressure naturally becomes negative. We find that the corresponding EoS mimics the effects of a variable DE term, whose limiting case reduces to the cosmological constant Λ. This permits us to avoid the introduction of a vacuum energy as DE source by hand, alleviating the coincidence and fine tuning problems. We find fairly good cosmological constraints, by performing three tests with supernovae Ia (SNeIa), baryonic acoustic oscillation (BAO) and cosmic microwave background (CMB) measurements. Finally, we perform the Akaike information criterion (AIC) and Bayesian information criterion (BIC) selection criteria, showing that our model is statistically favored with respect to the Chevallier-Polarsky-Linder (CPL) parametrization.

A water quality index model using stepwise regression and neural networks models for the Piabanha River basin in Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Villas Boas, M. D.; Olivera, F.; Azevedo, J. S.

2013-12-01

The evaluation of water quality through 'indexes' is widely used in environmental sciences. There are a number of methods available for calculating water quality indexes (WQI), usually based on site-specific parameters. In Brazil, WQI were initially used in the 1970s and were adapted from the methodology developed in association with the National Science Foundation (Brown et al, 1970). Specifically, the WQI 'IQA/SCQA', developed by the Institute of Water Management of Minas Gerais (IGAM), is estimated based on nine parameters: Temperature Range, Biochemical Oxygen Demand, Fecal Coliforms, Nitrate, Phosphate, Turbidity, Dissolved Oxygen, pH and Electrical Conductivity. The goal of this study was to develop a model for calculating the IQA/SCQA, for the Piabanha River basin in the State of Rio de Janeiro (Brazil), using only the parameters measurable by a Multiparameter Water Quality Sonde (MWQS) available in the study area. These parameters are: Dissolved Oxygen, pH and Electrical Conductivity. The use of this model will allow to further the water quality monitoring network in the basin, without requiring significant increases of resources. The water quality measurement with MWQS is less expensive than the laboratory analysis required for the other parameters. The water quality data used in the study were obtained by the Geological Survey of Brazil in partnership with other public institutions (i.e. universities and environmental institutes) as part of the project "Integrated Studies in Experimental and Representative Watersheds". Two models were developed to correlate the values of the three measured parameters and the IQA/SCQA values calculated based on all nine parameters. The results were evaluated according to the following validation statistics: coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike information criterion (AIC) and Final Prediction Error (FPE). The first model was a linear stepwise regression between three independent variables

The genealogy of samples in models with selection.

PubMed

Neuhauser, C; Krone, S M

1997-02-01

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.

The Genealogy of Samples in Models with Selection

PubMed Central

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

Adaptive Modeling Procedure Selection by Data Perturbation.

PubMed

Zhang, Yongli; Shen, Xiaotong

2015-10-01

Many procedures have been developed to deal with the high-dimensional problem that is emerging in various business and economics areas. To evaluate and compare these procedures, modeling uncertainty caused by model selection and parameter estimation has to be assessed and integrated into a modeling process. To do this, a data perturbation method estimates the modeling uncertainty inherited in a selection process by perturbing the data. Critical to data perturbation is the size of perturbation, as the perturbed data should resemble the original dataset. To account for the modeling uncertainty, we derive the optimal size of perturbation, which adapts to the data, the model space, and other relevant factors in the context of linear regression. On this basis, we develop an adaptive data-perturbation method that, unlike its nonadaptive counterpart, performs well in different situations. This leads to a data-adaptive model selection method. Both theoretical and numerical analysis suggest that the data-adaptive model selection method adapts to distinct situations in that it yields consistent model selection and optimal prediction, without knowing which situation exists a priori. The proposed method is applied to real data from the commodity market and outperforms its competitors in terms of price forecasting accuracy.

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…

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.

Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.

PubMed

Spielman, Stephanie J; Wilke, Claus O

2016-11-01

The mutation-selection model of coding sequence evolution has received renewed attention for its use in estimating site-specific amino acid propensities and selection coefficient distributions. Two computationally tractable mutation-selection inference frameworks have been introduced: One framework employs a fixed-effects, highly parameterized maximum likelihood approach, whereas the other employs a random-effects Bayesian Dirichlet Process approach. While both implementations follow the same model, they appear to make distinct predictions about the distribution of selection coefficients. The fixed-effects framework estimates a large proportion of highly deleterious substitutions, whereas the random-effects framework estimates that all substitutions are either nearly neutral or weakly deleterious. It remains unknown, however, how accurately each method infers evolutionary constraints at individual sites. Indeed, selection coefficient distributions pool all site-specific inferences, thereby obscuring a precise assessment of site-specific estimates. Therefore, in this study, we use a simulation-based strategy to determine how accurately each approach recapitulates the selective constraint at individual sites. We find that the fixed-effects approach, despite its extensive parameterization, consistently and accurately estimates site-specific evolutionary constraint. By contrast, the random-effects Bayesian approach systematically underestimates the strength of natural selection, particularly for slowly evolving sites. We also find that, despite the strong differences between their inferred selection coefficient distributions, the fixed- and random-effects approaches yield surprisingly similar inferences of site-specific selective constraint. We conclude that the fixed-effects mutation-selection framework provides the more reliable software platform for model application and future development. © The Author 2016. Published by Oxford University Press on behalf of the

Spatio-temporal Bayesian model selection for disease mapping

PubMed Central

Carroll, R; Lawson, AB; Faes, C; Kirby, RS; Aregay, M; Watjou, K

2016-01-01

Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor. PMID:28070156

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

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

Functional response models to estimate feeding rates of wading birds

USGS Publications Warehouse

Collazo, J.A.; Gilliam, J.F.; Miranda-Castro, L.

2010-01-01

Forager (predator) abundance may mediate feeding rates in wading birds. Yet, when modeled, feeding rates are typically derived from the purely prey-dependent Holling Type II (HoII) functional response model. Estimates of feeding rates are necessary to evaluate wading bird foraging strategies and their role in food webs; thus, models that incorporate predator dependence warrant consideration. Here, data collected in a mangrove swamp in Puerto Rico in 1994 were reanalyzed, reporting feeding rates for mixed-species flocks after comparing fits of the HoII model, as used in the original work, to the Beddington-DeAngelis (BD) and Crowley-Martin (CM) predator-dependent models. Model CM received most support (AIC c wi = 0.44), but models BD and HoII were plausible alternatives (AIC c ??? 2). Results suggested that feeding rates were constrained by predator abundance. Reductions in rates were attributed to interference, which was consistent with the independently observed increase in aggression as flock size increased (P < 0.05). Substantial discrepancies between the CM and HoII models were possible depending on flock sizes used to model feeding rates. However, inferences derived from the HoII model, as used in the original work, were sound. While Holling's Type II and other purely prey-dependent models have fostered advances in wading bird foraging ecology, evaluating models that incorporate predator dependence could lead to a more adequate description of data and processes of interest. The mechanistic bases used to derive models used here lead to biologically interpretable results and advance understanding of wading bird foraging ecology.

On spatial mutation-selection models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de; Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu; Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

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.

MMA, A Computer Code for Multi-Model Analysis

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2007-01-01

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will

VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS

PubMed Central

Huang, Jian; Horowitz, Joel L.; Wei, Fengrong

2010-01-01

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is “small” relative to the sample size. The statistical problem is to determine which additive components are nonzero. The additive components are approximated by truncated series expansions with B-spline bases. With this approximation, the problem of component selection becomes that of selecting the groups of coefficients in the expansion. We apply the adaptive group Lasso to select nonzero components, using the group Lasso to obtain an initial estimator and reduce the dimension of the problem. We give conditions under which the group Lasso selects a model whose number of components is comparable with the underlying model, and the adaptive group Lasso selects the nonzero components correctly with probability approaching one as the sample size increases and achieves the optimal rate of convergence. The results of Monte Carlo experiments show that the adaptive group Lasso procedure works well with samples of moderate size. A data example is used to illustrate the application of the proposed method. PMID:21127739

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…

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

PubMed Central

Beck, Cornelia; Neumann, Heiko

2011-01-01

Background The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance We propose a new neural model for MT pattern computation and motion disambiguation that is based on a combination of feature selection and integration. The model can explain a range of recent neurophysiological findings including temporally dynamic behaviour. PMID:21814543

VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA

PubMed Central

Garcia, Ramon I.; Ibrahim, Joseph G.; Zhu, Hongtu

2009-01-01

We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simultaneously optimizing the penalized likelihood function and estimating the penalty parameters. Particularly, we propose to use a model selection criterion, called the ICQ statistic, for selecting the penalty parameters. We show that the variable selection procedure based on ICQ automatically and consistently selects the important covariates and leads to efficient estimates with oracle properties. The methodology is very general and can be applied to numerous situations involving missing data, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Simulations are given to demonstrate the methodology and examine the finite sample performance of the variable selection procedures. Melanoma data from a cancer clinical trial is presented to illustrate the proposed methodology. PMID:20336190

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

A guide to Bayesian model selection for ecologists

USGS Publications Warehouse

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.

A Computational Model of Selection by Consequences

ERIC Educational Resources Information Center

McDowell, J. J.

2004-01-01

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of…

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…

Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics

PubMed Central

Dybowski, Richard; McKinley, Trevelyan J.; Mastroeni, Pietro; Restif, Olivier

2013-01-01

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered. PMID:24376528

A Parameter Subset Selection Algorithm for Mixed-Effects Models

DOE PAGES

Schmidt, Kathleen L.; Smith, Ralph C.

2016-01-01

Mixed-effects models are commonly used to statistically model phenomena that include attributes associated with a population or general underlying mechanism as well as effects specific to individuals or components of the general mechanism. This can include individual effects associated with data from multiple experiments. However, the parameterizations used to incorporate the population and individual effects are often unidentifiable in the sense that parameters are not uniquely specified by the data. As a result, the current literature focuses on model selection, by which insensitive parameters are fixed or removed from the model. Model selection methods that employ information criteria are applicablemore » to both linear and nonlinear mixed-effects models, but such techniques are limited in that they are computationally prohibitive for large problems due to the number of possible models that must be tested. To limit the scope of possible models for model selection via information criteria, we introduce a parameter subset selection (PSS) algorithm for mixed-effects models, which orders the parameters by their significance. In conclusion, we provide examples to verify the effectiveness of the PSS algorithm and to test the performance of mixed-effects model selection that makes use of parameter subset selection.« less

Using multilevel models to quantify heterogeneity in resource selection

USGS Publications Warehouse

Wagner, Tyler; Diefenbach, Duane R.; Christensen, Sonja; Norton, Andrew S.

2011-01-01

Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection.

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…

Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models.

PubMed

Vermaat, J S; van der Tweel, I; Mehra, N; Sleijfer, S; Haanen, J B; Roodhart, J M; Engwegen, J Y; Korse, C M; Langenberg, M H; Kruit, W; Groenewegen, G; Giles, R H; Schellens, J H; Beijnen, J H; Voest, E E

2010-07-01

In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting

Elementary Teachers' Selection and Use of Visual Models

NASA Astrophysics Data System (ADS)

Lee, Tammy D.; Gail Jones, M.

2018-02-01

As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

PubMed Central

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 HIV-1 drug resistance as episodic directional selection.

PubMed

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.

Antibiotic-Induced Depletion of Anti-inflammatory Clostridia Is Associated with the Development of Graft-versus-Host Disease in Pediatric Stem Cell Transplantation Patients.

PubMed

Simms-Waldrip, Tiffany R; Sunkersett, Gauri; Coughlin, Laura A; Savani, Milan R; Arana, Carlos; Kim, Jiwoong; Kim, Minsoo; Zhan, Xiaowei; Greenberg, David E; Xie, Yang; Davies, Stella M; Koh, Andrew Y

2017-05-01

Adult stem cell transplantation (SCT) patients with graft-versus-host-disease (GVHD) exhibit significant disruptions in gut microbial communities. These changes are associated with higher overall mortality and appear to be driven by specific antibiotic therapies. It is unclear whether pediatric SCT patients who develop GVHD exhibit similar antibiotic-induced gut microbiota community changes. Here, we show that pediatric SCT patients (from Children's Medical Center Dallas, n = 8, and Cincinnati Children's Hospital, n = 7) who developed GVHD showed a significant decline, up to 10-log fold, in gut anti-inflammatory Clostridia (AIC) compared with those without GVHD. In fact, the development of GVHD is significantly associated with this AIC decline and with cumulative antibiotic exposure, particularly antibiotics effective against anaerobic bacteria (P = .003, Firth logistic regression analysis). Using metagenomic shotgun sequencing analysis, we were able to identify specific commensal bacterial species, including AIC, that were significantly depleted in GVHD patients. We then used a preclinical GVHD model to verify our clinical observations. Clindamycin depleted AIC and exacerbated GVHD in mice, whereas oral AIC supplementation increased gut AIC levels and mitigated GVHD in mice. Together, these data suggest that an antibiotic-induced AIC depletion in the gut microbiota is associated with the development of GVHD in pediatric SCT patients. Copyright © 2017 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Simultaneous optimization of limited sampling points for pharmacokinetic analysis of amrubicin and amrubicinol in cancer patients.

PubMed

Makino, Yoshinori; Watanabe, Michiko; Makihara, Reiko Ando; Nokihara, Hiroshi; Yamamoto, Noboru; Ohe, Yuichiro; Sugiyama, Erika; Sato, Hitoshi; Hayashi, Yoshikazu

2016-09-01

Limited sampling points for both amrubicin (AMR) and its active metabolite amrubicinol (AMR-OH) were simultaneously optimized using Akaike's information criterion (AIC) calculated by pharmacokinetic modeling. In this pharmacokinetic study, 40 mg/m(2) of AMR was administered as a 5-min infusion on three consecutive days to 21 Japanese lung cancer patients. Blood samples were taken at 0, 0.08, 0.25, 0.5, 1, 2, 4, 8 and 24 h after drug infusion, and AMR and AMR-OH concentrations in plasma were quantitated using a high-performance liquid chromatography. The pharmacokinetic profile of AMR was characterized using a three-compartment model and that of AMR-OH using a one-compartment model following a first-order absorption process. These pharmacokinetic profiles were then integrated into one pharmacokinetic model for simultaneous fitting of AMR and AMR-OH. After fitting to the pharmacokinetic model, 65 combinations of four sampling points from the concentration profiles were evaluated for their AICs. Stepwise regression analysis was applied to select the sampling points for AMR and AMR-OH to predict the area under the concentration-time curves (AUCs) at best. Of the three combinations that yielded favorable AIC values, 0.25, 2, 4 and 8 h yielded the best AUC prediction for both AMR (R(2) = 0.977) and AMR-OH (R(2) = 0.886). The prediction error for AUC was less than 15%. The optimal limited sampling points of AMR and AMR-OH after AMR infusion were found to be 0.25, 2, 4 and 8 h, enabling less frequent blood sampling in further expanded pharmacokinetic studies for both AMR and AMR-OH. © 2016 John Wiley & Sons Australia, Ltd.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in

Analysis of survival in breast cancer patients by using different parametric models

NASA Astrophysics Data System (ADS)

Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

2017-09-01

In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

Modelling road accidents: An approach using structural time series

NASA Astrophysics Data System (ADS)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

PubMed Central

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

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.

Cross-validation to select Bayesian hierarchical models in phylogenetics.

PubMed

Duchêne, Sebastián; Duchêne, David A; Di Giallonardo, Francesca; Eden, John-Sebastian; Geoghegan, Jemma L; Holt, Kathryn E; Ho, Simon Y W; Holmes, Edward C

2016-05-26

Recent developments in Bayesian phylogenetic models have increased the range of inferences that can be drawn from molecular sequence data. Accordingly, model selection has become an important component of phylogenetic analysis. Methods of model selection generally consider the likelihood of the data under the model in question. In the context of Bayesian phylogenetics, the most common approach involves estimating the marginal likelihood, which is typically done by integrating the likelihood across model parameters, weighted by the prior. Although this method is accurate, it is sensitive to the presence of improper priors. We explored an alternative approach based on cross-validation that is widely used in evolutionary analysis. This involves comparing models according to their predictive performance. We analysed simulated data and a range of viral and bacterial data sets using a cross-validation approach to compare a variety of molecular clock and demographic models. Our results show that cross-validation can be effective in distinguishing between strict- and relaxed-clock models and in identifying demographic models that allow growth in population size over time. In most of our empirical data analyses, the model selected using cross-validation was able to match that selected using marginal-likelihood estimation. The accuracy of cross-validation appears to improve with longer sequence data, particularly when distinguishing between relaxed-clock models. Cross-validation is a useful method for Bayesian phylogenetic model selection. This method can be readily implemented even when considering complex models where selecting an appropriate prior for all parameters may be difficult.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Pleiotropic Models of Polygenic Variation, Stabilizing Selection, and Epistasis

PubMed Central

Gavrilets, S.; de-Jong, G.

1993-01-01

We show that in polymorphic populations many polygenic traits pleiotropically related to fitness are expected to be under apparent ``stabilizing selection'' independently of the real selection acting on the population. This occurs, for example, if the genetic system is at a stable polymorphic equilibrium determined by selection and the nonadditive contributions of the loci to the trait value either are absent, or are random and independent of those to fitness. Stabilizing selection is also observed if the polygenic system is at an equilibrium determined by a balance between selection and mutation (or migration) when both additive and nonadditive contributions of the loci to the trait value are random and independent of those to fitness. We also compare different viability models that can maintain genetic variability at many loci with respect to their ability to account for the strong stabilizing selection on an additive trait. Let V(m) be the genetic variance supplied by mutation (or migration) each generation, V(g) be the genotypic variance maintained in the population, and n be the number of the loci influencing fitness. We demonstrate that in mutation (migration)-selection balance models the strength of apparent stabilizing selection is order V(m)/V(g). In the overdominant model and in the symmetric viability model the strength of apparent stabilizing selection is approximately 1/(2n) that of total selection on the whole phenotype. We show that a selection system that involves pairwise additive by additive epistasis in maintaining variability can lead to a lower genetic load and genetic variance in fitness (approximately 1/(2n) times) than an equivalent selection system that involves overdominance. We show that, in the epistatic model, the apparent stabilizing selection on an additive trait can be as strong as the total selection on the whole phenotype. PMID:8325491

A Matched Cohort Study of Patients With End-Stage Heart Failure from Anthracycline-Induced Cardiomyopathy Requiring Advanced Cardiac Support.

PubMed

Thomas, Garry R; McDonald, Michael A; Day, Jennifer; Ross, Heather J; Delgado, Diego H; Billia, Filio; Butany, Jagdish W; Rao, Vivek; Amir, Eitan; Bedard, Philippe L; Thavendiranathan, Paaladinesh

2016-11-15

Anthracycline-induced cardiomyopathy (AIC) may progress to end-stage heart failure requiring mechanical circulatory support or orthotopic heart transplantation (OHT). Previous studies have described important clinical differences between AIC and nonischemic cardiomyopathy (NIC) cohorts requiring these advanced interventions. Therefore, we sought to extend this literature by comparing echocardiographic parameters, treatment strategies, and the prognosis between matched patients from these cohorts. This is a retrospective matched cohort study. All patients who received a ventricular assist device or OHT at a large Canadian center were reviewed (n = 421; 1988 to 2015) and subjects with clinical and pathologic evidence of AIC were included (n = 17, 4.0%). A comparison cohort with idiopathic NIC from the same database, matched 3:1 for age, gender, ethnicity, and year of heart failure onset was selected. The Mann-Whitney rank-sum and Fisher's exact tests were used for comparisons. Patients with AIC were predominantly women (70.6%) with heart failure diagnosed at age 40.2 ± 15.8 and 8.3 ± 8.9 years after anthracycline treatment. Compared with NIC, no differences were seen in co-morbidities, echocardiographic measures, the proportion of patients receiving a defibrillator, ventricular assist device, or OHT, the incidence of graft failure, and all-cause mortality. In contrast to other studies, AIC was not associated with a higher incidence of right ventricular dysfunction. A greater proportion of patients with AIC developed cancer (recurrence or new primary) post-OHT (21.4% vs 2.3%, p = 0.042). In conclusion, we demonstrate that when matched cohorts of patients with end-stage heart failure secondary to AIC and idiopathic NIC are compared, they are similar with respect to co-morbidities, degree of ventricular dysfunction, and advanced therapeutics used. The prognosis with OHT is also similar. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Model Selection in Systems Biology Depends on Experimental Design

PubMed Central

Silk, Daniel; Kirk, Paul D. W.; Barnes, Chris P.; Toni, Tina; Stumpf, Michael P. H.

2014-01-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis. PMID:24922483

Model selection in systems biology depends on experimental design.

PubMed

Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H

2014-06-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.

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.

A computational model of selection by consequences.

PubMed

McDowell, J J

2004-05-01

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior.

A computational model of selection by consequences.

PubMed Central

McDowell, J J

2004-01-01

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior. PMID:15357512

Model selection and assessment for multi­-species occupancy models

USGS Publications Warehouse

Broms, Kristin M.; Hooten, Mevin B.; Fitzpatrick, Ryan M.

2016-01-01

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk (Tamarix spp.) Habitat in the Western USA

USGS Publications Warehouse

Graham, Jim; Young, Nick; Jarnevich, Catherine S.; Newman, Greg; Evangelista, Paul; Stohlgren, Thomas J.

2013-01-01

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk (Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009-2013.

PubMed

Dang, Tran Ngoc; Seposo, Xerxes T; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11-1.83; low temperature effect, RR=2.0, 95% CI=1.13-3.52), females (low temperature effect, RR=2.19, 95% CI=1.14-4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91-6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15-2.22; low temperature effect, RR=1.99, 95% CI=0.92-4.28). In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City.

Time series ARIMA models for daily price of palm oil

NASA Astrophysics Data System (ADS)

Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

2015-02-01

Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

Robust Decision-making Applied to Model Selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 eachmore » 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.« less

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.

Model Selection for Monitoring CO2 Plume during Sequestration

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 finalmore » 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

Model selection for clustering of pharmacokinetic responses.

PubMed

Guerra, Rui P; Carvalho, Alexandra M; Mateus, Paulo

2018-08-01

Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community. Copyright © 2018 Elsevier B.V. All rights reserved.

Bayesian model evidence as a model evaluation metric

NASA Astrophysics Data System (ADS)

Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang

2017-04-01

When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e., which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. Criteria for evaluation often address aspects of accuracy (absence of bias) or of precision (absence of unnecessary variance) and need to be combined in a meaningful way in order to address the inherent bias-variance dilemma. We suggest using Bayesian model evidence (BME) as a model evaluation metric that implicitly performs a tradeoff between bias and variance. BME is typically associated with model weights in the context of Bayesian model averaging (BMA). However, it can also be seen as a model evaluation metric in a single-model context or in model comparison. It combines a measure for goodness of fit with a penalty for unjustifiable complexity. Unjustifiable refers to the fact that the appropriate level of model complexity is limited by the amount of information available for calibration. Derived in a Bayesian context, BME naturally accounts for measurement errors in the calibration data as well as for input and parameter uncertainty. BME is therefore perfectly suitable to assess model quality under uncertainty. We will explain in detail and with schematic illustrations what BME measures, i.e. how complexity is defined in the Bayesian setting and how this complexity is balanced with goodness of fit. We will further discuss how BME compares to other model evaluation metrics that address accuracy and precision such as the predictive logscore or other model selection criteria such as the AIC, BIC or KIC. Although computationally more expensive than other metrics or criteria, BME represents an appealing alternative because it provides a global measure of model quality. Even if not applicable to each and every case, we aim

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…

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

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate 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…

Response to Selection in Finite Locus Models with Nonadditive Effects.

PubMed

Esfandyari, Hadi; Henryon, Mark; Berg, Peer; Thomasen, Jørn Rind; Bijma, Piter; Sørensen, Anders Christian

2017-05-01

Under the finite-locus model in the absence of mutation, the additive genetic variation is expected to decrease when directional selection is acting on a population, according to quantitative-genetic theory. However, some theoretical studies of selection suggest that the level of additive variance can be sustained or even increased when nonadditive genetic effects are present. We tested the hypothesis that finite-locus models with both additive and nonadditive genetic effects maintain more additive genetic variance (VA) and realize larger medium- to long-term genetic gains than models with only additive effects when the trait under selection is subject to truncation selection. Four genetic models that included additive, dominance, and additive-by-additive epistatic effects were simulated. The simulated genome for individuals consisted of 25 chromosomes, each with a length of 1 M. One hundred bi-allelic QTL, 4 on each chromosome, were considered. In each generation, 100 sires and 100 dams were mated, producing 5 progeny per mating. The population was selected for a single trait (h2 = 0.1) for 100 discrete generations with selection on phenotype or BLUP-EBV. VA decreased with directional truncation selection even in presence of nonadditive genetic effects. Nonadditive effects influenced long-term response to selection and among genetic models additive gene action had highest response to selection. In addition, in all genetic models, BLUP-EBV resulted in a greater fixation of favorable and unfavorable alleles and higher response than phenotypic selection. In conclusion, for the schemes we simulated, the presence of nonadditive genetic effects had little effect in changes of additive variance and VA decreased by directional selection. © The American Genetic Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Quantum lattice model solver HΦ

NASA Astrophysics Data System (ADS)

Kawamura, Mitsuaki; Yoshimi, Kazuyoshi; Misawa, Takahiro; Yamaji, Youhei; Todo, Synge; Kawashima, Naoki

2017-08-01

HΦ [aitch-phi ] is a program package based on the Lanczos-type eigenvalue solution applicable to a broad range of quantum lattice models, i.e., arbitrary quantum lattice models with two-body interactions, including the Heisenberg model, the Kitaev model, the Hubbard model and the Kondo-lattice model. While it works well on PCs and PC-clusters, HΦ also runs efficiently on massively parallel computers, which considerably extends the tractable range of the system size. In addition, unlike most existing packages, HΦ supports finite-temperature calculations through the method of thermal pure quantum (TPQ) states. In this paper, we explain theoretical background and user-interface of HΦ. We also show the benchmark results of HΦ on supercomputers such as the K computer at RIKEN Advanced Institute for Computational Science (AICS) and SGI ICE XA (Sekirei) at the Institute for the Solid State Physics (ISSP).

Linear and curvilinear correlations of brain gray matter volume and density with age using voxel-based morphometry with the Akaike information criterion in 291 healthy children.

PubMed

Taki, Yasuyuki; Hashizume, Hiroshi; Thyreau, Benjamin; Sassa, Yuko; Takeuchi, Hikaru; Wu, Kai; Kotozaki, Yuka; Nouchi, Rui; Asano, Michiko; Asano, Kohei; Fukuda, Hiroshi; Kawashima, Ryuta

2013-08-01

We examined linear and curvilinear correlations of gray matter volume and density in cortical and subcortical gray matter with age using magnetic resonance images (MRI) in a large number of healthy children. We applied voxel-based morphometry (VBM) and region-of-interest (ROI) analyses with the Akaike information criterion (AIC), which was used to determine the best-fit model by selecting which predictor terms should be included. We collected data on brain structural MRI in 291 healthy children aged 5-18 years. Structural MRI data were segmented and normalized using a custom template by applying the diffeomorphic anatomical registration using exponentiated lie algebra (DARTEL) procedure. Next, we analyzed the correlations of gray matter volume and density with age in VBM with AIC by estimating linear, quadratic, and cubic polynomial functions. Several regions such as the prefrontal cortex, the precentral gyrus, and cerebellum showed significant linear or curvilinear correlations between gray matter volume and age on an increasing trajectory, and between gray matter density and age on a decreasing trajectory in VBM and ROI analyses with AIC. Because the trajectory of gray matter volume and density with age suggests the progress of brain maturation, our results may contribute to clarifying brain maturation in healthy children from the viewpoint of brain structure. Copyright © 2012 Wiley Periodicals, Inc.

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

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

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the

Distribution pattern of public transport passenger in Yogyakarta, Indonesia

NASA Astrophysics Data System (ADS)

Narendra, Alfa; Malkhamah, Siti; Sopha, Bertha Maya

2018-03-01

The arrival and departure distribution pattern of Trans Jogja bus passenger is one of the fundamental model for simulation. The purpose of this paper is to build models of passengers flows. This research used passengers data from January to May 2014. There is no policy that change the operation system affecting the nature of this pattern nowadays. The roads, buses, land uses, schedule, and people are relatively still the same. The data then categorized based on the direction, days, and location. Moreover, each category was fitted into some well-known discrete distributions. Those distributions are compared based on its AIC value and BIC. The chosen distribution model has the smallest AIC and BIC value and the negative binomial distribution found has the smallest AIC and BIC value. Probability mass function (PMF) plots of those models were compared to draw generic model from each categorical negative binomial distribution models. The value of accepted generic negative binomial distribution is 0.7064 and 1.4504 of mu. The minimum and maximum passenger vector value of distribution are is 0 and 41.

A Comparison of Dose-Response Models for the Parotid Gland in a Large Group of Head-and-Neck Cancer Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Houweling, Antonetta C., E-mail: A.Houweling@umcutrecht.n; Philippens, Marielle E.P.; Dijkema, Tim

2010-03-15

Purpose: The dose-response relationship of the parotid gland has been described most frequently using the Lyman-Kutcher-Burman model. However, various other normal tissue complication probability (NTCP) models exist. We evaluated in a large group of patients the value of six NTCP models that describe the parotid gland dose response 1 year after radiotherapy. Methods and Materials: A total of 347 patients with head-and-neck tumors were included in this prospective parotid gland dose-response study. The patients were treated with either conventional radiotherapy or intensity-modulated radiotherapy. Dose-volume histograms for the parotid glands were derived from three-dimensional dose calculations using computed tomography scans. Stimulatedmore » salivary flow rates were measured before and 1 year after radiotherapy. A threshold of 25% of the pretreatment flow rate was used to define a complication. The evaluated models included the Lyman-Kutcher-Burman model, the mean dose model, the relative seriality model, the critical volume model, the parallel functional subunit model, and the dose-threshold model. The goodness of fit (GOF) was determined by the deviance and a Monte Carlo hypothesis test. Ranking of the models was based on Akaike's information criterion (AIC). Results: None of the models was rejected based on the evaluation of the GOF. The mean dose model was ranked as the best model based on the AIC. The TD{sub 50} in these models was approximately 39 Gy. Conclusions: The mean dose model was preferred for describing the dose-response relationship of the parotid gland.« less

Variable selection in discrete survival models including heterogeneity.

PubMed

Groll, Andreas; Tutz, Gerhard

2017-04-01

Several variable selection procedures are available for continuous time-to-event data. However, if time is measured in a discrete way and therefore many ties occur models for continuous time are inadequate. We propose penalized likelihood methods that perform efficient variable selection in discrete survival modeling with explicit modeling of the heterogeneity in the population. The method is based on a combination of ridge and lasso type penalties that are tailored to the case of discrete survival. The performance is studied in simulation studies and an application to the birth of the first child.

Effects of error covariance structure on estimation of model averaging weights and predictive performance

USGS Publications Warehouse

Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

2013-01-01

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek

α-Intercalated cells defend the urinary system from bacterial infection.

PubMed

Paragas, Neal; Kulkarni, Ritwij; Werth, Max; Schmidt-Ott, Kai M; Forster, Catherine; Deng, Rong; Zhang, Qingyin; Singer, Eugenia; Klose, Alexander D; Shen, Tian Huai; Francis, Kevin P; Ray, Sunetra; Vijayakumar, Soundarapandian; Seward, Samuel; Bovino, Mary E; Xu, Katherine; Takabe, Yared; Amaral, Fábio E; Mohan, Sumit; Wax, Rebecca; Corbin, Kaitlyn; Sanna-Cherchi, Simone; Mori, Kiyoshi; Johnson, Lynne; Nickolas, Thomas; D'Agati, Vivette; Lin, Chyuan-Sheng; Qiu, Andong; Al-Awqati, Qais; Ratner, Adam J; Barasch, Jonathan

2014-07-01

α-Intercalated cells (A-ICs) within the collecting duct of the kidney are critical for acid-base homeostasis. Here, we have shown that A-ICs also serve as both sentinels and effectors in the defense against urinary infections. In a murine urinary tract infection model, A-ICs bound uropathogenic E. coli and responded by acidifying the urine and secreting the bacteriostatic protein lipocalin 2 (LCN2; also known as NGAL). A-IC-dependent LCN2 secretion required TLR4, as mice expressing an LPS-insensitive form of TLR4 expressed reduced levels of LCN2. The presence of LCN2 in urine was both necessary and sufficient to control the urinary tract infection through iron sequestration, even in the harsh condition of urine acidification. In mice lacking A-ICs, both urinary LCN2 and urinary acidification were reduced, and consequently bacterial clearance was limited. Together these results indicate that A-ICs, which are known to regulate acid-base metabolism, are also critical for urinary defense against pathogenic bacteria. They respond to both cystitis and pyelonephritis by delivering bacteriostatic chemical agents to the lower urinary system.

α–Intercalated cells defend the urinary system from bacterial infection

PubMed Central

Paragas, Neal; Kulkarni, Ritwij; Werth, Max; Schmidt-Ott, Kai M.; Forster, Catherine; Deng, Rong; Zhang, Qingyin; Singer, Eugenia; Klose, Alexander D.; Shen, Tian Huai; Francis, Kevin P.; Ray, Sunetra; Vijayakumar, Soundarapandian; Seward, Samuel; Bovino, Mary E.; Xu, Katherine; Takabe, Yared; Amaral, Fábio E.; Mohan, Sumit; Wax, Rebecca; Corbin, Kaitlyn; Sanna-Cherchi, Simone; Mori, Kiyoshi; Johnson, Lynne; Nickolas, Thomas; D’Agati, Vivette; Lin, Chyuan-Sheng; Qiu, Andong; Al-Awqati, Qais; Ratner, Adam J.; Barasch, Jonathan

2014-01-01

α–Intercalated cells (A-ICs) within the collecting duct of the kidney are critical for acid-base homeostasis. Here, we have shown that A-ICs also serve as both sentinels and effectors in the defense against urinary infections. In a murine urinary tract infection model, A-ICs bound uropathogenic E. coli and responded by acidifying the urine and secreting the bacteriostatic protein lipocalin 2 (LCN2; also known as NGAL). A-IC–dependent LCN2 secretion required TLR4, as mice expressing an LPS-insensitive form of TLR4 expressed reduced levels of LCN2. The presence of LCN2 in urine was both necessary and sufficient to control the urinary tract infection through iron sequestration, even in the harsh condition of urine acidification. In mice lacking A-ICs, both urinary LCN2 and urinary acidification were reduced, and consequently bacterial clearance was limited. Together these results indicate that A-ICs, which are known to regulate acid-base metabolism, are also critical for urinary defense against pathogenic bacteria. They respond to both cystitis and pyelonephritis by delivering bacteriostatic chemical agents to the lower urinary system. PMID:24937428

Working covariance model selection for generalized estimating equations.

PubMed

Carey, Vincent J; Wang, You-Gan

2011-11-20

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright © 2011 John Wiley & Sons, Ltd.

[Application of ARIMA model on prediction of malaria incidence].

PubMed

Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

2016-01-29

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

PubMed Central

Türkcan, Silvan; Masson, Jean-Baptiste

2013-01-01

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens -toxin (CPT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to

Detecting consistent patterns of directional adaptation using differential selection codon models.

PubMed

Parto, Sahar; Lartillot, Nicolas

2017-06-23

Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

On the selection of ordinary differential equation models with application to predator-prey dynamical models.

PubMed

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. © 2014, The International Biometric Society.

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different

Incidence and description of autoimmune cytopenias during treatment with ibrutinib for chronic lymphocytic leukemia.

PubMed

Rogers, K A; Ruppert, A S; Bingman, A; Andritsos, L A; Awan, F T; Blum, K A; Flynn, J M; Jaglowski, S M; Lozanski, G; Maddocks, K J; Byrd, J C; Woyach, J A; Jones, J A

2016-02-01

Chronic lymphocytic leukemia (CLL) is frequently complicated by secondary autoimmune cytopenias (AICs). Ibrutinib is an irreversible inhibitor of Bruton's tyrosine kinase approved for the treatment of relapsed CLL and CLL with del(17p). The effect of ibrutinib treatment on the incidence of AIC is currently unknown. We reviewed medical records of 301 patients treated with ibrutinib, as participants in therapeutic clinical trials at The Ohio State University Comprehensive Cancer Center between July 2010 and July 2014. Subjects were reviewed with respect to past history of AIC, and treatment-emergent AIC cases were identified. Before starting ibrutinib treatment, 26% of patients had experienced AIC. Information was available for a total of 468 patient-years of ibrutinib exposure, during which there were six cases of treatment-emergent AIC. This corresponds to an estimated incidence rate of 13 episodes for every 1000 patient-years of ibrutinib treatment. We further identified 22 patients receiving therapy for AIC at the time ibrutinib was started. Of these 22 patients, 19 were able to discontinue AIC therapy. We found that ibrutinib treatment is associated with a low rate of treatment-emergent AIC. Patients with an existing AIC have been successfully treated with ibrutinib and subsequently discontinued AIC therapy.

Equilibrium and nonequilibrium attractors for a discrete, selection-migration model

Treesearch

James F. Selgrade; James H. Roberds

2003-01-01

This study presents a discrete-time model for the effects of selection and immigration on the demographic and genetic compositions of a population. Under biologically reasonable conditions, it is shown that the model always has an equilibrium. Although equilibria for similar models without migration must have real eigenvalues, for this selection-migration model we...

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

PubMed

Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

2016-01-01

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

A Gambler's Model of Natural Selection.

ERIC Educational Resources Information Center

Nolan, Michael J.; Ostrovsky, David S.

1996-01-01

Presents an activity that highlights the mechanism and power of natural selection. Allows students to think in terms of modeling a biological process and instills an appreciation for a mathematical approach to biological problems. (JRH)

Models of microbiome evolution incorporating host and microbial selection.

PubMed

Zeng, Qinglong; Wu, Steven; Sukumaran, Jeet; Rodrigo, Allen

2017-09-25

Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong

Model Selection in Historical Research Using Approximate Bayesian Computation

PubMed Central

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

Measures and limits of models of fixation selection.

PubMed

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.

Using electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels.

PubMed

Lessios, Nicolas

2017-01-01

Understanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike's information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

PubMed

Hu, Wenbiao; Tong, Shilu; Mengersen, Kerrie; Connell, Des

2007-09-01

Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

ModelTest Server: a web-based tool for the statistical selection of models of nucleotide substitution online

PubMed Central

Posada, David

2006-01-01

ModelTest server is a web-based application for the selection of models of nucleotide substitution using the program ModelTest. The server takes as input a text file with likelihood scores for the set of candidate models. Models can be selected with hierarchical likelihood ratio tests, or with the Akaike or Bayesian information criteria. The output includes several statistics for the assessment of model selection uncertainty, for model averaging or to estimate the relative importance of model parameters. The server can be accessed at . PMID:16845102

Cognitive niches: an ecological model of strategy selection.

PubMed

Marewski, Julian N; Schooler, Lael J

2011-07-01

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 strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.

Model selection criterion in survival analysis

NASA Astrophysics Data System (ADS)

Karabey, Uǧur; Tutkun, Nihal Ata

2017-07-01

Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

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.

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.

Effects of parceling on model selection: Parcel-allocation variability in model ranking.

PubMed

Sterba, Sonya K; Rights, Jason D

2017-03-01

Research interest often lies in comparing structural model specifications implying different relationships among latent factors. In this context parceling is commonly accepted, assuming the item-level measurement structure is well known and, conservatively, assuming items are unidimensional in the population. Under these assumptions, researchers compare competing structural models, each specified using the same parcel-level measurement model. However, little is known about consequences of parceling for model selection in this context-including whether and when model ranking could vary across alternative item-to-parcel allocations within-sample. This article first provides a theoretical framework that predicts the occurrence of parcel-allocation variability (PAV) in model selection index values and its consequences for PAV in ranking of competing structural models. These predictions are then investigated via simulation. We show that conditions known to manifest PAV in absolute fit of a single model may or may not manifest PAV in model ranking. Thus, one cannot assume that low PAV in absolute fit implies a lack of PAV in ranking, and vice versa. PAV in ranking is shown to occur under a variety of conditions, including large samples. To provide an empirically supported strategy for selecting a model when PAV in ranking exists, we draw on relationships between structural model rankings in parcel- versus item-solutions. This strategy employs the across-allocation modal ranking. We developed software tools for implementing this strategy in practice, and illustrate them with an example. Even if a researcher has substantive reason to prefer one particular allocation, investigating PAV in ranking within-sample still provides an informative sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Inflation model selection meets dark radiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tram, Thomas; Vallance, Robert; Vennin, Vincent, E-mail: thomas.tram@port.ac.uk, E-mail: robert.vallance@student.manchester.ac.uk, E-mail: vincent.vennin@port.ac.uk

2017-01-01

We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species N {sub eff}. Using a minimal dataset (Planck low-ℓ polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptionsmore » are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when N {sub eff} is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H {sub 0} makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H {sub 0} tension would have deep consequences for inflation model selection.« less

Targeted versus statistical approaches to selecting parameters for modelling sediment provenance

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick

2017-04-01

One effective field-based approach to modelling sediment provenance is the source fingerprinting technique. Arguably, one of the most important steps for this approach is selecting the appropriate suite of parameters or fingerprints used to model source contributions. Accordingly, approaches to selecting parameters for sediment source fingerprinting will be reviewed. Thereafter, opportunities and limitations of these approaches and some future research directions will be presented. For properties to be effective tracers of sediment, they must discriminate between sources whilst behaving conservatively. Conservative behavior is characterized by constancy in sediment properties, where the properties of sediment sources remain constant, or at the very least, any variation in these properties should occur in a predictable and measurable way. Therefore, properties selected for sediment source fingerprinting should remain constant through sediment detachment, transportation and deposition processes, or vary in a predictable and measurable way. One approach to select conservative properties for sediment source fingerprinting is to identify targeted tracers, such as caesium-137, that provide specific source information (e.g. surface versus subsurface origins). A second approach is to use statistical tests to select an optimal suite of conservative properties capable of modelling sediment provenance. In general, statistical approaches use a combination of a discrimination (e.g. Kruskal Wallis H-test, Mann-Whitney U-test) and parameter selection statistics (e.g. Discriminant Function Analysis or Principle Component Analysis). The challenge is that modelling sediment provenance is often not straightforward and there is increasing debate in the literature surrounding the most appropriate approach to selecting elements for modelling. Moving forward, it would be beneficial if researchers test their results with multiple modelling approaches, artificial mixtures, and multiple

Automatic selection of arterial input function using tri-exponential models

NASA Astrophysics Data System (ADS)

Yao, Jianhua; Chen, Jeremy; Castro, Marcelo; Thomasson, David

2009-02-01

Dynamic Contrast Enhanced MRI (DCE-MRI) is one method for drug and tumor assessment. Selecting a consistent arterial input function (AIF) is necessary to calculate tissue and tumor pharmacokinetic parameters in DCE-MRI. This paper presents an automatic and robust method to select the AIF. The first stage is artery detection and segmentation, where knowledge about artery structure and dynamic signal intensity temporal properties of DCE-MRI is employed. The second stage is AIF model fitting and selection. A tri-exponential model is fitted for every candidate AIF using the Levenberg-Marquardt method, and the best fitted AIF is selected. Our method has been applied in DCE-MRIs of four different body parts: breast, brain, liver and prostate. The success rates in artery segmentation for 19 cases are 89.6%+/-15.9%. The pharmacokinetic parameters computed from the automatically selected AIFs are highly correlated with those from manually determined AIFs (R2=0.946, P(T<=t)=0.09). Our imaging-based tri-exponential AIF model demonstrated significant improvement over a previously proposed bi-exponential model.

Variable selection for marginal longitudinal generalized linear models.

PubMed

Cantoni, Eva; Flemming, Joanna Mills; Ronchetti, Elvezio

2005-06-01

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GC(p).

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.

Evaluation of new collision-pair selection models in DSMC

NASA Astrophysics Data System (ADS)

Akhlaghi, Hassan; Roohi, Ehsan

2017-10-01

The current paper investigates new collision-pair selection procedures in a direct simulation Monte Carlo (DSMC) method. Collision partner selection based on the random procedure from nearest neighbor particles and deterministic selection of nearest neighbor particles have already been introduced as schemes that provide accurate results in a wide range of problems. In the current research, new collision-pair selections based on the time spacing and direction of the relative movement of particles are introduced and evaluated. Comparisons between the new and existing algorithms are made considering appropriate test cases including fluctuations in homogeneous gas, 2D equilibrium flow, and Fourier flow problem. Distribution functions for number of particles and collisions in cell, velocity components, and collisional parameters (collision separation, time spacing, relative velocity, and the angle between relative movements of particles) are investigated and compared with existing analytical relations for each model. The capability of each model in the prediction of the heat flux in the Fourier problem at different cell numbers, numbers of particles, and time steps is examined. For new and existing collision-pair selection schemes, the effect of an alternative formula for the number of collision-pair selections and avoiding repetitive collisions are investigated via the prediction of the Fourier heat flux. The simulation results demonstrate the advantages and weaknesses of each model in different test cases.

Pharmacokinetic Modeling of Intranasal Scopolamine in Plasma Saliva and Urine

NASA Technical Reports Server (NTRS)

Wu, L.; Chow, D. S. L.; Tam, V.; Putcha, L.

2014-01-01

An intranasal gel formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness. The bioavailability and pharmacokinetics (PK) were evaluated under the Food and Drug Administration guidelines for clinical trials for an Investigative New Drug (IND). The aim of this project was to develop a PK model that can predict the relationship between plasma, saliva and urinary scopolamine concentrations using data collected from the IND clinical trial with INSCOP. METHODS: Twelve healthy human subjects were administered three dose levels (0.1, 0.2 and 0.4 mg) of INSCOP. Serial blood, saliva and urine samples were collected between 5 min to 24 h after dosing and scopolamine concentrations measured by using a validated LC-MS-MS assay. Pharmacokinetic Compartmental models, using actual dosing and sampling times, were built using Phoenix (version 1.2). Model discrimination was performed, by minimizing the Akaike Information Criteria (AIC), maximizing the coefficient of determination (r²) and by comparison of the quality of fit plots. RESULTS: The best structural model to describe scopolamine disposition after INSCOP administration (minimal AIC =907.2) consisted of one compartment for plasma, saliva and urine respectively that were inter-connected with different rate constants. The estimated values of PK parameters were compiled in Table 1. The model fitting exercises revealed a nonlinear PK for scopolamine between plasma and saliva compartments for K21, Vmax and Km. CONCLUSION: PK model for INSCOP was developed and for the first time it satisfactorily predicted the PK of scopolamine in plasma, saliva and urine after INSCOP administration. Using non-linear PK yielded the best structural model to describe scopolamine disposition between plasma and saliva compartments, and inclusion of non-linear PK resulted in a significant improved model fitting. The model can be utilized to predict scopolamine plasma concentration using saliva and/or urine data that

A Process Model of Principal Selection.

ERIC Educational Resources Information Center

Flanigan, J. L.; And Others

A process model to assist school district superintendents in the selection of principals is presented in this paper. Components of the process are described, which include developing an action plan, formulating an explicit job description, advertising, assessing candidates' philosophy, conducting interview analyses, evaluating response to stress,…

Diversified models for portfolio selection based on uncertain semivariance

NASA Astrophysics Data System (ADS)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

RADIO TRANSIENTS FROM ACCRETION-INDUCED COLLAPSE OF WHITE DWARFS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriya, Takashi J., E-mail: takashi.moriya@nao.ac.jp

2016-10-20

We investigate observational properties of accretion-induced collapse (AIC) of white dwarfs (WDs) in radio frequencies. If AIC is triggered by accretion from a companion star, a dense circumstellar medium can be formed around the progenitor system. Then, the ejecta from AIC collide with the dense circumstellar medium, creating a strong shock. The strong shock can produce synchrotron emission that can be observed in radio frequencies. Even if AIC occurs as a result of WD mergers, we argue that AIC may cause fast radio bursts (FRBs) if a certain condition is satisfied. If AIC forms neutron stars (NSs) that are somore » massive that rotation is required to support themselves (i.e., supramassive NSs), the supramassive NSs may immediately lose their rotational energy by the r-mode instability and collapse to black holes. If the collapsing supramassive NSs are strongly magnetized, they may emit FRBs, as previously proposed. The AIC radio transients from single-degenerate systems may be detected in future radio transient surveys like the Very Large Array Sky Survey or the Square Kilometer Array transient survey. Because AIC has been proposed as a source of gravitational waves (GWs), GWs from AIC may be accompanied by radio-bright transients that can be used to confirm the AIC origin of observed GWs.« less

EEG sleep in Cushing's disease and Cushing's syndrome: comparison with patients with major depressive disorder.

PubMed

Shipley, J E; Schteingart, D E; Tandon, R; Pande, A C; Grunhaus, L; Haskett, R F; Starkman, M N

1992-07-15

Because patients with Cushing' syndrome (CS) and Major depressive disorder (MDD) share features of hypercortisolism and the depressive syndrome, we compared electro-encephalographic (EEG) sleep in patients with pituitary-ACTH-dependent Cushing's syndrome (Cushing's disease, CD), patients with ACTH-independent Cushing's syndrome (AICS), patients with major depressive disorder (MDD), and normal subjects. There were substantial similarities in the abnormal polysomnography profiles of patients with CD, AICS, and MDD. All three patient groups demonstrated poorer sleep continuity, shortened rapid eye movement (REM) latency, and increased first REM period density compared with normal subjects. In addition, AICS patients and MDD patients had elevated REM activity and density. These findings are discussed in terms of models of pathophysiology that relate abnormalities in sleep, mood, and hypothalamic-pituitary-adrenal function.

Fisher-Wright model with deterministic seed bank and selection.

PubMed

Koopmann, Bendix; Müller, Johannes; Tellier, Aurélien; Živković, Daniel

2017-04-01

Seed banks are common characteristics to many plant species, which allow storage of genetic diversity in the soil as dormant seeds for various periods of time. We investigate an above-ground population following a Fisher-Wright model with selection coupled with a deterministic seed bank assuming the length of the seed bank is kept constant and the number of seeds is large. To assess the combined impact of seed banks and selection on genetic diversity, we derive a general diffusion model. The applied techniques outline a path of approximating a stochastic delay differential equation by an appropriately rescaled stochastic differential equation. We compute the equilibrium solution of the site-frequency spectrum and derive the times to fixation of an allele with and without selection. Finally, it is demonstrated that seed banks enhance the effect of selection onto the site-frequency spectrum while slowing down the time until the mutation-selection equilibrium is reached. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

NASA Astrophysics Data System (ADS)

Harwati

2017-06-01

Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009–2013

PubMed Central

Dang, Tran Ngoc; Seposo, Xerxes T.; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

Background The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Objectives Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. Design We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). Results High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11–1.83; low temperature effect, RR=2.0, 95% CI=1.13–3.52), females (low temperature effect, RR=2.19, 95% CI=1.14–4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91–6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15–2.22; low temperature effect, RR=1.99, 95% CI=0.92–4.28). Conclusions In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City. PMID:26781954

Broken selection rule in the quantum Rabi model

PubMed Central

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

Modeling selective pressures on phytoplankton in the global ocean.

PubMed

Bragg, Jason G; Dutkiewicz, Stephanie; Jahn, Oliver; Follows, Michael J; Chisholm, Sallie W

2010-03-10

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

Model selection for pion photoproduction

DOE PAGES

Landay, J.; Doring, M.; Fernandez-Ramirez, C.; ...

2017-01-12

Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory andmore » K-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. As a result, the principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (dσ/dΩ), photon-beam asymmetries (Σ), and target asymmetry differential cross sections (dσ T/d≡Tdσ/dΩ) in the low-energy regime.« less

Model averaging techniques for quantifying conceptual model uncertainty.

PubMed

Singh, Abhishek; Mishra, Srikanta; Ruskauff, Greg

2010-01-01

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories--Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

COUNCIL FOR REGULATORY ENVIRONMENTAL MODELING (CREM) PILOT WATER QUALITY MODEL SELECTION TOOL

EPA Science Inventory

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 ...

A Population Genetics Model of Marker-Assisted Selection

PubMed Central

Luo, Z. W.; Thompson, R.; Woolliams, J. A.

1997-01-01

A deterministic two-loci model was developed to predict genetic response to marker-assisted selection (MAS) in one generation and in multiple generations. Formulas were derived to relate linkage disequilibrium in a population to the proportion of additive genetic variance used by MAS, and in turn to an extra improvement in genetic response over phenotypic selection. Predictions of the response were compared to those predicted by using an infinite-loci model and the factors affecting efficiency of MAS were examined. Theoretical analyses of the present study revealed the nonlinearity between the selection intensity and genetic response in MAS. In addition to the heritability of the trait and the proportion of the marker-associated genetic variance, the frequencies of the selectively favorable alleles at the two loci, one marker and one quantitative trait locus, were found to play an important role in determining both the short- and long-term efficiencies of MAS. The evolution of linkage disequilibrium and thus the genetic response over several generations were predicted theoretically and examined by simulation. MAS dissipated the disequilibrium more quickly than drift alone. In some cases studied, the rate of dissipation was as large as that to be expected in the circumstance where the true recombination fraction was increased by three times and selection was absent. PMID:9215918

A Unifying Mechanistic Model of Selective Attention in Spiking Neurons

PubMed Central

Bobier, Bruce; Stewart, Terrence C.; Eliasmith, Chris

2014-01-01

Visuospatial attention produces myriad effects on the activity and selectivity of cortical neurons. Spiking neuron models capable of reproducing a wide variety of these effects remain elusive. We present a model called the Attentional Routing Circuit (ARC) that provides a mechanistic description of selective attentional processing in cortex. The model is described mathematically and implemented at the level of individual spiking neurons, with the computations for performing selective attentional processing being mapped to specific neuron types and laminar circuitry. The model is used to simulate three studies of attention in macaque, and is shown to quantitatively match several observed forms of attentional modulation. Specifically, ARC demonstrates that with shifts of spatial attention, neurons may exhibit shifting and shrinking of receptive fields; increases in responses without changes in selectivity for non-spatial features (i.e. response gain), and; that the effect on contrast-response functions is better explained as a response-gain effect than as contrast-gain. Unlike past models, ARC embodies a single mechanism that unifies the above forms of attentional modulation, is consistent with a wide array of available data, and makes several specific and quantifiable predictions. PMID:24921249

Selection, calibration, and validation of models of tumor growth.

PubMed

Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

2016-11-01

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory

Magnetosheath plasma stability and ULF wave occurrence as a function of location in the magnetosheath and upstream bow shock parameters

NASA Astrophysics Data System (ADS)

Soucek, Jan; Escoubet, C. Philippe; Grison, Benjamin

2015-04-01

We present the results of a statistical study of the distribution of mirror and Alfvén-ion cyclotron (AIC) waves in the magnetosheath together with plasma parameters important for the stability of ULF waves, specifically ion temperature anisotropy and ion beta. Magnetosheath crossings registered by Cluster spacecraft over the course of 2 years served as a basis for the statistics. For each observation we used bow shock, magnetopause, and magnetosheath flow models to identify the relative position of the spacecraft with respect to magnetosheath boundaries and local properties of the upstream shock crossing. A strong dependence of both plasma parameters and mirror/AIC wave occurrence on upstream ΘBn and MA is identified. We analyzed a joint dependence of the same parameters on ΘBn and fractional distance between shock and magnetopause, zenith angle, and length of the flow line. Finally, the occurrence of mirror and AIC modes was compared against the respective instability thresholds. We noted that AIC waves occurred nearly exclusively under mirror stable conditions. This is interpreted in terms of different characters of nonlinear saturation of the two modes.

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

Compromise Approach-Based Genetic Algorithm for Constrained Multiobjective Portfolio Selection Model

NASA Astrophysics Data System (ADS)

Li, Jun

In this paper, fuzzy set theory is incorporated into a multiobjective portfolio selection model for investors’ taking into three criteria: return, risk and liquidity. The cardinality constraint, the buy-in threshold constraint and the round-lots constraints are considered in the proposed model. To overcome the difficulty of evaluation a large set of efficient solutions and selection of the best one on non-dominated surface, a compromise approach-based genetic algorithm is presented to obtain a compromised solution for the proposed constrained multiobjective portfolio selection model.

Hydraulic head interpolation using ANFIS—model selection and sensitivity analysis

NASA Astrophysics Data System (ADS)

Kurtulus, Bedri; Flipo, Nicolas

2012-01-01

The aim of this study is to investigate the efficiency of ANFIS (adaptive neuro fuzzy inference system) for interpolating hydraulic head in a 40-km 2 agricultural watershed of the Seine basin (France). Inputs of ANFIS are Cartesian coordinates and the elevation of the ground. Hydraulic head was measured at 73 locations during a snapshot campaign on September 2009, which characterizes low-water-flow regime in the aquifer unit. The dataset was then split into three subsets using a square-based selection method: a calibration one (55%), a training one (27%), and a test one (18%). First, a method is proposed to select the best ANFIS model, which corresponds to a sensitivity analysis of ANFIS to the type and number of membership functions (MF). Triangular, Gaussian, general bell, and spline-based MF are used with 2, 3, 4, and 5 MF per input node. Performance criteria on the test subset are used to select the 5 best ANFIS models among 16. Then each is used to interpolate the hydraulic head distribution on a (50×50)-m grid, which is compared to the soil elevation. The cells where the hydraulic head is higher than the soil elevation are counted as "error cells." The ANFIS model that exhibits the less "error cells" is selected as the best ANFIS model. The best model selection reveals that ANFIS models are very sensitive to the type and number of MF. Finally, a sensibility analysis of the best ANFIS model with four triangular MF is performed on the interpolation grid, which shows that ANFIS remains stable to error propagation with a higher sensitivity to soil elevation.

A model of two-way selection system for human behavior.

PubMed

Zhou, Bin; Qin, Shujia; Han, Xiao-Pu; He, Zhe; Xie, Jia-Rong; Wang, Bing-Hong

2014-01-01

Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.

Cross-validation pitfalls when selecting and assessing regression and classification models.

PubMed

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

A Heckman selection model for the safety analysis of signalized intersections

PubMed Central

Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

2017-01-01

Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. PMID:28732050

Automating an integrated spatial data-mining model for landfill site selection

NASA Astrophysics Data System (ADS)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

2017-10-01

An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

Selecting single model in combination forecasting based on cointegration test and encompassing test.

PubMed

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.

Multilevel selection in a resource-based model.

PubMed

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. 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.

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.

Selective Cooperation in Early Childhood – How to Choose Models and Partners

PubMed Central

Hermes, Jonas; Behne, Tanya; Studte, Kristin; Zeyen, Anna-Maria; Gräfenhain, Maria; Rakoczy, Hannes

2016-01-01

Cooperation is essential for human society, and children engage in cooperation from early on. It is unclear, however, how children select their partners for cooperation. We know that children choose selectively whom to learn from (e.g. preferring reliable over unreliable models) on a rational basis. The present study investigated whether children (and adults) also choose their cooperative partners selectively and what model characteristics they regard as important for cooperative partners and for informants about novel words. Three- and four-year-old children (N = 64) and adults (N = 14) saw contrasting pairs of models differing either in physical strength or in accuracy (in labeling known objects). Participants then performed different tasks (cooperative problem solving and word learning) requiring the choice of a partner or informant. Both children and adults chose their cooperative partners selectively. Moreover they showed the same pattern of selective model choice, regarding a wide range of model characteristics as important for cooperation (preferring both the strong and the accurate model for a strength-requiring cooperation tasks), but only prior knowledge as important for word learning (preferring the knowledgeable but not the strong model for word learning tasks). Young children’s selective model choice thus reveals an early rational competence: They infer characteristics from past behavior and flexibly consider what characteristics are relevant for certain tasks. PMID:27505043

Near-optimal experimental design for model selection in systems biology.

PubMed

Busetto, Alberto Giovanni; Hauser, Alain; Krummenacher, Gabriel; Sunnåker, Mikael; Dimopoulos, Sotiris; Ong, Cheng Soon; Stelling, Jörg; Buhmann, Joachim M

2013-10-15

Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

A model for plant lighting system selection.

PubMed

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.

Selection of climate change scenario data for impact modelling.

PubMed

Sloth Madsen, M; Maule, C Fox; MacKellar, N; Olesen, J E; Christensen, J Hesselbjerg

2012-01-01

Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

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…

Evidence accumulation as a model for lexical selection.

PubMed

Anders, R; Riès, S; van Maanen, L; Alario, F X

2015-11-01

We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.

Water availability determines the richness and density of fig trees within Brazilian semideciduous forest landscapes

NASA Astrophysics Data System (ADS)

Coelho, Luís Francisco Mello; Ribeiro, Milton Cezar; Pereira, Rodrigo Augusto Santinelo

2014-05-01

The success of fig trees in tropical ecosystems is evidenced by the great diversity (+750 species) and wide geographic distribution of the genus. We assessed the contribution of environmental variables on the species richness and density of fig trees in fragments of seasonal semideciduous forest (SSF) in Brazil. We assessed 20 forest fragments in three regions in Sao Paulo State, Brazil. Fig tree richness and density was estimated in rectangular plots, comprising 31.4 ha sampled. Both richness and fig tree density were linearly modeled as function of variables representing (1) fragment metrics, (2) forest structure, and (3) landscape metrics expressing water drainage in the fragments. Model selection was performed by comparing the AIC values (Akaike Information Criterion) and the relative weight of each model (wAIC). Both species richness and fig tree density were better explained by the water availability in the fragment (meter of streams/ha): wAICrichness = 0.45, wAICdensity = 0.96. The remaining variables related to anthropic perturbation and forest structure were of little weight in the models. The rainfall seasonality in SSF seems to select for both establishment strategies and morphological adaptations in the hemiepiphytic fig tree species. In the studied SSF, hemiepiphytes established at lower heights in their host trees than reported for fig trees in evergreen rainforests. Some hemiepiphytic fig species evolved superficial roots extending up to 100 m from their trunks, resulting in hectare-scale root zones that allow them to efficiently forage water and soil nutrients. The community of fig trees was robust to variation in forest structure and conservation level of SSF fragments, making this group of plants an important element for the functioning of seasonal tropical forests.

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

PubMed Central

Offman, Marc N; Tournier, Alexander L; Bates, Paul A

2008-01-01

Background Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection. Results In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed. Conclusion This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA. PMID:18673557

Nonmathematical models for evolution of altruism, and for group selection (peck order-territoriality-ant colony-dual-determinant model-tri-determinant model).

PubMed

Darlington, P J

1972-02-01

Mathematical biologists have failed to produce a satisfactory general model for evolution of altruism, i.e., of behaviors by which "altruists" benefit other individuals but not themselves; kin selection does not seem to be a sufficient explanation of nonreciprocal altruism. Nonmathematical (but mathematically acceptable) models are now proposed for evolution of negative altruism in dual-determinant and of positive altruism in tri-determinant systems. Peck orders, territorial systems, and an ant society are analyzed as examples. In all models, evolution is primarily by individual selection, probably supplemented by group selection. Group selection is differential extinction of populations. It can act only on populations preformed by selection at the individual level, but can either cancel individual selective trends (effecting evolutionary homeostasis) or supplement them; its supplementary effect is probably increasingly important in the evolution of increasingly organized populations.

Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

PubMed Central

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. PMID:24892061

Fixation probability in a two-locus intersexual selection model.

PubMed

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. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian block-diagonal variable selection and model averaging

PubMed Central

Papaspiliopoulos, O.; Rossell, D.

2018-01-01

Summary We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a single one-dimensional integral, and other quantities of interest such as variable inclusion probabilities and model-averaged regression estimates are obtained by an adaptive, deterministic one-dimensional numerical integration. The overall computational cost scales linearly with the number of blocks, which can be processed in parallel, and exponentially with the block size, rendering it most adequate in situations where predictors are organized in many moderately-sized blocks. For general designs, we approximate the Gram matrix by a block-diagonal matrix using spectral clustering and propose an iterative algorithm that capitalizes on the block-diagonal algorithms to explore efficiently the model space. All methods proposed in this paper are implemented in the R library mombf. PMID:29861501

Melatonin treatment further improves adipose-derived mesenchymal stem cell therapy for acute interstitial cystitis in rat.

PubMed

Chen, Yen-Ta; Chiang, Hsin-Ju; Chen, Chih-Hung; Sung, Pei-Hsun; Lee, Fan-Yen; Tsai, Tzu-Hsien; Chang, Chia-Lo; Chen, Hong-Hwa; Sun, Cheuk-Kwan; Leu, Steve; Chang, Hsueh-Wen; Yang, Chih-Chao; Yip, Hon-Kan

2014-10-01

This study tests the hypothesis that combined melatonin and adipose-derived mesenchymal stem cell (ADMSC, 1.2 × 10(6) given intravenously) treatment offer superior protection against cyclophosphamide (CYP 150 mg/kg)-induced acute interstitial cystitis (AIC) in rats. Male adult Sprague-Dawley rats were treated as follows: sham controls, AIC alone, AIC + melatonin, AIC + ADMSC, and AIC + melatonin +ADMSC. When melatonin was used, it was given as follows: 20 mg/kg at 30 min after CYP and 50 mg/kg at 6 and 18 hr after CYP. Twenty-four-hour urine volume, urine albumin level, and severity of hematuria were highest in AIC rats and lowest in the controls; likewise urine volume was higher in AIC + melatonin rats than in AIC + ADMSC and AIC + melatonin + ADMSC treated rats; in all cases, P < 0.001. The numbers of CD14+, CD74+, CD68+, MIP+, Cox-2+, substance P+, cells and protein expression of IL-6, IL-12, RANTES, TNF-α, NF-κB, MMP-9, iNOS (i.e. inflammatory biomarkers), glycosaminoglycan level, expression of oxidized protein, and protein expression of reactive oxygen species (NOX-1, NOX-2, NOX-4) in the bladder tissue exhibited an identical pattern compared with that of hematuria among the five groups (all P < 0.0001). The integrity of epithelial layer and area of collagen deposition displayed an opposite pattern compared to that of hematuria among all groups (P < 0.0001). The cellular expressions of antioxidants (GR, GPx, HO-1, NQO 1) showed a significant progressive increase form controls to AIC + melatonin + ADMSC (all P < 0.0001). Combined regimen of melatonin and ADMSC was superior to either alone in protecting against CYP-induced AIC. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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 genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection

PubMed Central

Sale, Mark; Sherer, Eric A

2015-01-01

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection. PMID:23772792

Selection of latent variables for multiple mixed-outcome models

PubMed Central

ZHOU, LING; LIN, HUAZHEN; SONG, XINYUAN; LI, YI

2014-01-01

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them. PMID:27642219

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

Genetic signatures of natural selection in a model invasive ascidian

NASA Astrophysics Data System (ADS)

Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

2017-03-01

Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta.

Genetic signatures of natural selection in a model invasive ascidian

PubMed Central

Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

2017-01-01

Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta. PMID:28266616

Determining a Retention Model for the Selected Marine Corps Reserve

DTIC Science & Technology

2016-03-01

RETENTION MODEL FOR THE SELECTED MARINE CORPS RESERVE by Andrew D. Dausman March 2016 Thesis Advisor: Marigee Bacolod Co-Advisor: Chad W...2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE DETERMINING A RETENTION MODEL FOR THE SELECTED MARINE CORPS RESERVE...Approved for public release; distribution is unlimited 12b. DISTRIBUTION CODE A 13. ABSTRACT (maximum 200 words) This thesis develops a retention

Elementary Teachers' Selection and Use of Visual Models

ERIC Educational Resources Information Center

Lee, Tammy D.; Jones, M. Gail

2018-01-01

As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service…

Generative model selection using a scalable and size-independent complex network classifier

NASA Astrophysics Data System (ADS)

Motallebi, Sadegh; Aliakbary, Sadegh; Habibi, Jafar

2013-12-01

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks," outperforms existing methods with respect to accuracy, scalability, and size-independence.

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.

Lipid correction model of carbon stable isotopes for a cosmopolitan predator, spiny dogfish Squalus acanthias.

PubMed

Reum, J C P

2011-12-01

Three lipid correction models were evaluated for liver and white dorsal muscle from Squalus acanthias. For muscle, all three models performed well, based on the Akaike Information Criterion value corrected for small sample sizes (AIC(c) ), and predicted similar lipid corrections to δ(13) C that were up to 2.8 ‰ higher than those predicted using previously published models based on multispecies data. For liver, which possessed higher bulk C:N values compared to that of white muscle, all three models performed poorly and lipid-corrected δ(13) C values were best approximated by simply adding 5.74 ‰ to bulk δ(13) C values. © 2011 The Author. Journal of Fish Biology © 2011 The Fisheries Society of the British Isles.

Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

NASA Astrophysics Data System (ADS)

Mihai, L. Angela; Woolley, Thomas E.; Goriely, Alain

2018-03-01

Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests. These quantities can take on different values, corresponding to possible outcomes of the experiments. As multiple models can be derived that adequately represent the observed phenomena, we apply Occam's razor by providing an explicit criterion for model selection based on Bayesian statistics. We then employ this criterion to select a model among competing models calibrated to experimental data for rubber and brain tissue under single or multiaxial loads.

Increasing selection response by Bayesian modeling of heterogeneous environmental variances

USDA-ARS?s Scientific Manuscript database

Heterogeneity of environmental variance among genotypes reduces selection response because genotypes with higher variance are more likely to be selected than low-variance genotypes. Modeling heterogeneous variances to obtain weighted means corrected for heterogeneous variances is difficult in likel...

Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model

PubMed Central

Wichary, Szymon; Smolen, Tomasz

2016-01-01

In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models of this process. We also present the Bottom-Up Model of Strategy Selection (BUMSS). The model assumes that the use of the rational Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: (1) cue weight computation, (2) gain modulation, and (3) weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neural signals. PMID:27877103

Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model.

PubMed

Wichary, Szymon; Smolen, Tomasz

2016-01-01

In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models of this process. We also present the Bottom-Up Model of Strategy Selection (BUMSS). The model assumes that the use of the rational Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: (1) cue weight computation, (2) gain modulation, and (3) weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neural signals.

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

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

Potential roles of the interaction between model V1 neurons with orientation-selective and non-selective surround inhibition in contour detection.

PubMed

Yang, Kai-Fu; Li, Chao-Yi; Li, Yong-Jie

2015-01-01

Both the neurons with orientation-selective and with non-selective surround inhibition have been observed in the primary visual cortex (V1) of primates and cats. Though the inhibition coming from the surround region (named as non-classical receptive field, nCRF) has been considered playing critical role in visual perception, the specific role of orientation-selective and non-selective inhibition in the task of contour detection is less known. To clarify above question, we first carried out computational analysis of the contour detection performance of V1 neurons with different types of surround inhibition, on the basis of which we then proposed two integrated models to evaluate their role in this specific perceptual task by combining the two types of surround inhibition with two different ways. The two models were evaluated with synthetic images and a set of challenging natural images, and the results show that both of the integrated models outperform the typical models with orientation-selective or non-selective inhibition alone. The findings of this study suggest that V1 neurons with different types of center-surround interaction work in cooperative and adaptive ways at least when extracting organized structures from cluttered natural scenes. This work is expected to inspire efficient phenomenological models for engineering applications in field of computational machine-vision.

Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function.

PubMed

Wang, Guobao; Corwin, Michael T; Olson, Kristin A; Badawi, Ramsey D; Sarkar, Souvik

2018-05-30

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET did not show a promise. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. The objective of this study is to evaluate and identify a dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen human patients with nonalcoholic fatty liver disease were included in the study. Each patient underwent one-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and modified model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation reference. The results showed that the optimization-derived DBIF model improved the fitting of liver time activity curves and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for kinetic analysis of dynamic liver FDG-PET data in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation. © 2018

Generative model selection using a scalable and size-independent complex network classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Motallebi, Sadegh, E-mail: motallebi@ce.sharif.edu; Aliakbary, Sadegh, E-mail: aliakbary@ce.sharif.edu; Habibi, Jafar, E-mail: jhabibi@sharif.edu

2013-12-15

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree formore » model selection. Our proposed method, which is named “Generative Model Selection for Complex Networks,” outperforms existing methods with respect to accuracy, scalability, and size-independence.« less

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.

PubMed

Urbanowicz, Ryan J; Kiralis, Jeff; Fisher, Jonathan M; Moore, Jason H

2012-09-26

Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model's EDM and COR are each stronger predictors of model detection success than heritability. This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.

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…

ULF waves and plasma stability in different regions of the magnetosheath

NASA Astrophysics Data System (ADS)

Soucek, Jan; Escoubet, C. Philippe; Grison, Benjamin

2016-04-01

We present a statistical study of the occurrence and properties of ultra low frequency waves in the magnetosheath and interpret the results in terms of the competition of mirror and Alfvén-ion-cyclotron (AIC) instabilities. Both mirror and AIC waves are generated in high beta plasma of the magnetosheath when ion temperature anisotropy exceeds the threshold of the respective instabilities. These waves are frequently observed in the terrestrial and planetary magnetosheaths, but their distribution within the magnetosheath is inhomogeneous and their character varies as a function of location, local and upstream plasma parameters. We studied the spatial distribution of the two wave modes in the magnetosheath together with the local plasma parameters important for the stability of ULF waves. This analysis was performed on a dataset of all magnetosheath crossings observed by Cluster spacecraft over two years. For each observation we used bow shock, magnetopause and magnetosheath flow models to identify the relative position of the spacecraft with respect to magnetosheath boundaries and local properties of the upstream shock crossing. A strong dependence of parameters characterizing plasma stability and mirror/AIC wave occurrence on upstream ΘBn and MA is identified. The occurrence of mirror and AIC modes was compared against the respective instability thresholds and it was observed that AIC waves occurred nearly exclusively under mirror stable conditions. This is interpreted in terms of the different character of non-linear saturation of the two modes.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

PubMed

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic

Bivariate copula in fitting rainfall data

NASA Astrophysics Data System (ADS)

Yee, Kong Ching; Suhaila, Jamaludin; Yusof, Fadhilah; Mean, Foo Hui

2014-07-01

The usage of copula to determine the joint distribution between two variables is widely used in various areas. The joint distribution of rainfall characteristic obtained using the copula model is more ideal than the standard bivariate modelling where copula is belief to have overcome some limitation. Six copula models will be applied to obtain the most suitable bivariate distribution between two rain gauge stations. The copula models are Ali-Mikhail-Haq (AMH), Clayton, Frank, Galambos, Gumbel-Hoogaurd (GH) and Plackett. The rainfall data used in the study is selected from rain gauge stations which are located in the southern part of Peninsular Malaysia, during the period from 1980 to 2011. The goodness-of-fit test in this study is based on the Akaike information criterion (AIC).

Evaluation of portfolio credit risk based on survival analysis for progressive censored data

NASA Astrophysics Data System (ADS)

Jaber, Jamil J.; Ismail, Noriszura; Ramli, Siti Norafidah Mohd

2017-04-01

In credit risk management, the Basel committee provides a choice of three approaches to the financial institutions for calculating the required capital: the standardized approach, the Internal Ratings-Based (IRB) approach, and the Advanced IRB approach. The IRB approach is usually preferred compared to the standard approach due to its higher accuracy and lower capital charges. This paper use several parametric models (Exponential, log-normal, Gamma, Weibull, Log-logistic, Gompertz) to evaluate the credit risk of the corporate portfolio in the Jordanian banks based on the monthly sample collected from January 2010 to December 2015. The best model is selected using several goodness-of-fit criteria (MSE, AIC, BIC). The results indicate that the Gompertz distribution is the best model parametric model for the data.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease

Population genetics of polymorphism and divergence for diploid selection models with arbitrary dominance.

PubMed

Williamson, Scott; Fledel-Alon, Adi; Bustamante, Carlos D

2004-09-01

We develop a Poisson random-field model of polymorphism and divergence that allows arbitrary dominance relations in a diploid context. This model provides a maximum-likelihood framework for estimating both selection and dominance parameters of new mutations using information on the frequency spectrum of sequence polymorphisms. This is the first DNA sequence-based estimator of the dominance parameter. Our model also leads to a likelihood-ratio test for distinguishing nongenic from genic selection; simulations indicate that this test is quite powerful when a large number of segregating sites are available. We also use simulations to explore the bias in selection parameter estimates caused by unacknowledged dominance relations. When inference is based on the frequency spectrum of polymorphisms, genic selection estimates of the selection parameter can be very strongly biased even for minor deviations from the genic selection model. Surprisingly, however, when inference is based on polymorphism and divergence (McDonald-Kreitman) data, genic selection estimates of the selection parameter are nearly unbiased, even for completely dominant or recessive mutations. Further, we find that weak overdominant selection can increase, rather than decrease, the substitution rate relative to levels of polymorphism. This nonintuitive result has major implications for the interpretation of several popular tests of neutrality.

Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

PubMed

Shangguan, Wei; Dai, YongJiu; García-Gutiérrez, Carlos; Yuan, Hua

2014-01-01

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

Development of an Environment for Software Reliability Model Selection

DTIC Science & Technology

1992-09-01

now is directed to other related problems such as tools for model selection, multiversion programming, and software fault tolerance modeling... multiversion programming, 7. Hlardware can be repaired by spare modules, which is not. the case for software, 2-6 N. Preventive maintenance is very important

A computational model of selection by consequences: log survivor plots.

PubMed

Kulubekova, Saule; McDowell, J J

2008-06-01

[McDowell, J.J, 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297-317] instantiated the principle of selection by consequences in a virtual organism with an evolving repertoire of possible behaviors undergoing selection, reproduction, and mutation over many generations. The process is based on the computational approach, which is non-deterministic and rules-based. The model proposes a causal account for operant behavior. McDowell found that the virtual organism consistently showed a hyperbolic relationship between response and reinforcement rates according to the quantitative law of effect. To continue validation of the computational model, the present study examined its behavior on the molecular level by comparing the virtual organism's IRT distributions in the form of log survivor plots to findings from live organisms. Log survivor plots did not show the "broken-stick" feature indicative of distinct bouts and pauses in responding, although the bend in slope of the plots became more defined at low reinforcement rates. The shape of the virtual organism's log survivor plots was more consistent with the data on reinforced responding in pigeons. These results suggest that log survivor plot patterns of the virtual organism were generally consistent with the findings from live organisms providing further support for the computational model of selection by consequences as a viable account of operant behavior.

Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

Variable selection in subdistribution hazard frailty models with competing risks data

PubMed Central

Do Ha, Il; Lee, Minjung; Oh, Seungyoung; Jeong, Jong-Hyeon; Sylvester, Richard; Lee, Youngjo

2014-01-01

The proportional subdistribution hazards model (i.e. Fine-Gray model) has been widely used for analyzing univariate competing risks data. Recently, this model has been extended to clustered competing risks data via frailty. To the best of our knowledge, however, there has been no literature on variable selection method for such competing risks frailty models. In this paper, we propose a simple but unified procedure via a penalized h-likelihood (HL) for variable selection of fixed effects in a general class of subdistribution hazard frailty models, in which random effects may be shared or correlated. We consider three penalty functions (LASSO, SCAD and HL) in our variable selection procedure. We show that the proposed method can be easily implemented using a slight modification to existing h-likelihood estimation approaches. Numerical studies demonstrate that the proposed procedure using the HL penalty performs well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The usefulness of the new method is illustrated using two actual data sets from multi-center clinical trials. PMID:25042872

Multi-scale habitat selection modeling: A review and outlook

Treesearch

Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman

2016-01-01

Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.

Orbital-selective Mott phase in multiorbital models for iron pnictides and chalcogenides

NASA Astrophysics Data System (ADS)

Yu, Rong; Si, Qimiao

2017-09-01

There is increasing recognition that the multiorbital nature of the 3 d electrons is important to the proper description of the electronic states in the normal state of the iron-based superconductors. Earlier studies of the pertinent multiorbital Hubbard models identified an orbital-selective Mott phase, which anchors the orbital-selective behavior seen in the overall phase diagram. An important characteristics of the models is that the orbitals are kinetically coupled, i.e., hybridized, to each other, which makes the orbital-selective Mott phase especially nontrivial. A U (1 ) slave-spin method was used to analyze the model with nonzero orbital-level splittings. Here we develop a Landau free-energy functional to shed further light on this issue. We put the microscopic analysis from the U (1 ) slave-spin approach in this perspective, and show that the intersite spin correlations are crucial to the renormalization of the bare hybridization amplitude towards zero and the concomitant realization of the orbital-selective Mott transition. Based on this insight, we discuss additional ways to study the orbital-selective Mott physics from a dynamical competition between the interorbital hybridization and collective spin correlations. Our results demonstrate the robustness of the orbital-selective Mott phase in the multiorbital models appropriate for the iron-based superconductors.

Potential roles of the interaction between model V1 neurons with orientation-selective and non-selective surround inhibition in contour detection

PubMed Central

Yang, Kai-Fu; Li, Chao-Yi; Li, Yong-Jie

2015-01-01

Both the neurons with orientation-selective and with non-selective surround inhibition have been observed in the primary visual cortex (V1) of primates and cats. Though the inhibition coming from the surround region (named as non-classical receptive field, nCRF) has been considered playing critical role in visual perception, the specific role of orientation-selective and non-selective inhibition in the task of contour detection is less known. To clarify above question, we first carried out computational analysis of the contour detection performance of V1 neurons with different types of surround inhibition, on the basis of which we then proposed two integrated models to evaluate their role in this specific perceptual task by combining the two types of surround inhibition with two different ways. The two models were evaluated with synthetic images and a set of challenging natural images, and the results show that both of the integrated models outperform the typical models with orientation-selective or non-selective inhibition alone. The findings of this study suggest that V1 neurons with different types of center–surround interaction work in cooperative and adaptive ways at least when extracting organized structures from cluttered natural scenes. This work is expected to inspire efficient phenomenological models for engineering applications in field of computational machine-vision. PMID:26136664

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

PubMed

Lawson, A B; Carroll, R; Faes, C; Kirby, R S; Aregay, M; Watjou, K

2017-12-01

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Nutaro, James J

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 paradigmmore » to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.« less

Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.

Anterior insula coordinates hierarchical processing of tactile mismatch responses

PubMed Central

Allen, Micah; Fardo, Francesca; Dietz, Martin J.; Hillebrandt, Hauke; Friston, Karl J.; Rees, Geraint; Roepstorff, Andreas

2016-01-01

The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy—projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace. PMID:26584870

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

Models of Cultural Niche Construction with Selection and Assortative Mating

PubMed Central

Feldman, Marcus W.

2012-01-01

Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits. PMID:22905167

Fantasy-Testing-Assessment: A Proposed Model for the Investigation of Mate Selection.

ERIC Educational Resources Information Center

Nofz, Michael P.

1984-01-01

Proposes a model for mate selection which outlines three modes of interpersonal relating--fantasy, testing, and assessment (FTA). The model is viewed as a more accurate representation of mate selection processes than suggested by earlier theories, and can be used to clarify couples' understandings of their own relationships. (JAC)

SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.

PubMed

Weight, Michael D; Harpending, Henry

2017-01-01

The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.

Sensitivity of resource selection and connectivity models to landscape definition

Treesearch

Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce

2017-01-01

Context: The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention. Objectives: We evaluated the sensitivity of resource selection and connectivity models to four landscape...

Where to deliver baits for deworming urban red foxes for Echinococcus multilocularis control: new protocol for micro-habitat modeling of fox denning requirements.

PubMed

Ikeda, Takako; Yoshimura, Masashi; Onoyama, Keiichi; Oku, Yuzaburo; Nonaka, Nariaki; Katakura, Ken

2014-08-06

Deworming wild foxes by baiting with the anthelmintic praziquantel is being established as a preventive technique against environmental contamination with Echinococcus multilocularis eggs. Improvement of the cost-benefit performance of baiting treatment is required urgently to raise and maintain the efficacy of deworming. We established a spatial model of den site selection by urban red foxes, the definitive host, to specify the optimal micro-habitats for delivering baits in a new modeling approach modified for urban fox populations. The model was established for two cities (Obihiro and Sapporo) in Hokkaido, Japan, in which a sylvatic cycle of E. multilocularis is maintained. The two cities have different degrees of urbanization. The modeling process was designed to detect the best combination of key environmental factors and spatial scale that foxes pay attention to most (here named 'heeding range') when they select den sites. All possible models were generated using logistic regression analysis, with "presence" or "absence" of fox den as the objective variable, and nine landscape categories customized for urban environments as predictor variables to detect the best subset of predictors. This procedure was conducted for each of ten sizes of concentric circles from dens and control points to detect the best circle size. Out of all models generated, the most parsimonious model was selected using Akaike's Information Criterion (AIC) inspection. Our models suggest that fox dens in Obihiro are located at the center of a circle with 500 m radius including low percentages of wide roads, narrow roads, and occupied buildings, but high percentages of green covered areas; the dens in Sapporo within 300 m radius with low percentages of wide roads, occupied buildings, but high percentages of riverbeds and green covered areas. The variation of the models suggests the necessity of accumulating models for various types of cities in order to reveal the patterns of the model. Our

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

USGS Publications Warehouse

Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

2014-01-01

Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

PubMed Central

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

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…

Selection and Validation of Model Early Childhood Projects: Final Report.

ERIC Educational Resources Information Center

Stock, John R.; And Others

Presented is the final report of a research program to select and describe outstanding Handicapped Children's Early Education Program (HCEEP) projects. Projects were analyzed in terms of 14 components of an HCEEP program (such as screening, programing, and inservice training). A detailed discussion of model selection and validation is appendixed.)…

Item selection via Bayesian IRT models.

PubMed

Arima, Serena

2015-02-10

With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan. Copyright © 2014 John Wiley & Sons, Ltd.

Evaluation of some random effects methodology applicable to bird ringing data

USGS Publications Warehouse

Burnham, K.P.; White, Gary C.

2002-01-01

Existing models for ring recovery and recapture data analysis treat temporal variations in annual survival probability (S) as fixed effects. Often there is no explainable structure to the temporal variation in S1,..., Sk; random effects can then be a useful model: Si = E(S) + ??i. Here, the temporal variation in survival probability is treated as random with average value E(??2) = ??2. This random effects model can now be fit in program MARK. Resultant inferences include point and interval estimation for process variation, ??2, estimation of E(S) and var (E??(S)) where the latter includes a component for ??2 as well as the traditional component for v??ar(S??\\S??). Furthermore, the random effects model leads to shrinkage estimates, Si, as improved (in mean square error) estimators of Si compared to the MLE, S??i, from the unrestricted time-effects model. Appropriate confidence intervals based on the Si are also provided. In addition, AIC has been generalized to random effects models. This paper presents results of a Monte Carlo evaluation of inference performance under the simple random effects model. Examined by simulation, under the simple one group Cormack-Jolly-Seber (CJS) model, are issues such as bias of ??s2, confidence interval coverage on ??2, coverage and mean square error comparisons for inference about Si based on shrinkage versus maximum likelihood estimators, and performance of AIC model selection over three models: Si ??? S (no effects), Si = E(S) + ??i (random effects), and S1,..., Sk (fixed effects). For the cases simulated, the random effects methods performed well and were uniformly better than fixed effects MLE for the Si.

Mathematical Modelling for Patient Selection in Proton Therapy.

PubMed

Mee, T; Kirkby, N F; Kirkby, K J

2018-05-01

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Contrast gain control: a bilinear model for chromatic selectivity.

PubMed

Singer, B; D'Zmura, M

1995-04-01

We report the results of psychophysical experiments on color contrast induction. In earlier work [Vision Res. 34, 3111 (1994)], we showed that modulating the spatial contrast of an annulus in time induces an apparent modulation of the contrast of a central disk, at isoluminance. Here we vary the chromatic properties of disk and annulus systematically in a study of the interactions among the luminance and the color-opponent channels. Results show that induced contrast depends linearly on both disk and annulus contrast, at low and moderate contrast levels. This dependence leads us to propose a bilinear model for color contrast gain control. The model predicts the magnitude and the chromatic properties of induced contrast. In agreement with experimental results, the model displays chromatic selectivity in contrast gain control and a negligible effect of contrast modulation at isoluminance on the appearance of achromatic contrast. We show that the bilinear model for chromatic selectivity may be realized as a feed-forward multiplicative gain control. Data collected at high contrast levels are fit by embellishing the model with saturating nonlinearities in the contrast gain control of each color channel.

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

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

Validation of elk resource selection models with spatially independent data

Treesearch

Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

2011-01-01

Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

A model for the sustainable selection of building envelope assemblies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huedo, Patricia, E-mail: huedo@uji.es; Mulet, Elena, E-mail: emulet@uji.es; López-Mesa, Belinda, E-mail: belinda@unizar.es

2016-02-15

The aim of this article is to define an evaluation model for the environmental impacts of building envelopes to support planners in the early phases of materials selection. The model is intended to estimate environmental impacts for different combinations of building envelope assemblies based on scientifically recognised sustainability indicators. These indicators will increase the amount of information that existing catalogues show to support planners in the selection of building assemblies. To define the model, first the environmental indicators were selected based on the specific aims of the intended sustainability assessment. Then, a simplified LCA methodology was developed to estimate themore » impacts applicable to three types of dwellings considering different envelope assemblies, building orientations and climate zones. This methodology takes into account the manufacturing, installation, maintenance and use phases of the building. Finally, the model was validated and a matrix in Excel was created as implementation of the model. - Highlights: • Method to assess the envelope impacts based on a simplified LCA • To be used at an earlier phase than the existing methods in a simple way. • It assigns a score by means of known sustainability indicators. • It estimates data about the embodied and operating environmental impacts. • It compares the investment costs with the costs of the consumed energy.« less

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…

Global attractors for a discrete selection model with periodic immigration

Treesearch

James F. Selgrade; James H. Roberds

2007-01-01

A one-island selection-migration model is used to study the periodic immigration of a population of fixed allele frequency into a natural population. Density-dependent selection and immigration are the primary factors affecting the demographic genetic change in the island population. With the assumptions of complete dominance (CD) or no dominance (ND) and homozygote...

Effects of floods on fish assemblages in an intermittent prairie stream

USGS Publications Warehouse

Franssen, N.R.; Gido, K.B.; Guy, C.S.; Tripe, J.A.; Shrank, S.J.; Strakosh, T.R.; Bertrand, K.N.; Franssen, C.M.; Pitts, K.L.; Paukert, C.P.

2006-01-01

1. Floods are major disturbances to stream ecosystems that can kill or displace organisms and modify habitats. Many studies have reported changes in fish assemblages after a single flood, but few studies have evaluated the importance of timing and intensity of floods on long-term fish assemblage dynamics. 2. We used a 10-year dataset to evaluate the effects of floods on fishes in Kings Creek, an intermittent prairie stream in north-eastern, Kansas, U.S.A. Samples were collected seasonally at two perennial headwater sites (1995-2005) and one perennial downstream flowing site (1997-2005) allowing us to evaluate the effects of floods at different locations within a watershed. In addition, four surveys during 2003 and 2004 sampled 3-5 km of stream between the long-term study sites to evaluate the use of intermittent reaches of this stream. 3. Because of higher discharge and bed scouring at the downstream site, we predicted that the fish assemblage would have lowered species richness and abundance following floods. In contrast, we expected increased species richness and abundance at headwater sites because floods increase stream connectivity and create the potential for colonisation from downstream reaches. 4. Akaike Information Criteria (AIC) was used to select among candidate regression models that predicted species richness and abundance based on Julian date, time since floods, season and physical habitat at each site. At the downstream site, AIC weightings suggested Julian date was the best predictor of fish assemblage structure, but no model explained >16% of the variation in species richness or community structure. Variation explained by Julian date was primarily attributed to a long-term pattern of declining abundance of common species. At the headwater sites, there was not a single candidate model selected to predict total species abundance and assemblage structure. AIC weightings suggested variation in assemblage structure was associated with either Julian date

The anterior insula bidirectionally modulates cost-benefit decision-making on a rodent gambling task.

PubMed

Daniel, M L; Cocker, P J; Lacoste, J; Mar, A C; Houeto, J L; Belin-Rauscent, A; Belin, D

2017-11-01

Deficits in cost-benefit decision-making, as assessed in the Iowa Gambling Task (IGT), are commonly observed in neuropsychiatric disorders such as addiction. There is considerable variation in the maximization of rewards on such tasks, both in the general population and in rodent models, suggesting individual differences in decision-making may represent a key endophenotype for vulnerability to neuropsychiatric disorders. Increasing evidence suggests that the insular cortex, which is involved in interoception and emotional processes in humans, may be a key neural locus in the control of decision-making processes. However, the extent to which the insula contributes to individual differences in cost-benefit decision-making remains unknown. Using male Sprague Dawley rats, we first assessed individual differences in the performance over the course of a single session on a rodent analogue of the IGT (rGT). Rats were matched for their ability to maximize reward and received bilateral excitotoxic or sham lesions of the anterior insula cortex (AIC). Animals were subsequently challenged on a second rGT session with altered contingencies. Finally, animals were also assessed for instrumental conditioning and reversal learning. AIC lesions produced bidirectional alterations on rGT performance; rats that had performed optimally prior to surgery subsequently showed impairments, and animals that had performed poorly showed improvements in comparison with sham-operated controls. These bidirectional effects were not attributable to alterations in behavioural flexibility or in motivation. These data suggest that the recruitment of the AIC during decision-making may be state-dependent and help guide response selection towards subjectively favourable options. © 2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

[Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].

PubMed

Li, Jian; Wu, Huan-Yu; Li, Yan-Ting; Jin, Hui-Ming; Gu, Bao-Ke; Yuan, Zheng-An

2010-01-01

To explore the feasibility of establishing and applying of autoregressive integrated moving average (ARIMA) model to predict the incidence rate of dysentery in Shanghai, so as to provide the theoretical basis for prevention and control of dysentery. ARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and conclusion, and the model goodness-of-fit was determined through Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. The model ARIMA (1, 1, 1) (0, 1, 2)(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1 = 0.443) during the past time series, moving average coefficient (MA1 = 0.806) and seasonal moving average coefficient (SMA1 = 0.543, SMA2 = 0.321) being statistically significant (P < 0.01). AIC and SBC were 2.878 and 16.131 respectively and predicting error was white noise. The mathematic function was (1-0.443B) (1-B) (1-B(12))Z(t) = (1-0.806B) (1-0.543B(12)) (1-0.321B(2) x 12) micro(t). The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6.78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9.390 per 100 thousand. ARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high precision for short-time forecast.

Deposition and characterization of silicon thin-films by aluminum-induced crystallization

NASA Astrophysics Data System (ADS)

Ebil, Ozgenc

Polycrystalline silicon (poly-Si) as a thin-film solar cell material could have major advantages compared to non-silicon thin-film technologies. In theory, thin-film poly-Si may retain the performance and stability of c-Si while taking advantage of established manufacturing techniques. However, poly-Si films deposited onto foreign substrates at low temperatures typically have an average grain size of 10--50 nm. Such a grain structure presents a potential problem for device performance since it introduces an excessive number of grain boundaries which, if left unpassivated, lead to poor solar cell properties. Therefore, for optimum device performance, the grain size of the poly-Si film should be at least comparable to the thickness of the films. For this project, the objectives were the deposition of poly-Si thin-films with 2--5 mum grain size on glass substrates using in-situ and conventional aluminum-induced crystallization (AIC) and the development of a model for AIC process. In-situ AIC experiments were performed using Hot-Wire Chemical Vapor Deposition (HWCVD) both above and below the eutectic temperature (577°C) of Si-Al binary system. Conventional AIC experiments were performed using a-Si layers deposited on aluminum coated glass substrates by Electron-beam deposition, Plasma Enhanced Chemical Vapor Deposition (PECVD) and HWCVD. Continuous poly-Si films with an average grain size of 10 mum on glass substrates were achieved by both in-situ and conventional aluminum-induced crystallization of Si below eutectic temperature. The grain size was determined by three factors; the grain structure of Al layer, the nature of the interfacial oxide, and crystallization temperature. The interface oxide was found to be crucial for AIC process but not necessary for crystallization itself. The characterization of interfacial oxide layer formed on Al films revealed a bilayer structure containing Al2O3 and Al(OH)3 . The effective activation energy for AIC process was determined

A semiparametric graphical modelling approach for large-scale equity selection

PubMed Central

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption. PMID:28316507

A semiparametric graphical modelling approach for large-scale equity selection.

PubMed

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption.

Model-independent plot of dynamic PET data facilitates data interpretation and model selection.

PubMed

Munk, Ole Lajord

2012-02-21

When testing new PET radiotracers or new applications of existing tracers, the blood-tissue exchange and the metabolism need to be examined. However, conventional plots of measured time-activity curves from dynamic PET do not reveal the inherent kinetic information. A novel model-independent volume-influx plot (vi-plot) was developed and validated. The new vi-plot shows the time course of the instantaneous distribution volume and the instantaneous influx rate. The vi-plot visualises physiological information that facilitates model selection and it reveals when a quasi-steady state is reached, which is a prerequisite for the use of the graphical analyses by Logan and Gjedde-Patlak. Both axes of the vi-plot have direct physiological interpretation, and the plot shows kinetic parameter in close agreement with estimates obtained by non-linear kinetic modelling. The vi-plot is equally useful for analyses of PET data based on a plasma input function or a reference region input function. The vi-plot is a model-independent and informative plot for data exploration that facilitates the selection of an appropriate method for data analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

MMI: Multimodel inference or models with management implications?

USGS Publications Warehouse

Fieberg, J.; Johnson, Douglas H.

2015-01-01

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Estimating animal resource selection from telemetry data using point process models

USGS Publications Warehouse

Johnson, Devin S.; Hooten, Mevin B.; Kuhn, Carey E.

2013-01-01

To demonstrate the analysis of telemetry data with the point process approach, we analysed a data set of telemetry locations from northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska. Both a space–time and an aggregated space-only model were fitted. At the individual level, the space–time analysis showed little selection relative to the habitat covariates. However, at the study area level, the space-only model showed strong selection relative to the covariates.

Wind scatterometry with improved ambiguity selection and rain modeling

NASA Astrophysics Data System (ADS)

Draper, David Willis

Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributors to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributor to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous

FADO: a statistical method to detect favored or avoided distances between occurrences of motifs using the Hawkes' model.

PubMed

Gusto, Gaelle; Schbath, Sophie

2005-01-01

We propose an original statistical method to estimate how the occurrences of a given process along a genome, genes or motifs for instance, may be influenced by the occurrences of a second process. More precisely, the aim is to detect avoided and/or favored distances between two motifs, for instance, suggesting possible interactions at a molecular level. For this, we consider occurrences along the genome as point processes and we use the so-called Hawkes' model. In such model, the intensity at position t depends linearly on the distances to past occurrences of both processes via two unknown profile functions to estimate. We perform a non parametric estimation of both profiles by using B-spline decompositions and a constrained maximum likelihood method. Finally, we use the AIC criterion for the model selection. Simulations show the excellent behavior of our estimation procedure. We then apply it to study (i) the dependence between gene occurrences along the E. coli genome and the occurrences of a motif known to be part of the major promoter for this bacterium, and (ii) the dependence between the yeast S. cerevisiae genes and the occurrences of putative polyadenylation signals. The results are coherent with known biological properties or previous predictions, meaning this method can be of great interest for functional motif detection, or to improve knowledge of some biological mechanisms.

Procedure for the Selection and Validation of a Calibration Model I-Description and Application.

PubMed

Desharnais, Brigitte; Camirand-Lemyre, Félix; Mireault, Pascal; Skinner, Cameron D

2017-05-01

Calibration model selection is required for all quantitative methods in toxicology and more broadly in bioanalysis. This typically involves selecting the equation order (quadratic or linear) and weighting factor correctly modelizing the data. A mis-selection of the calibration model will generate lower quality control (QC) accuracy, with an error up to 154%. Unfortunately, simple tools to perform this selection and tests to validate the resulting model are lacking. We present a stepwise, analyst-independent scheme for selection and validation of calibration models. The success rate of this scheme is on average 40% higher than a traditional "fit and check the QCs accuracy" method of selecting the calibration model. Moreover, the process was completely automated through a script (available in Supplemental Data 3) running in RStudio (free, open-source software). The need for weighting was assessed through an F-test using the variances of the upper limit of quantification and lower limit of quantification replicate measurements. When weighting was required, the choice between 1/x and 1/x2 was determined by calculating which option generated the smallest spread of weighted normalized variances. Finally, model order was selected through a partial F-test. The chosen calibration model was validated through Cramer-von Mises or Kolmogorov-Smirnov normality testing of the standardized residuals. Performance of the different tests was assessed using 50 simulated data sets per possible calibration model (e.g., linear-no weight, quadratic-no weight, linear-1/x, etc.). This first of two papers describes the tests, procedures and outcomes of the developed procedure using real LC-MS-MS results for the quantification of cocaine and naltrexone. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

On Large Time Behavior and Selection Principle for a Diffusive Carr-Penrose Model

NASA Astrophysics Data System (ADS)

Conlon, Joseph G.; Dabkowski, Michael; Wu, Jingchen

2016-04-01

This paper is concerned with the study of a diffusive perturbation of the linear LSW model introduced by Carr and Penrose. A main subject of interest is to understand how the presence of diffusion acts as a selection principle, which singles out a particular self-similar solution of the linear LSW model as determining the large time behavior of the diffusive model. A selection principle is rigorously proven for a model which is a semiclassical approximation to the diffusive model. Upper bounds on the rate of coarsening are also obtained for the full diffusive model.

Amylose-potassium oleate inclusion complex in plain set-style yogurt.

PubMed

Singh, Mukti; Byars, Jeffrey A; Kenar, James A

2014-05-01

Health and wellness aspirations of U.S. consumers continue to drive the demand for lower fat from inherently beneficial foods such as yogurt. Removing fat from yogurt negatively affects the gel strength, texture, syneresis, and storage of yogurt. Amylose-potassium oleate inclusion complexes (AIC) were used to replace skim milk solids to improve the quality of nonfat yogurt. The effect of AIC on fermentation of yogurt mix and strength of yogurt gel was studied and compared to full-fat samples. Texture, storage modulus, and syneresis of yogurt were observed over 4 weeks of storage at 4 °C. Yogurt mixes having the skim milk solids partially replaced by AIC fermented at a similar rate as yogurt samples with no milk solids replaced and full-fat milk. Initial viscosity was higher for yogurt mixes with AIC. The presence of 3% AIC strengthened the yogurt gel as indicated by texture and rheology measurements. Yogurt samples with 3% AIC maintained the gel strength during storage and resulted in low syneresis after storage for 4 wk. © 2014 Institute of Food Technologists®

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

SELECTION OF CANDIDATE EUTROPHICATION MODELS FOR TOTAL MAXIMUM DAILY LOADS ANALYSES

EPA Science Inventory

A tiered approach was developed to evaluate candidate eutrophication models to select a common suite of models that could be used for Total Maximum Daily Loads (TMDL) analyses in estuaries, rivers, and lakes/reservoirs. Consideration for linkage to watershed models and ecologica...

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

PubMed

Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K

2017-11-01

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection.

PubMed

Mwanza, Jean-Claude; Warren, Joshua L; Hochberg, Jessica T; Budenz, Donald L; Chang, Robert T; Ramulu, Pradeep Y

2015-01-01

To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. One hundred ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike's information criterion (AIC), and prediction confidence interval lengths. For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDx-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT×NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single-variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAP-FDT, and interaction GDx-TSNIT×NAP-FDT consistently provided better discriminating abilities for detecting early, moderate, and severe glaucoma than the best single-variable models. The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDx-TSNIT×NAP-FDT provides the best glaucoma prediction compared with all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared with using GDx or FDT alone.

Augmented Self-Modeling as a Treatment for Children with Selective Mutism.

ERIC Educational Resources Information Center

Kehle, Thomas J.; Madaus, Melissa R.; Baratta, Victoria S.; Bray, Melissa A.

1998-01-01

Describes the treatment of three children experiencing selective mutism. The procedure utilized incorporated self-modeling, mystery motivators, self-reinforcement, stimulus fading, spacing, and antidepressant medication. All three children evidenced a complete cessation of selective mutism and maintained their treatment gains at follow-up.…

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Examining speed versus selection in connectivity models using elk migration as an example

USGS Publications Warehouse

Brennan, Angela; Hanks, Ephraim M.; Merkle, Jerod A.; Cole, Eric K.; Dewey, Sarah R.; Courtemanch, Alyson B.; Cross, Paul C.

2018-01-01

ContextLandscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.ObjectiveTo compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.MethodsUsing movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.ResultsAll connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.ConclusionsCTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

PubMed

Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio

2016-09-01

A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see "On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection" (Bruni et al. [2])). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.

A model of directional selection applied to the evolution of drug resistance in HIV-1.

PubMed

Seoighe, Cathal; Ketwaroo, Farahnaz; Pillay, Visva; Scheffler, Konrad; Wood, Natasha; Duffet, Rodger; Zvelebil, Marketa; Martinson, Neil; McIntyre, James; Morris, Lynn; Hide, Winston

2007-04-01

Understanding how pathogens acquire resistance to drugs is important for the design of treatment strategies, particularly for rapidly evolving viruses such as HIV-1. Drug treatment can exert strong selective pressures and sites within targeted genes that confer resistance frequently evolve far more rapidly than the neutral rate. Rapid evolution at sites that confer resistance to drugs can be used to help elucidate the mechanisms of evolution of drug resistance and to discover or corroborate novel resistance mutations. We have implemented standard maximum likelihood methods that are used to detect diversifying selection and adapted them for use with serially sampled reverse transcriptase (RT) coding sequences isolated from a group of 300 HIV-1 subtype C-infected women before and after single-dose nevirapine (sdNVP) to prevent mother-to-child transmission. We have also extended the standard models of codon evolution for application to the detection of directional selection. Through simulation, we show that the directional selection model can provide a substantial improvement in sensitivity over models of diversifying selection. Five of the sites within the RT gene that are known to harbor mutations that confer resistance to nevirapine (NVP) strongly supported the directional selection model. There was no evidence that other mutations that are known to confer NVP resistance were selected in this cohort. The directional selection model, applied to serially sampled sequences, also had more power than the diversifying selection model to detect selection resulting from factors other than drug resistance. Because inference of selection from serial samples is unlikely to be adversely affected by recombination, the methods we describe may have general applicability to the analysis of positive selection affecting recombining coding sequences when serially sampled data are available.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.

PubMed

Baston, Chiara; Ursino, Mauro

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

PubMed Central

Baston, Chiara

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments. PMID:26640481

Examining speed versus selection in connectivity models using elk migration as an example

USGS Publications Warehouse

Brennan, Angela; Hanks, EM; Merkle, JA; Cole, EK; Dewey, SR; Courtemanch, AB; Cross, Paul C.

2018-01-01

Context: Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity. Objective: To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection. Methods: Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements. Results: All models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP algorithms. Conclusions: CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Identification of landscape features influencing gene flow: How useful are habitat selection models?

Treesearch

Gretchen H. Roffler; Michael K. Schwartz; Kristine Pilgrim; Sandra L. Talbot; George K. Sage; Layne G. Adams; Gordon Luikart

2016-01-01

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is...

From "AICE-ing" the Test to Earning the Degree: Enrollment and Graduation Patterns among Students with the Cambridge Advanced International Certificate of Education (AICE) Diploma

ERIC Educational Resources Information Center

Rodeiro, Carmen Vidal; Crawford, Cara; Shaw, Stuart

2017-01-01

A key issue for admissions teams is to distinguish which students of those who apply are truly able and sufficiently committed to complete a degree. One signal of a student's ability to achieve college-level academic requirements is participation in high school acceleration programs such as Advanced Placement, International Baccalaureate or…

The coalescent process in models with selection and recombination.

PubMed

Hudson, R R; Kaplan, N L

1988-11-01

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.

How Reliable is Bayesian Model Averaging Under Noisy Data? Statistical Assessment and Implications for Robust Model Selection

NASA Astrophysics Data System (ADS)

Schöniger, Anneli; Wöhling, Thomas; Nowak, Wolfgang

2014-05-01

Bayesian model averaging ranks the predictive capabilities of alternative conceptual models based on Bayes' theorem. The individual models are weighted with their posterior probability to be the best one in the considered set of models. Finally, their predictions are combined into a robust weighted average and the predictive uncertainty can be quantified. This rigorous procedure does, however, not yet account for possible instabilities due to measurement noise in the calibration data set. This is a major drawback, since posterior model weights may suffer a lack of robustness related to the uncertainty in noisy data, which may compromise the reliability of model ranking. We present a new statistical concept to account for measurement noise as source of uncertainty for the weights in Bayesian model averaging. Our suggested upgrade reflects the limited information content of data for the purpose of model selection. It allows us to assess the significance of the determined posterior model weights, the confidence in model selection, and the accuracy of the quantified predictive uncertainty. Our approach rests on a brute-force Monte Carlo framework. We determine the robustness of model weights against measurement noise by repeatedly perturbing the observed data with random realizations of measurement error. Then, we analyze the induced variability in posterior model weights and introduce this "weighting variance" as an additional term into the overall prediction uncertainty analysis scheme. We further determine the theoretical upper limit in performance of the model set which is imposed by measurement noise. As an extension to the merely relative model ranking, this analysis provides a measure of absolute model performance. To finally decide, whether better data or longer time series are needed to ensure a robust basis for model selection, we resample the measurement time series and assess the convergence of model weights for increasing time series length. We illustrate

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.

Model Selection with Strong-lensing Systems

NASA Astrophysics Data System (ADS)

Leaf, Kyle; Melia, Fulvio

2018-05-01

In this paper, we use an unprecedentedly large sample (158) of confirmed strong lens systems for model selection, comparing five well studied Friedmann-Robertson-Walker cosmologies: ΛCDM, wCDM (the standard model with a variable dark-energy equation of state), the Rh = ct universe, the (empty) Milne cosmology, and the classical Einstein-de Sitter (matter dominated) universe. We first use these sources to optimize the parameters in the standard model and show that they are consistent with Planck, though the quality of the best fit is not satisfactory. We demonstrate that this is likely due to under-reported errors, or to errors yet to be included in this kind of analysis. We suggest that the missing dispersion may be due to scatter about a pure single isothermal sphere (SIS) model that is often assumed for the mass distribution in these lenses. We then use the Bayes information criterion, with the inclusion of a suggested SIS dispersion, to calculate the relative likelihoods and ranking of these models, showing that Milne and Einstein-de Sitter are completely ruled out, while Rh = ct is preferred over ΛCDM/wCDM with a relative probability of ˜73% versus ˜24%. The recently reported sample of new strong lens candidates by the Dark Energy Survey, if confirmed, may be able to demonstrate which of these two models is favoured over the other at a level exceeding 3σ.

A Technical Guide to Ground-Water Model Selection at Sites Contaminated with Radioactive Substances

EPA Pesticide Factsheets

This report addresses the selection of ground-water flow and contaminant transport models and is intended to be used by hydrogeologists and geoscientists responsible for selecting transport models for use at sites containing radioactive materials.

How can we model selectively neutral density dependence in evolutionary games.

PubMed

Argasinski, Krzysztof; Kozłowski, Jan

2008-03-01

The problem of density dependence appears in all approaches to the modelling of population dynamics. It is pertinent to classic models (i.e., Lotka-Volterra's), and also population genetics and game theoretical models related to the replicator dynamics. There is no density dependence in the classic formulation of replicator dynamics, which means that population size may grow to infinity. Therefore the question arises: How is unlimited population growth suppressed in frequency-dependent models? Two categories of solutions can be found in the literature. In the first, replicator dynamics is independent of background fitness. In the second type of solution, a multiplicative suppression coefficient is used, as in a logistic equation. Both approaches have disadvantages. The first one is incompatible with the methods of life history theory and basic probabilistic intuitions. The logistic type of suppression of per capita growth rate stops trajectories of selection when population size reaches the maximal value (carrying capacity); hence this method does not satisfy selective neutrality. To overcome these difficulties, we must explicitly consider turn-over of individuals dependent on mortality rate. This new approach leads to two interesting predictions. First, the equilibrium value of population size is lower than carrying capacity and depends on the mortality rate. Second, although the phase portrait of selection trajectories is the same as in density-independent replicator dynamics, pace of selection slows down when population size approaches equilibrium, and then remains constant and dependent on the rate of turn-over of individuals.

Models for Selecting Chief State School Officers. Policy Memo Series, No. 1.

ERIC Educational Resources Information Center

Sanchez, Karen L. Van Til; Hall, Gayle C.

The process of selecting a chief state school officer (CSSO) can be a significant means of allocating policymaking power in state educational governance. This paper examines the role of the chief state school officer and explains how that role is influenced by the selection process. Four selection models are described, along with the advantages…

Model selection for the North American Breeding Bird Survey: A comparison of methods

USGS Publications Warehouse

Link, William; Sauer, John; Niven, Daniel

2017-01-01

The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.

Models Used to Select Strategic Planning Experts for High Technology Productions

NASA Astrophysics Data System (ADS)

Zakharova, Alexandra A.; Grigorjeva, Antonina A.; Tseplit, Anna P.; Ozgogov, Evgenij V.

2016-04-01

The article deals with the problems and specific aspects in organizing works of experts involved in assessment of companies that manufacture complex high-technology products. A model is presented that is intended for evaluating competences of experts in individual functional areas of expertise. Experts are selected to build a group on the basis of tables used to determine a competence level. An expert selection model based on fuzzy logic is proposed and additional requirements for the expert group composition can be taken into account, with regard to the needed quality and competence related preferences of decision-makers. A Web-based information system model is developed for the interaction between experts and decision-makers when carrying out online examinations.

Cliff-edge model of obstetric selection in humans.

PubMed

Mitteroecker, Philipp; Huttegger, Simon M; Fischer, Barbara; Pavlicev, Mihaela

2016-12-20

The strikingly high incidence of obstructed labor due to the disproportion of fetal size and the mother's pelvic dimensions has puzzled evolutionary scientists for decades. Here we propose that these high rates are a direct consequence of the distinct characteristics of human obstetric selection. Neonatal size relative to the birth-relevant maternal dimensions is highly variable and positively associated with reproductive success until it reaches a critical value, beyond which natural delivery becomes impossible. As a consequence, the symmetric phenotype distribution cannot match the highly asymmetric, cliff-edged fitness distribution well: The optimal phenotype distribution that maximizes population mean fitness entails a fraction of individuals falling beyond the "fitness edge" (i.e., those with fetopelvic disproportion). Using a simple mathematical model, we show that weak directional selection for a large neonate, a narrow pelvic canal, or both is sufficient to account for the considerable incidence of fetopelvic disproportion. Based on this model, we predict that the regular use of Caesarean sections throughout the last decades has led to an evolutionary increase of fetopelvic disproportion rates by 10 to 20%.

A selection model for accounting for publication bias in a full network meta-analysis.

PubMed

Mavridis, Dimitris; Welton, Nicky J; Sutton, Alex; Salanti, Georgia

2014-12-30

Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency. Copyright © 2014 John Wiley & Sons, Ltd.

The Abstract Selection Task: New Data and an Almost Comprehensive Model

ERIC Educational Resources Information Center

Klauer, Karl Christoph; Stahl, Christoph; Erdfelder, Edgar

2007-01-01

A complete quantitative account of P. Wason's (1966) abstract selection task is proposed. The account takes the form of a mathematical model. It is assumed that some response patterns are caused by inferential reasoning, whereas other responses reflect cognitive processes that affect each card selection separately and independently of other card…

An Optimization Model For Strategy Decision Support to Select Kind of CPO’s Ship

NASA Astrophysics Data System (ADS)

Suaibah Nst, Siti; Nababan, Esther; Mawengkang, Herman

2018-01-01

The selection of marine transport for the distribution of crude palm oil (CPO) is one of strategy that can be considered in reducing cost of transport. The cost of CPO’s transport from one area to CPO’s factory located at the port of destination may affect the level of CPO’s prices and the number of demands. In order to maintain the availability of CPO a strategy is required to minimize the cost of transporting. In this study, the strategy used to select kind of charter ships as barge or chemical tanker. This study aims to determine an optimization model for strategy decision support in selecting kind of CPO’s ship by minimizing costs of transport. The select of ship was done randomly, so that two-stage stochastic programming model was used to select the kind of ship. Model can help decision makers to select either barge or chemical tanker to distribute CPO.

Differences between selection on sex versus recombination in red queen models with diploid hosts.

PubMed

Agrawal, Aneil F

2009-08-01

The Red Queen hypothesis argues that parasites generate selection for genetic mixing (sex and recombination) in their hosts. A number of recent papers have examined this hypothesis using models with haploid hosts. In these haploid models, sex and recombination are selectively equivalent. However, sex and recombination are not equivalent in diploids because selection on sex depends on the consequences of segregation as well as recombination. Here I compare how parasites select on modifiers of sexual reproduction and modifiers of recombination rate. Across a wide set of parameters, parasites tend to select against both sex and recombination, though recombination is favored more often than is sex. There is little correspondence between the conditions favoring sex and those favoring recombination, indicating that the direction of selection on sex is often determined by the effects of segregation, not recombination. Moreover, when sex was favored it is usually due to a long-term advantage whereas short-term effects are often responsible for selection favoring recombination. These results strongly indicate that Red Queen models focusing exclusively on the effects of recombination cannot be used to infer the type of selection on sex that is generated by parasites on diploid hosts.

Evaluating experimental design for soil-plant model selection using a Bootstrap Filter and Bayesian model averaging

NASA Astrophysics Data System (ADS)

Wöhling, T.; Schöniger, A.; Geiges, A.; Nowak, W.; Gayler, S.

2013-12-01

The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have been developed that exhibit a wide range of complexity regarding their approximation of processes in the coupled model compartments. We present a method for evaluating experimental design for maximum confidence in the model selection task. The method considers uncertainty in parameters, measurements and model structures. Advancing the ideas behind Bayesian Model Averaging (BMA), we analyze the changes in posterior model weights and posterior model choice uncertainty when more data are made available. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modelling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. Using a Bootstrap Filter (BF), the models were then conditioned on field measurements of soil moisture, matric potential, leaf-area index, and evapotranspiration rates (from eddy-covariance measurements) during a vegetation period of winter wheat at a field site at the Swabian Alb in Southwestern Germany. Following our new method, we derived model weights when using all data or different subsets thereof. We discuss to which degree the posterior mean outperforms the prior mean and all

Transverse tripolar stimulation of peripheral nerve: a modelling study of spatial selectivity.

PubMed

Deurloo, K E; Holsheimer, J; Boom, H B

1998-01-01

Various anode-cathode configurations in a nerve cuff are modelled to predict their spatial selectivity characteristics for functional nerve stimulation. A 3D volume conductor model of a monofascicular nerve is used for the computation of stimulation-induced field potentials, whereas a cable model of myelinated nerve fibre is used for the calculation of the excitation thresholds of fibres. As well as the usual configurations (monopole, bipole, longitudinal tripole, 'steering' anode), a transverse tripolar configuration (central cathode) is examined. It is found that the transverse tripole is the only configuration giving convex recruitment contours and therefore maximises activation selectivity for a small (cylindrical) bundle of fibres in the periphery of a monofascicular nerve trunk. As the electrode configuration is changed to achieve greater selectivity, the threshold current increases. Therefore threshold currents for fibre excitation with a transverse tripole are relatively high. Inverse recruitment is less extreme than for the other configurations. The influences of several geometrical parameters and model conductivities of the transverse tripole on selectivity and threshold current are analysed. In chronic implantation, when electrodes are encapsulated by a layer of fibrous tissue, threshold currents are low, whereas the shape of the recruitment contours in transverse tripolar stimulation does not change.

An Integrated Model of Emotional Problems, Beta Power of Electroencephalography, and Low Frequency of Heart Rate Variability after Childhood Trauma in a Non-Clinical Sample: A Path Analysis Study.

PubMed

Jin, Min Jin; Kim, Ji Sun; Kim, Sungkean; Hyun, Myoung Ho; Lee, Seung-Hwan

2017-01-01

Childhood trauma is known to be related to emotional problems, quantitative electroencephalography (EEG) indices, and heart rate variability (HRV) indices in adulthood, whereas directions among these factors have not been reported yet. This study aimed to evaluate pathway models in young and healthy adults: (1) one with physiological factors first and emotional problems later in adulthood as results of childhood trauma and (2) one with emotional problems first and physiological factors later. A total of 103 non-clinical volunteers were included. Self-reported psychological scales, including the Childhood Trauma Questionnaire (CTQ), State-Trait Anxiety Inventory, Beck Depression Inventory, and Affective Lability Scale were administered. For physiological evaluation, EEG record was performed during resting eyes closed condition in addition to the resting-state HRV, and the quantitative power analyses of eight EEG bands and three HRV components were calculated in the frequency domain. After a normality test, Pearson's correlation analysis to make path models and path analyses to examine them were conducted. The CTQ score was significantly correlated with depression, state and trait anxiety, affective lability, and HRV low-frequency (LF) power. LF power was associated with beta2 (18-22 Hz) power that was related to affective lability. Affective lability was associated with state anxiety, trait anxiety, and depression. Based on the correlation and the hypothesis, two models were composed: a model with pathways from CTQ score to affective lability, and a model with pathways from CTQ score to LF power. The second model showed significantly better fit than the first model (AIC model1  = 63.403 > AIC model2  = 46.003), which revealed that child trauma could affect emotion, and then physiology. The specific directions of relationships among emotions, the EEG, and HRV in adulthood after childhood trauma was discussed.