Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

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Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have

What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

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Thomas, Emily H.; Galambos, Nora

To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

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Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

Fractal approach to computer-analytical modelling of tree crown

International Nuclear Information System (INIS)

Berezovskaya, F.S.; Karev, G.P.; Kisliuk, O.F.; Khlebopros, R.G.; Tcelniker, Yu.L.

1993-09-01

In this paper we discuss three approaches to the modeling of a tree crown development. These approaches are experimental (i.e. regressive), theoretical (i.e. analytical) and simulation (i.e. computer) modeling. The common assumption of these is that a tree can be regarded as one of the fractal objects which is the collection of semi-similar objects and combines the properties of two- and three-dimensional bodies. We show that a fractal measure of crown can be used as the link between the mathematical models of crown growth and light propagation through canopy. The computer approach gives the possibility to visualize a crown development and to calibrate the model on experimental data. In the paper different stages of the above-mentioned approaches are described. The experimental data for spruce, the description of computer system for modeling and the variant of computer model are presented. (author). 9 refs, 4 figs

Prediction of survival to discharge following cardiopulmonary resuscitation using classification and regression trees.

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Ebell, Mark H; Afonso, Anna M; Geocadin, Romergryko G

2013-12-01

To predict the likelihood that an inpatient who experiences cardiopulmonary arrest and undergoes cardiopulmonary resuscitation survives to discharge with good neurologic function or with mild deficits (Cerebral Performance Category score = 1). Classification and Regression Trees were used to develop branching algorithms that optimize the ability of a series of tests to correctly classify patients into two or more groups. Data from 2007 to 2008 (n = 38,092) were used to develop candidate Classification and Regression Trees models to predict the outcome of inpatient cardiopulmonary resuscitation episodes and data from 2009 (n = 14,435) to evaluate the accuracy of the models and judge the degree of over fitting. Both supervised and unsupervised approaches to model development were used. 366 hospitals participating in the Get With the Guidelines-Resuscitation registry. Adult inpatients experiencing an index episode of cardiopulmonary arrest and undergoing cardiopulmonary resuscitation in the hospital. The five candidate models had between 8 and 21 nodes and an area under the receiver operating characteristic curve from 0.718 to 0.766 in the derivation group and from 0.683 to 0.746 in the validation group. One of the supervised models had 14 nodes and classified 27.9% of patients as very unlikely to survive neurologically intact or with mild deficits (Tree models that predict survival to discharge with good neurologic function or with mild deficits following in-hospital cardiopulmonary arrest. Models like this can assist physicians and patients who are considering do-not-resuscitate orders.

Regionalization of meso-scale physically based nitrogen modeling outputs to the macro-scale by the use of regression trees

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Künne, A.; Fink, M.; Kipka, H.; Krause, P.; Flügel, W.-A.

2012-06-01

In this paper, a method is presented to estimate excess nitrogen on large scales considering single field processes. The approach was implemented by using the physically based model J2000-S to simulate the nitrogen balance as well as the hydrological dynamics within meso-scale test catchments. The model input data, the parameterization, the results and a detailed system understanding were used to generate the regression tree models with GUIDE (Loh, 2002). For each landscape type in the federal state of Thuringia a regression tree was calibrated and validated using the model data and results of excess nitrogen from the test catchments. Hydrological parameters such as precipitation and evapotranspiration were also used to predict excess nitrogen by the regression tree model. Hence they had to be calculated and regionalized as well for the state of Thuringia. Here the model J2000g was used to simulate the water balance on the macro scale. With the regression trees the excess nitrogen was regionalized for each landscape type of Thuringia. The approach allows calculating the potential nitrogen input into the streams of the drainage area. The results show that the applied methodology was able to transfer the detailed model results of the meso-scale catchments to the entire state of Thuringia by low computing time without losing the detailed knowledge from the nitrogen transport modeling. This was validated with modeling results from Fink (2004) in a catchment lying in the regionalization area. The regionalized and modeled excess nitrogen correspond with 94%. The study was conducted within the framework of a project in collaboration with the Thuringian Environmental Ministry, whose overall aim was to assess the effect of agro-environmental measures regarding load reduction in the water bodies of Thuringia to fulfill the requirements of the European Water Framework Directive (Bäse et al., 2007; Fink, 2006; Fink et al., 2007).

Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones

International Nuclear Information System (INIS)

Rosli, A D; Baharudin, R; Hashim, H; Khairuzzaman, N A; Mohd Sampian, A F; Abdullah, N E; Kamaru'zzaman, M; Sulaiman, M S

2015-01-01

This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water. (paper)

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

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Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

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Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Malignancy Risk Assessment in Patients with Thyroid Nodules Using Classification and Regression Trees

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Shokouh Taghipour Zahir

2013-01-01

Full Text Available Purpose. We sought to investigate the utility of classification and regression trees (CART classifier to differentiate benign from malignant nodules in patients referred for thyroid surgery. Methods. Clinical and demographic data of 271 patients referred to the Sadoughi Hospital during 2006–2011 were collected. In a two-step approach, a CART classifier was employed to differentiate patients with a high versus low risk of thyroid malignancy. The first step served as the screening procedure and was tailored to produce as few false negatives as possible. The second step identified those with the lowest risk of malignancy, chosen from a high risk population. Sensitivity, specificity, positive and negative predictive values (PPV and NPV of the optimal tree were calculated. Results. In the first step, age, sex, and nodule size contributed to the optimal tree. Ultrasonographic features were employed in the second step with hypoechogenicity and/or microcalcifications yielding the highest discriminatory ability. The combined tree produced a sensitivity and specificity of 80.0% (95% CI: 29.9–98.9 and 94.1% (95% CI: 78.9–99.0, respectively. NPV and PPV were 66.7% (41.1–85.6 and 97.0% (82.5–99.8, respectively. Conclusion. CART classifier reliably identifies patients with a low risk of malignancy who can avoid unnecessary surgery.

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

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Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units

International Nuclear Information System (INIS)

Kropat, Georg; Bochud, Francois; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios, Martha; Baechler, Sébastien

2015-01-01

Purpose: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. Method: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). Results: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. Conclusion: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables

Market-based approaches to tree valuation

Science.gov (United States)

Geoffrey H. Donovan; David T. Butry

2008-01-01

A recent four-part series in Arborist News outlined different appraisal processes used to value urban trees. The final article in the series described the three generally accepted approaches to tree valuation: the sales comparison approach, the cost approach, and the income capitalization approach. The author, D. Logan Nelson, noted that the sales comparison approach...

Building optimal regression tree by ant colony system-genetic algorithm: Application to modeling of melting points

Energy Technology Data Exchange (ETDEWEB)

Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)

2011-10-17

Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.

Perceived Organizational Support for Enhancing Welfare at Work: A Regression Tree Model

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Giorgi, Gabriele; Dubin, David; Perez, Javier Fiz

2016-01-01

When trying to examine outcomes such as welfare and well-being, research tends to focus on main effects and take into account limited numbers of variables at a time. There are a number of techniques that may help address this problem. For example, many statistical packages available in R provide easy-to-use methods of modeling complicated analysis such as classification and tree regression (i.e., recursive partitioning). The present research illustrates the value of recursive partitioning in the prediction of perceived organizational support in a sample of more than 6000 Italian bankers. Utilizing the tree function party package in R, we estimated a regression tree model predicting perceived organizational support from a multitude of job characteristics including job demand, lack of job control, lack of supervisor support, training, etc. The resulting model appears particularly helpful in pointing out several interactions in the prediction of perceived organizational support. In particular, training is the dominant factor. Another dimension that seems to influence organizational support is reporting (perceived communication about safety and stress concerns). Results are discussed from a theoretical and methodological point of view. PMID:28082924

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

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Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen. Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

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Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

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Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

Data to support "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations & Biological Condition"

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U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...

Chronic subdural hematoma: Surgical management and outcome in 986 cases: A classification and regression tree approach

Science.gov (United States)

Rovlias, Aristedis; Theodoropoulos, Spyridon; Papoutsakis, Dimitrios

2015-01-01

Background: Chronic subdural hematoma (CSDH) is one of the most common clinical entities in daily neurosurgical practice which carries a most favorable prognosis. However, because of the advanced age and medical problems of patients, surgical therapy is frequently associated with various complications. This study evaluated the clinical features, radiological findings, and neurological outcome in a large series of patients with CSDH. Methods: A classification and regression tree (CART) technique was employed in the analysis of data from 986 patients who were operated at Asclepeion General Hospital of Athens from January 1986 to December 2011. Burr holes evacuation with closed system drainage has been the operative technique of first choice at our institution for 29 consecutive years. A total of 27 prognostic factors were examined to predict the outcome at 3-month postoperatively. Results: Our results indicated that neurological status on admission was the best predictor of outcome. With regard to the other data, age, brain atrophy, thickness and density of hematoma, subdural accumulation of air, and antiplatelet and anticoagulant therapy were found to correlate significantly with prognosis. The overall cross-validated predictive accuracy of CART model was 85.34%, with a cross-validated relative error of 0.326. Conclusions: Methodologically, CART technique is quite different from the more commonly used methods, with the primary benefit of illustrating the important prognostic variables as related to outcome. Since, the ideal therapy for the treatment of CSDH is still under debate, this technique may prove useful in developing new therapeutic strategies and approaches for patients with CSDH. PMID:26257985

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

Science.gov (United States)

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p logistic regression model for the classification of risk groups for PTB.

Iron Supplementation and Altitude: Decision Making Using a Regression Tree

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Laura A. Garvican-Lewis, Andrew D. Govus, Peter Peeling, Chris R. Abbiss, Christopher J. Gore

2016-03-01

not increase in the 8 athletes with Ferritin-Pre <35 µg.L-1 who were supplemented with 105 mg.d-1, it is possible that in these athletes this dose was insufficient to support erythropoiesis. Furthermore, athletes with higher Ferritin-Pre, but large daily iron requirements, may also benefit from supplementation at altitude. The lack of improvement in Hbmass in non-supplemented athletes, may be related to reduced iron availability and mobility, despite plentiful iron stores, perhaps due to an increase in hepcidin post-exercise (Peeling, 2010. In summary, our regression tree suggests if sufficient iron is made available (via supplementation, even ID athletes can improve Hbmass in response to altitude. Some athletes with otherwise normal ferritin may also require supplementation to maintain an iron balance capable of supporting both the haematological and non-haematological adaptations to altitude. We recommend an individualised approach when deciding whether iron supplementation is appropriate, particularly concerning the dose provided.

Downscaling soil moisture over East Asia through multi-sensor data fusion and optimization of regression trees

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Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

2017-04-01

Soil moisture is one of the most important keys for understanding regional and global climate systems. Soil moisture is directly related to agricultural processes as well as hydrological processes because soil moisture highly influences vegetation growth and determines water supply in the agroecosystem. Accurate monitoring of the spatiotemporal pattern of soil moisture is important. Soil moisture has been generally provided through in situ measurements at stations. Although field survey from in situ measurements provides accurate soil moisture with high temporal resolution, it requires high cost and does not provide the spatial distribution of soil moisture over large areas. Microwave satellite (e.g., advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR2), the Advanced Scatterometer (ASCAT), and Soil Moisture Active Passive (SMAP)) -based approaches and numerical models such as Global Land Data Assimilation System (GLDAS) and Modern- Era Retrospective Analysis for Research and Applications (MERRA) provide spatial-temporalspatiotemporally continuous soil moisture products at global scale. However, since those global soil moisture products have coarse spatial resolution ( 25-40 km), their applications for agriculture and water resources at local and regional scales are very limited. Thus, soil moisture downscaling is needed to overcome the limitation of the spatial resolution of soil moisture products. In this study, GLDAS soil moisture data were downscaled up to 1 km spatial resolution through the integration of AMSR2 and ASCAT soil moisture data, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Moderate Resolution Imaging Spectroradiometer (MODIS) data—Land Surface Temperature, Normalized Difference Vegetation Index, and Land cover—using modified regression trees over East Asia from 2013 to 2015. Modified regression trees were implemented using Cubist, a commercial software tool based on machine learning. An

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Science.gov (United States)

Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2009-01-01

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

A Branch-and-Price approach to find optimal decision trees

NARCIS (Netherlands)

Firat, M.; Crognier, Guillaume; Gabor, Adriana; Zhang, Y.

2018-01-01

In Artificial Intelligence (AI) field, decision trees have gained certain importance due to their effectiveness in solving classification and regression problems. Recently, in the literature we see finding optimal decision trees are formulated as Mixed Integer Linear Programming (MILP) models. This

Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata

Directory of Open Access Journals (Sweden)

Oguntunji Abel Olusegun

2017-01-01

Full Text Available Morphometric parameters and their indices are central to the understanding of the type and function of livestock. The present study was conducted to predict body weight (BWT of adult Nigerian Muscovy ducks from nine (9 morphometric parameters and seven (7 body indices and also to identify the most important predictor of BWT among them using regression tree analysis (RTA. The experimental birds comprised of 1,020 adult male and female Nigerian Muscovy ducks randomly sampled in Rain Forest (203, Guinea Savanna (298 and Derived Savanna (519 agro-ecological zones. Result of RTA revealed that compactness; body girth and massiveness were the most important independent variables in predicting BWT and were used in constructing RT. The combined effect of the three predictors was very high and explained 91.00% of the observed variation of the target variable (BWT. The optimal regression tree suggested that Muscovy ducks with compactness >5.765 would be fleshy and have highest BWT. The result of the present study could be exploited by animal breeders and breeding companies in selection and improvement of BWT of Muscovy ducks.

The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

DEFF Research Database (Denmark)

Brunori, Paolo; Hufe, Paul; Mahler, Daniel Gerszon

2017-01-01

the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make...... the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions....

Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis

Science.gov (United States)

Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John

2012-01-01

Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…

Fuzzy multiple linear regression: A computational approach

Science.gov (United States)

Juang, C. H.; Huang, X. H.; Fleming, J. W.

1992-01-01

This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

The decision tree approach to classification

Science.gov (United States)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

A novel dendrochronological approach reveals drivers of carbon sequestration in tree species of riparian forests across spatiotemporal scales.

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Rieger, Isaak; Kowarik, Ingo; Cherubini, Paolo; Cierjacks, Arne

2017-01-01

Aboveground carbon (C) sequestration in trees is important in global C dynamics, but reliable techniques for its modeling in highly productive and heterogeneous ecosystems are limited. We applied an extended dendrochronological approach to disentangle the functioning of drivers from the atmosphere (temperature, precipitation), the lithosphere (sedimentation rate), the hydrosphere (groundwater table, river water level fluctuation), the biosphere (tree characteristics), and the anthroposphere (dike construction). Carbon sequestration in aboveground biomass of riparian Quercus robur L. and Fraxinus excelsior L. was modeled (1) over time using boosted regression tree analysis (BRT) on cross-datable trees characterized by equal annual growth ring patterns and (2) across space using a subsequent classification and regression tree analysis (CART) on cross-datable and not cross-datable trees. While C sequestration of cross-datable Q. robur responded to precipitation and temperature, cross-datable F. excelsior also responded to a low Danube river water level. However, CART revealed that C sequestration over time is governed by tree height and parameters that vary over space (magnitude of fluctuation in the groundwater table, vertical distance to mean river water level, and longitudinal distance to upstream end of the study area). Thus, a uniform response to climatic drivers of aboveground C sequestration in Q. robur was only detectable in trees of an intermediate height class and in taller trees (>21.8m) on sites where the groundwater table fluctuated little (≤0.9m). The detection of climatic drivers and the river water level in F. excelsior depended on sites at lower altitudes above the mean river water level (≤2.7m) and along a less dynamic downstream section of the study area. Our approach indicates unexploited opportunities of understanding the interplay of different environmental drivers in aboveground C sequestration. Results may support species-specific and

Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

Science.gov (United States)

Guo, Huey-Ming; Shyu, Yea-Ing Lotus; Chang, Her-Kun

2006-01-01

In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data

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Yingxin Gu

2016-11-01

Full Text Available Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD between the predicted and actual NDVI (scaled NDVI, value from 0–200 and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4, which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

An efficient and extensible approach for compressing phylogenetic trees

KAUST Repository

Matthews, Suzanne J; Williams, Tiffani L

2011-01-01

Background: Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend

An efficient and extensible approach for compressing phylogenetic trees.

Science.gov (United States)

Matthews, Suzanne J; Williams, Tiffani L

2011-10-18

Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend our TreeZip algorithm by handling trees with weighted branches. Furthermore, by using the compressed TreeZip file as input, we have designed an extensible decompressor that can extract subcollections of trees, compute majority and strict consensus trees, and merge tree collections using set operations such as union, intersection, and set difference. On unweighted phylogenetic trees, TreeZip is able to compress Newick files in excess of 98%. On weighted phylogenetic trees, TreeZip is able to compress a Newick file by at least 73%. TreeZip can be combined with 7zip with little overhead, allowing space savings in excess of 99% (unweighted) and 92%(weighted). Unlike TreeZip, 7zip is not immune to branch rotations, and performs worse as the level of variability in the Newick string representation increases. Finally, since the TreeZip compressed text (TRZ) file contains all the semantic information in a collection of trees, we can easily filter and decompress a subset of trees of interest (such as the set of unique trees), or build the resulting consensus tree in a matter of seconds. We also show the ease of which set operations can be performed on TRZ files, at speeds quicker than those performed on Newick or 7zip compressed Newick files, and without loss of space savings. TreeZip is an efficient approach for compressing large collections of phylogenetic trees. The semantic and compact nature of the TRZ file allow it to be operated upon directly and quickly, without a need to decompress the original Newick file. We believe that TreeZip will be vital for compressing and archiving trees in the biological community.

Computer-oriented approach to fault-tree construction

International Nuclear Information System (INIS)

Salem, S.L.; Apostolakis, G.E.; Okrent, D.

1976-11-01

A methodology for systematically constructing fault trees for general complex systems is developed and applied, via the Computer Automated Tree (CAT) program, to several systems. A means of representing component behavior by decision tables is presented. The method developed allows the modeling of components with various combinations of electrical, fluid and mechanical inputs and outputs. Each component can have multiple internal failure mechanisms which combine with the states of the inputs to produce the appropriate output states. The generality of this approach allows not only the modeling of hardware, but human actions and interactions as well. A procedure for constructing and editing fault trees, either manually or by computer, is described. The techniques employed result in a complete fault tree, in standard form, suitable for analysis by current computer codes. Methods of describing the system, defining boundary conditions and specifying complex TOP events are developed in order to set up the initial configuration for which the fault tree is to be constructed. The approach used allows rapid modifications of the decision tables and systems to facilitate the analysis and comparison of various refinements and changes in the system configuration and component modeling

Use of GLM approach to assess the responses of tropical trees to urban air pollution in relation to leaf functional traits and tree characteristics.

Science.gov (United States)

Mukherjee, Arideep; Agrawal, Madhoolika

2018-05-15

Responses of urban vegetation to air pollution stress in relation to their tolerance and sensitivity have been extensively studied, however, studies related to air pollution responses based on different leaf functional traits and tree characteristics are limited. In this paper, we have tried to assess combined and individual effects of major air pollutants PM 10 (particulate matter ≤ 10 µm), TSP (total suspended particulate matter), SO 2 (sulphur dioxide), NO 2 (nitrogen dioxide) and O 3 (ozone) on thirteen tropical tree species in relation to fifteen leaf functional traits and different tree characteristics. Stepwise linear regression a general linear modelling approach was used to quantify the pollution response of trees against air pollutants. The study was performed for six successive seasons for two years in three distinct urban areas (traffic, industrial and residential) of Varanasi city in India. At all the study sites, concentrations of air pollutants, specifically PM (particulate matter) and NO 2 were above the specified standards. Distinct variations were recorded in all the fifteen leaf functional traits with pollution load. Caesalpinia sappan was identified as most tolerant species followed by Psidium guajava, Dalbergia sissoo and Albizia lebbeck. Stepwise regression analysis identified maximum response of Eucalyptus citriodora and P. guajava to air pollutants explaining overall 59% and 58% variability's in leaf functional traits, respectively. Among leaf functional traits, maximum effect of air pollutants was observed on non-enzymatic antioxidants followed by photosynthetic pigments and leaf water status. Among the pollutants, PM was identified as the major stress factor followed by O 3 explaining 47% and 33% variability's in leaf functional traits. Tolerance and pollution response were regulated by different tree characteristics such as height, canopy size, leaf from, texture and nature of tree. Outcomes of this study will help in urban forest

An efficient and extensible approach for compressing phylogenetic trees

KAUST Repository

Matthews, Suzanne J

2011-01-01

Background: Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend our TreeZip algorithm by handling trees with weighted branches. Furthermore, by using the compressed TreeZip file as input, we have designed an extensible decompressor that can extract subcollections of trees, compute majority and strict consensus trees, and merge tree collections using set operations such as union, intersection, and set difference.Results: On unweighted phylogenetic trees, TreeZip is able to compress Newick files in excess of 98%. On weighted phylogenetic trees, TreeZip is able to compress a Newick file by at least 73%. TreeZip can be combined with 7zip with little overhead, allowing space savings in excess of 99% (unweighted) and 92%(weighted). Unlike TreeZip, 7zip is not immune to branch rotations, and performs worse as the level of variability in the Newick string representation increases. Finally, since the TreeZip compressed text (TRZ) file contains all the semantic information in a collection of trees, we can easily filter and decompress a subset of trees of interest (such as the set of unique trees), or build the resulting consensus tree in a matter of seconds. We also show the ease of which set operations can be performed on TRZ files, at speeds quicker than those performed on Newick or 7zip compressed Newick files, and without loss of space savings.Conclusions: TreeZip is an efficient approach for compressing large collections of phylogenetic trees. The semantic and compact nature of the TRZ file allow it to be operated upon directly and quickly, without a need to decompress the original Newick file. We believe that TreeZip will be vital for compressing and archiving trees in the biological community. © 2011 Matthews and Williams; licensee BioMed Central Ltd.

bayesQR: A Bayesian Approach to Quantile Regression

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Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

City housing atmospheric pollutant impact on emergency visit for asthma: A classification and regression tree approach.

Science.gov (United States)

Mazenq, Julie; Dubus, Jean-Christophe; Gaudart, Jean; Charpin, Denis; Viudes, Gilles; Noel, Guilhem

2017-11-01

Particulate matter, nitrogen dioxide (NO 2 ) and ozone are recognized as the three pollutants that most significantly affect human health. Asthma is a multifactorial disease. However, the place of residence has rarely been investigated. We compared the impact of air pollution, measured near patients' homes, on emergency department (ED) visits for asthma or trauma (controls) within the Provence-Alpes-Côte-d'Azur region. Variables were selected using classification and regression trees on asthmatic and control population, 3-99 years, visiting ED from January 1 to December 31, 2013. Then in a nested case control study, randomization was based on the day of ED visit and on defined age groups. Pollution, meteorological, pollens and viral data measured that day were linked to the patient's ZIP code. A total of 794,884 visits were reported including 6250 for asthma and 278,192 for trauma. Factors associated with an excess risk of emergency visit for asthma included short-term exposure to NO 2 , female gender, high viral load and a combination of low temperature and high humidity. Short-term exposures to high NO 2 concentrations, as assessed close to the homes of the patients, were significantly associated with asthma-related ED visits in children and adults. Copyright © 2017 Elsevier Ltd. All rights reserved.

New approaches to evaluating fault trees

International Nuclear Information System (INIS)

Sinnamon, R.M.; Andrews, J.D.

1997-01-01

Fault Tree Analysis is now a widely accepted technique to assess the probability and frequency of system failure in many industries. For complex systems an analysis may produce hundreds of thousands of combinations of events which can cause system failure (minimal cut sets). The determination of these cut sets can be a very time consuming process even on modern high speed digital computers. Computerised methods, such as bottom-up or top-down approaches, to conduct this analysis are now so well developed that further refinement is unlikely to result in vast reductions in computer time. It is felt that substantial improvement in computer utilisation will only result from a completely new approach. This paper describes the use of a Binary Decision Diagram for Fault Tree Analysis and some ways in which it can be efficiently implemented on a computer. In particular, attention is given to the production of a minimum form of the Binary Decision Diagram by considering the ordering that has to be given to the basic events of the fault tree

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

Science.gov (United States)

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis

Science.gov (United States)

Cohen, Ira L.; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N. S.; Romanczyk, Raymond G.; Karmel, Bernard Z.; Gardner, Judith M.

2016-01-01

In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80%,…

Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer – a classification tree approach

International Nuclear Information System (INIS)

Martin, Michael A; Meyricke, Ramona; O'Neill, Terry; Roberts, Steven

2006-01-01

A critical choice facing breast cancer patients is which surgical treatment – mastectomy or breast conserving surgery (BCS) – is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of 'propensity' is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients

Application of Logistic Regression Tree Model in Determining Habitat Distribution of Astragalus verus

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M. Saki

2013-03-01

Full Text Available The relationship between plant species and environmental factors has always been a central issue in plant ecology. With rising power of statistical techniques, geo-statistics and geographic information systems (GIS, the development of predictive habitat distribution models of organisms has rapidly increased in ecology. This study aimed to evaluate the ability of Logistic Regression Tree model to create potential habitat map of Astragalus verus. This species produces Tragacanth and has economic value. A stratified- random sampling was applied to 100 sites (50 presence- 50 absence of given species, and produced environmental and edaphic factors maps by using Kriging and Inverse Distance Weighting methods in the ArcGIS software for the whole study area. Relationships between species occurrence and environmental factors were determined by Logistic Regression Tree model and extended to the whole study area. The results indicated species occurrence has strong correlation with environmental factors such as mean daily temperature and clay, EC and organic carbon content of the soil. Species occurrence showed direct relationship with mean daily temperature and clay and organic carbon, and inverse relationship with EC. Model accuracy was evaluated both by Cohen’s kappa statistics (κ and by area under Receiver Operating Characteristics curve based on independent test data set. Their values (kappa=0.9, Auc of ROC=0.96 indicated the high power of LRT to create potential habitat map on local scales. This model, therefore, can be applied to recognize potential sites for rangeland reclamation projects.

Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

Science.gov (United States)

Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Science.gov (United States)

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Accurate phylogenetic tree reconstruction from quartets: a heuristic approach.

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Reaz, Rezwana; Bayzid, Md Shamsuzzoha; Rahman, M Sohel

2014-01-01

Supertree methods construct trees on a set of taxa (species) combining many smaller trees on the overlapping subsets of the entire set of taxa. A 'quartet' is an unrooted tree over 4 taxa, hence the quartet-based supertree methods combine many 4-taxon unrooted trees into a single and coherent tree over the complete set of taxa. Quartet-based phylogeny reconstruction methods have been receiving considerable attentions in the recent years. An accurate and efficient quartet-based method might be competitive with the current best phylogenetic tree reconstruction methods (such as maximum likelihood or Bayesian MCMC analyses), without being as computationally intensive. In this paper, we present a novel and highly accurate quartet-based phylogenetic tree reconstruction method. We performed an extensive experimental study to evaluate the accuracy and scalability of our approach on both simulated and biological datasets.

Decision trees in epidemiological research.

Science.gov (United States)

Venkatasubramaniam, Ashwini; Wolfson, Julian; Mitchell, Nathan; Barnes, Timothy; JaKa, Meghan; French, Simone

2017-01-01

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].

Science.gov (United States)

Liu, Yang; Lü, Yi-he; Zheng, Hai-feng; Chen, Li-ding

2010-05-01

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.

A best-first tree-searching approach for ML decoding in MIMO system

KAUST Repository

Shen, Chung-An; Eltawil, Ahmed M.; Mondal, Sudip; Salama, Khaled N.

2012-01-01

In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve

Extensions and applications of ensemble-of-trees methods in machine learning

Science.gov (United States)

Bleich, Justin

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of

Decision trees in epidemiological research

Directory of Open Access Journals (Sweden)

Ashwini Venkatasubramaniam

2017-09-01

Full Text Available Abstract Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. Main text We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART technique and the newer Conditional Inference tree (CTree technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Conclusions Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

Forecasting exchange rates: a robust regression approach

OpenAIRE

Preminger, Arie; Franck, Raphael

2005-01-01

The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...

Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

Science.gov (United States)

van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A

2018-02-01

This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.

Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data

Directory of Open Access Journals (Sweden)

Trevor G. Jones

2014-07-01

Full Text Available Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm, 5 (i.e., SWIR: 1.55–1.75 µm and 7 (SWIR: 2.08–2.35 µm were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

Science.gov (United States)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

A knowledge-based approach to the evaluation of fault trees

International Nuclear Information System (INIS)

Hwang, Yann-Jong; Chow, Louis R.; Huang, Henry C.

1996-01-01

A list of critical components is useful for determining the potential problems of a complex system. However, to find this list through evaluating the fault trees is expensive and time consuming. This paper intends to propose an integrated software program which consists of a fault tree constructor, a knowledge base, and an efficient algorithm for evaluating minimal cut sets of a large fault tree. The proposed algorithm uses the approaches of top-down heuristic searching and the probability-based truncation. That makes the evaluation of fault trees obviously efficient and provides critical components for solving the potential problems in complex systems. Finally, some practical fault trees are included to illustrate the results

APPLICATION OF MULTIPLE LOGISTIC REGRESSION, BAYESIAN LOGISTIC AND CLASSIFICATION TREE TO IDENTIFY THE SIGNIFICANT FACTORS INFLUENCING CRASH SEVERITY

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MILAD TAZIK

2017-11-01

Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.

Integrating classification trees with local logistic regression in Intensive Care prognosis.

Science.gov (United States)

Abu-Hanna, Ameen; de Keizer, Nicolette

2003-01-01

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

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Maggi Kelly

2013-08-01

Full Text Available Light detection and ranging (lidar data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA of a canopy height model (CHM. The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2, discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96. Tree detection rates increased for more dominant trees (8–100 percent. The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

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Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

2017-04-01

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R training 2 and R test 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R training 2 and R test 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R training 2 and R test 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Copyright © 2016 Elsevier Ltd. All rights reserved.

A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

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Edwards Scott V

2010-10-01

Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

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Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

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Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

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Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan

2006-01-01

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...

Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines

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Dario Pastrone

2012-01-01

Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.

Analisis Perbandingan Teknik Support Vector Regression (SVR) Dan Decision Tree C4.5 Dalam Data Mining

OpenAIRE

Astuti, Yuniar Andi

2011-01-01

This study examines techniques Support Vector Regression and Decision Tree C4.5 has been used in studies in various fields, in order to know the advantages and disadvantages of both techniques that appear in Data Mining. From the ten studies that use both techniques, the results of the analysis showed that the accuracy of the SVR technique for 59,64% and C4.5 for 76,97% So in this study obtained a statement that C4.5 is better than SVR 097038020

Metabolic activity of tree saps of different origin towards cultured human cells in the light of grade correspondence analysis and multiple regression modeling

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Artur Wnorowski

2017-06-01

Full Text Available Tree saps are nourishing biological media commonly used for beverage and syrup production. Although the nutritional aspect of tree saps is widely acknowledged, the exact relationship between the sap composition, origin, and effect on the metabolic rate of human cells is still elusive. Thus, we collected saps from seven different tree species and conducted composition-activity analysis. Saps from trees of Betulaceae, but not from Salicaceae, Sapindaceae, nor Juglandaceae families, were increasing the metabolic rate of HepG2 cells, as measured using tetrazolium-based assay. Content of glucose, fructose, sucrose, chlorides, nitrates, sulphates, fumarates, malates, and succinates in sap samples varied across different tree species. Grade correspondence analysis clustered trees based on the saps’ chemical footprint indicating its usability in chemotaxonomy. Multiple regression modeling showed that glucose and fumarate present in saps from silver birch (Betula pendula Roth., black alder (Alnus glutinosa Gaertn., and European hornbeam (Carpinus betulus L. are positively affecting the metabolic activity of HepG2 cells.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

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Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

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Christensen Helen

2009-11-01

Full Text Available Abstract Background Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. Results The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. Conclusion The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Bridging process-based and empirical approaches to modeling tree growth

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Harry T. Valentine; Annikki Makela; Annikki Makela

2005-01-01

The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.

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Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-Koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

2016-10-01

Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. The proposed model had an accuracy of 94.84% (. 24.42) in order to correct prediction of the ESD disease. Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.

Groundwater level prediction of landslide based on classification and regression tree

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Yannan Zhao

2016-09-01

Full Text Available According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree (CART model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15% respectively. To compare the support vector machine (SVM model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.

Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

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Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

2012-01-01

agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

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Kritski Afrânio

2006-02-01

Full Text Available Abstract Background Smear negative pulmonary tuberculosis (SNPT accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Prediction of cannabis and cocaine use in adolescence using decision trees and logistic regression

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Alfonso L. Palmer

2010-01-01

Full Text Available Spain is one of the European countries with the highest prevalence of cannabis and cocaine use among young people. The aim of this study was to investigate the factors related to the consumption of cocaine and cannabis among adolescents. A questionnaire was administered to 9,284 students between 14 and 18 years of age in Palma de Mallorca (47.1% boys and 52.9% girls whose mean age was 15.59 years. Logistic regression and decision trees were carried out in order to model the consumption of cannabis and cocaine. The results show the use of legal substances and committing fraudulence or theft are the main variables that raise the odds of consuming cannabis. In boys, cannabis consumption and a family history of drug use increase the odds of consuming cocaine, whereas in girls the use of alcohol, behaviours of fraudulence or theft and difficulty in some personal skills influence their odds of consuming cocaine. Finally, ease of access to the substance greatly raises the odds of consuming cocaine and cannabis in both genders. Decision trees highlight the role of consuming other substances and committing fraudulence or theft. The results of this study gain importance when it comes to putting into practice effective prevention programmes.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

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Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of

A symptom based decision tree approach to boiling water reactor emergency operating procedures

International Nuclear Information System (INIS)

Knobel, R.C.

1984-01-01

This paper describes a Decision Tree approach to development of BWR Emergency Operating Procedures for use by operators during emergencies. This approach utilizes the symptom based Emergency Procedure Guidelines approved for implementation by the USNRC. Included in the paper is a discussion of the relative merits of the event based Emergency Operating Procedures currently in use at USBWR plants. The body of the paper is devoted to a discussion of the Decision Tree Approach to Emergency Operating Procedures soon to be implemented at two United States Boiling Water Reactor plants, why this approach solves many of the problems with procedures indentified in the post accident reviews of Three Mile Island procedures, and why only now is this approach both desirable and feasible. The paper discusses how nuclear plant simulators were involved in the development of the Emergency Operating Procedure decision trees, and in the verification and validation of these procedures. (orig./HP)

Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

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He, Lingjun; Levine, Richard A.; Fan, Juanjuan; Beemer, Joshua; Stronach, Jeanne

2018-01-01

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

Identifying Domain-General and Domain-Specific Predictors of Low Mathematics Performance: A Classification and Regression Tree Analysis

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David J. Purpura

2017-12-01

Full Text Available Many children struggle to successfully acquire early mathematics skills. Theoretical and empirical evidence has pointed to deficits in domain-specific skills (e.g., non-symbolic mathematics skills or domain-general skills (e.g., executive functioning and language as underlying low mathematical performance. In the current study, we assessed a sample of 113 three- to five-year old preschool children on a battery of domain-specific and domain-general factors in the fall and spring of their preschool year to identify Time 1 (fall factors associated with low performance in mathematics knowledge at Time 2 (spring. We used the exploratory approach of classification and regression tree analyses, a strategy that uses step-wise partitioning to create subgroups from a larger sample using multiple predictors, to identify the factors that were the strongest classifiers of low performance for younger and older preschool children. Results indicated that the most consistent classifier of low mathematics performance at Time 2 was children’s Time 1 mathematical language skills. Further, other distinct classifiers of low performance emerged for younger and older children. These findings suggest that risk classification for low mathematics performance may differ depending on children’s age.

Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees.

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Price, B; Gomez, A; Mathys, L; Gardi, O; Schellenberger, A; Ginzler, C; Thürig, E

2017-03-01

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

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Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

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Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

On the quantitative relationships between environmental parameters and heavy metals pollution in Mediterranean soils using GIS regression-trees

DEFF Research Database (Denmark)

Bou Kheir, Rania; Shomar, B.; Greve, Mogens Humlekrog

2014-01-01

Soil heavy metal pollution has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study used Geographic Information Systems (GIS) and regression-tree modeling (196 trees) to precisely quantify...... the relationships between four toxic heavy metals (Ni, Cr, Cd and As) and sixteen environmental parameters (e.g., parent material, slope gradient, proximity to roads, etc.) in the soils of northern Lebanon (as a case study of Mediterranean landscapes), and to detect the most important parameters that can be used...... between 68% and 100%), surroundings of waste areas (48 – 92%), proximity to roads (45 – 82%) and parent materials (57 – 73%) considerably influenced all investigated heavy metals, which is not the case of hydromorphological and soil properties. For instance, hydraulic conductivity (18 – 41%) and pH (23...

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

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Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

DIF Trees: Using Classification Trees to Detect Differential Item Functioning

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Vaughn, Brandon K.; Wang, Qiu

2010-01-01

A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

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Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

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Murtagh, Fionn

2017-06-01

This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.

A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis.

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Seidel, Dominik

2018-01-01

The three-dimensional forest structure affects many ecosystem functions and services provided by forests. As forests are made of trees it seems reasonable to approach their structure by investigating individual tree structure. Based on three-dimensional point clouds from laser scanning, a newly developed holistic approach is presented that enables to calculate the box dimension as a measure of structural complexity of individual trees using fractal analysis. It was found that the box dimension of trees was significantly different among the tested species, among trees belonging to the same species but exposed to different growing conditions (at gap vs. forest interior) or to different kinds of competition (intraspecific vs. interspecific). Furthermore, it was shown that the box dimension is positively related to the trees' growth rate. The box dimension was identified as an easy to calculate measure that integrates the effect of several external drivers of tree structure, such as competition strength and type, while simultaneously providing information on structure-related properties, like tree growth.

Design and analysis of experiments classical and regression approaches with SAS

CERN Document Server

Onyiah, Leonard C

2008-01-01

Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo

Identification of chilling and heat requirements of cherry trees--a statistical approach.

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Luedeling, Eike; Kunz, Achim; Blanke, Michael M

2013-09-01

Most trees from temperate climates require the accumulation of winter chill and subsequent heat during their dormant phase to resume growth and initiate flowering in the following spring. Global warming could reduce chill and hence hamper the cultivation of high-chill species such as cherries. Yet determining chilling and heat requirements requires large-scale controlled-forcing experiments, and estimates are thus often unavailable. Where long-term phenology datasets exist, partial least squares (PLS) regression can be used as an alternative, to determine climatic requirements statistically. Bloom dates of cherry cv. 'Schneiders späte Knorpelkirsche' trees in Klein-Altendorf, Germany, from 24 growing seasons were correlated with 11-day running means of daily mean temperature. Based on the output of the PLS regression, five candidate chilling periods ranging in length from 17 to 102 days, and one forcing phase of 66 days were delineated. Among three common chill models used to quantify chill, the Dynamic Model showed the lowest variation in chill, indicating that it may be more accurate than the Utah and Chilling Hours Models. Based on the longest candidate chilling phase with the earliest starting date, cv. 'Schneiders späte Knorpelkirsche' cherries at Bonn exhibited a chilling requirement of 68.6 ± 5.7 chill portions (or 1,375 ± 178 chilling hours or 1,410 ± 238 Utah chill units) and a heat requirement of 3,473 ± 1,236 growing degree hours. Closer investigation of the distinct chilling phases detected by PLS regression could contribute to our understanding of dormancy processes and thus help fruit and nut growers identify suitable tree cultivars for a future in which static climatic conditions can no longer be assumed. All procedures used in this study were bundled in an R package ('chillR') and are provided as Supplementary materials. The procedure was also applied to leaf emergence dates of walnut (cv. 'Payne') at Davis, California.

A Voronoi interior adjacency-based approach for generating a contour tree

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Chen, Jun; Qiao, Chaofei; Zhao, Renliang

2004-05-01

A contour tree is a good graphical tool for representing the spatial relations of contour lines and has found many applications in map generalization, map annotation, terrain analysis, etc. A new approach for generating contour trees by introducing a Voronoi-based interior adjacency set concept is proposed in this paper. The immediate interior adjacency set is employed to identify all of the children contours of each contour without contour elevations. It has advantages over existing methods such as the point-in-polygon method and the region growing-based method. This new approach can be used for spatial data mining and knowledge discovering, such as the automatic extraction of terrain features and construction of multi-resolution digital elevation model.

Fuzzy set theoretic approach to fault tree analysis | Tyagi ...

African Journals Online (AJOL)

This approach can be widely used to improve the reliability and to reduce the operating cost of a system. The proposed techniques are discussed and illustrated by taking an example of a nuclear power plant. Keywords: Fault tree, Triangular and Trapezoidal fuzzy number, Fuzzy importance, Ranking of fuzzy numbers ...

A computational approach to compare regression modelling strategies in prediction research.

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Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Time series modeling by a regression approach based on a latent process.

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Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Analysis of the impact of recreational trail usage for prioritising management decisions: a regression tree approach

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Tomczyk, Aleksandra; Ewertowski, Marek; White, Piran; Kasprzak, Leszek

2016-04-01

The dual role of many Protected Natural Areas in providing benefits for both conservation and recreation poses challenges for management. Although recreation-based damage to ecosystems can occur very quickly, restoration can take many years. The protection of conservation interests at the same as providing for recreation requires decisions to be made about how to prioritise and direct management actions. Trails are commonly used to divert visitors from the most important areas of a site, but high visitor pressure can lead to increases in trail width and a concomitant increase in soil erosion. Here we use detailed field data on condition of recreational trails in Gorce National Park, Poland, as the basis for a regression tree analysis to determine the factors influencing trail deterioration, and link specific trail impacts with environmental, use related and managerial factors. We distinguished 12 types of trails, characterised by four levels of degradation: (1) trails with an acceptable level of degradation; (2) threatened trails; (3) damaged trails; and (4) heavily damaged trails. Damaged trails were the most vulnerable of all trails and should be prioritised for appropriate conservation and restoration. We also proposed five types of monitoring of recreational trail conditions: (1) rapid inventory of negative impacts; (2) monitoring visitor numbers and variation in type of use; (3) change-oriented monitoring focusing on sections of trail which were subjected to changes in type or level of use or subjected to extreme weather events; (4) monitoring of dynamics of trail conditions; and (5) full assessment of trail conditions, to be carried out every 10-15 years. The application of the proposed framework can enhance the ability of Park managers to prioritise their trail management activities, enhancing trail conditions and visitor safety, while minimising adverse impacts on the conservation value of the ecosystem. A.M.T. was supported by the Polish Ministry of

A conceptual approach to approximate tree root architecture in infinite slope models

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Schmaltz, Elmar; Glade, Thomas

2016-04-01

Vegetation-related properties - particularly tree root distribution and coherent hydrologic and mechanical effects on the underlying soil mantle - are commonly not considered in infinite slope models. Indeed, from a geotechnical point of view, these effects appear to be difficult to be reproduced reliably in a physically-based modelling approach. The growth of a tree and the expansion of its root architecture are directly connected with both intrinsic properties such as species and age, and extrinsic factors like topography, availability of nutrients, climate and soil type. These parameters control four main issues of the tree root architecture: 1) Type of rooting; 2) maximum growing distance to the tree stem (radius r); 3) maximum growing depth (height h); and 4) potential deformation of the root system. Geometric solids are able to approximate the distribution of a tree root system. The objective of this paper is to investigate whether it is possible to implement root systems and the connected hydrological and mechanical attributes sufficiently in a 3-dimensional slope stability model. Hereby, a spatio-dynamic vegetation module should cope with the demands of performance, computation time and significance. However, in this presentation, we focus only on the distribution of roots. The assumption is that the horizontal root distribution around a tree stem on a 2-dimensional plane can be described by a circle with the stem located at the centroid and a distinct radius r that is dependent on age and species. We classified three main types of tree root systems and reproduced the species-age-related root distribution with three respective mathematical solids in a synthetic 3-dimensional hillslope ambience. Thus, two solids in an Euclidian space were distinguished to represent the three root systems: i) cylinders with radius r and height h, whilst the dimension of latter defines the shape of a taproot-system or a shallow-root-system respectively; ii) elliptic

Regression: The Apple Does Not Fall Far From the Tree.

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Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches

Energy Technology Data Exchange (ETDEWEB)

Yoo S.; Yang, Y.; Carbonell, J.

2011-10-24

Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.

Decision Tree Approach to Discovering Fraud in Leasing Agreements

Directory of Open Access Journals (Sweden)

Horvat Ivan

2014-09-01

Full Text Available Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.

A Demographic Approach to Evaluating Tree Population Sustainability

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Corey R. Halpin

2017-02-01

Full Text Available Quantitative criteria for assessing demographic sustainability of tree populations would be useful in forest conservation, as climate change and a growing complex of invasive pests are likely to drive forests outside their historic range of variability. In this paper, we used CANOPY, a spatially explicit, individual‐tree model, to examine the effects of initial size distributions on sustainability of tree populations for 70 northern hardwood stands under current environmental conditions. A demographic sustainability index was calculated as the ratio of future simulated basal area to current basal area, given current demographic structure and density‐dependent demographic equations. Only steeply descending size distributions were indicated to be moderately or highly sustainable (final basal area/initial basal area ≥0.7 over several tree generations. Five of the six principal species had demographic sustainability index values of <0.6 in 40%–84% of the stands. However, at a small landscape scale, nearly all species had mean index values >1. Simulation experiments suggested that a minimum sapling density of 300 per hectare was required to sustain the initial basal area, but further increases in sapling density did not increase basal area because of coincident increases in mortality. A variable slope with high q‐ratios in small size classes was needed to maintain the existing overstory of mature and old‐growth stands. This analytical approach may be useful in identifying stands needing restoration treatments to maintain existing species composition in situations where forests are likely to have future recruitment limitations.

Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

Science.gov (United States)

Galelli, S.; Castelletti, A.

2013-07-01

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees.

Science.gov (United States)

Nattee, Cholwich; Khamsemanan, Nirattaya; Lawtrakul, Luckhana; Toochinda, Pisanu; Hannongbua, Supa

2017-01-01

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K i of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K i values from the proposed model show a strong coefficient of determination, R 2 =0.996, to experimental K i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K i values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K i . Copyright © 2016 Elsevier Inc. All rights reserved.

Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

Directory of Open Access Journals (Sweden)

Junzan Zhou

2015-01-01

Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

Comprehensive database of diameter-based biomass regressions for North American tree species

Science.gov (United States)

Jennifer C. Jenkins; David C. Chojnacky; Linda S. Heath; Richard A. Birdsey

2004-01-01

A database consisting of 2,640 equations compiled from the literature for predicting the biomass of trees and tree components from diameter measurements of species found in North America. Bibliographic information, geographic locations, diameter limits, diameter and biomass units, equation forms, statistical errors, and coefficients are provided for each equation,...

Investigating the Influences of Tree Coverage and Road Density on Property Crime

Directory of Open Access Journals (Sweden)

Chengming Ye

2018-03-01

Full Text Available With the development of Geographic Information Systems (GIS, crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal’s selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS regression, Spatial Lag regression, and Geographically Weighted Regression (GWR models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

VOXEL-BASED APPROACH FOR ESTIMATING URBAN TREE VOLUME FROM TERRESTRIAL LASER SCANNING DATA

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C. Vonderach

2012-07-01

Full Text Available The importance of single trees and the determination of related parameters has been recognized in recent years, e.g. for forest inventories or management. For urban areas an increasing interest in the data acquisition of trees can be observed concerning aspects like urban climate, CO2 balance, and environmental protection. Urban trees differ significantly from natural systems with regard to the site conditions (e.g. technogenic soils, contaminants, lower groundwater level, regular disturbance, climate (increased temperature, reduced humidity and species composition and arrangement (habitus and health status and therefore allometric relations cannot be transferred from natural sites to urban areas. To overcome this problem an extended approach was developed for a fast and non-destructive extraction of branch volume, DBH (diameter at breast height and height of single trees from point clouds of terrestrial laser scanning (TLS. For data acquisition, the trees were scanned with highest scan resolution from several (up to five positions located around the tree. The resulting point clouds (20 to 60 million points are analysed with an algorithm based on voxel (volume elements structure, leading to an appropriate data reduction. In a first step, two kinds of noise reduction are carried out: the elimination of isolated voxels as well as voxels with marginal point density. To obtain correct volume estimates, the voxels inside the stem and branches (interior voxels where voxels contain no laser points must be regarded. For this filling process, an easy and robust approach was developed based on a layer-wise (horizontal layers of the voxel structure intersection of four orthogonal viewing directions. However, this procedure also generates several erroneous "phantom" voxels, which have to be eliminated. For this purpose the previous approach was extended by a special region growing algorithm. In a final step the volume is determined layer-wise based on the

A best-first tree-searching approach for ML decoding in MIMO system

KAUST Repository

Shen, Chung-An

2012-07-28

In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.

Measuring the satisfaction of intensive care unit patient families in Morocco: a regression tree analysis.

Science.gov (United States)

Damghi, Nada; Khoudri, Ibtissam; Oualili, Latifa; Abidi, Khalid; Madani, Naoufel; Zeggwagh, Amine Ali; Abouqal, Redouane

2008-07-01

Meeting the needs of patients' family members becomes an essential part of responsibilities of intensive care unit physicians. The aim of this study was to evaluate the satisfaction of patients' family members using the Arabic version of the Society of Critical Care Medicine's Family Needs Assessment questionnaire and to assess the predictors of family satisfaction using the classification and regression tree method. The authors conducted a prospective study. This study was conducted at a 12-bed medical intensive care unit in Morocco. Family representatives (n = 194) of consecutive patients with a length of stay >48 hrs were included in the study. Intervention was the Society of Critical Care Medicine's Family Needs Assessment questionnaire. Demographic data for relatives included age, gender, relationship with patients, education level, and intensive care unit commuting time. Clinical data for patients included age, gender, diagnoses, intensive care unit length of stay, Acute Physiology and Chronic Health Evaluation, MacCabe index, Therapeutic Interventioning Scoring System, and mechanical ventilation. The Arabic version of the Society of Critical Care Medicine's Family Needs Assessment questionnaire was administered between the third and fifth days after admission. Of family representatives, 81% declared being satisfied with information provided by physicians, 27% would like more information about the diagnosis, 30% about prognosis, and 45% about treatment. In univariate analysis, family satisfaction (small Society of Critical Care Medicine's Family Needs Assessment questionnaire score) increased with a lower family education level (p = .005), when the information was given by a senior physician (p = .014), and when the Society of Critical Care Medicine's Family Needs Assessment questionnaire was administered by an investigator (p = .002). Multivariate analysis (classification and regression tree) showed that the education level was the predominant factor

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Science.gov (United States)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Directory of Open Access Journals (Sweden)

Brian A. Johnson

2018-01-01

Full Text Available The advent of very high resolution (VHR satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters. The scale parameter (SP is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water through meta-analysis and nonlinear regression tree (RT modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions, and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our

Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

Science.gov (United States)

Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

2014-12-01

This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis.

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Yamashita, Takashi; Kart, Cary S; Noe, Douglas A

2012-12-01

Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources. With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c testing. Using Andersen's health behavior model, need factors appear to dominate the definition of which sub-groups were at greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their self-care behaviors. Limitations and contributions of the study are discussed.

Benefit-based tree valuation

Science.gov (United States)

E.G. McPherson

2007-01-01

Benefit-based tree valuation provides alternative estimates of the fair and reasonable value of trees while illustrating the relative contribution of different benefit types. This study compared estimates of tree value obtained using cost- and benefit-based approaches. The cost-based approach used the Council of Landscape and Tree Appraisers trunk formula method, and...

Partitioning of Multivariate Phenotypes using Regression Trees Reveals Complex Patterns of Adaptation to Climate across the Range of Black Cottonwood (Populus trichocarpa

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Regis Wendpouire Oubida

2015-03-01

Full Text Available Local adaptation to climate in temperate forest trees involves the integration of multiple physiological, morphological, and phenological traits. Latitudinal clines are frequently observed for these traits, but environmental constraints also track longitude and altitude. We combined extensive phenotyping of 12 candidate adaptive traits, multivariate regression trees, quantitative genetics, and a genome-wide panel of SNP markers to better understand the interplay among geography, climate, and adaptation to abiotic factors in Populus trichocarpa. Heritabilities were low to moderate (0.13 to 0.32 and population differentiation for many traits exceeded the 99th percentile of the genome-wide distribution of FST, suggesting local adaptation. When climate variables were taken as predictors and the 12 traits as response variables in a multivariate regression tree analysis, evapotranspiration (Eref explained the most variation, with subsequent splits related to mean temperature of the warmest month, frost-free period (FFP, and mean annual precipitation (MAP. These grouping matched relatively well the splits using geographic variables as predictors: the northernmost groups (short FFP and low Eref had the lowest growth, and lowest cold injury index; the southern British Columbia group (low Eref and intermediate temperatures had average growth and cold injury index; the group from the coast of California and Oregon (high Eref and FFP had the highest growth performance and the highest cold injury index; and the southernmost, high-altitude group (with high Eref and low FFP performed poorly, had high cold injury index, and lower water use efficiency. Taken together, these results suggest variation in both temperature and water availability across the range shape multivariate adaptive traits in poplar.

Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant

International Nuclear Information System (INIS)

Chan, Yea-Kuang; Tsai, Yu-Ching

2017-01-01

The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant

Energy Technology Data Exchange (ETDEWEB)

Chan, Yea-Kuang; Tsai, Yu-Ching [Institute of Nuclear Energy Research, Taoyuan City, Taiwan (China). Nuclear Engineering Division

2017-03-15

The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

Bayesian approach to errors-in-variables in regression models

Science.gov (United States)

Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

Efficient fault tree handling - the Asea-Atom approach

International Nuclear Information System (INIS)

Ericsson, G.; Knochenhauer, M.; Mills, R.

1985-01-01

In recent years there has been a trend in Swedish Probabilistic Safety Analysis (PSA) work towards coordination of the tools and methods used, in order to facilitate exchange of information and review. Thus, standardized methods for fault tree drawing and basic event coding have been developed as well as a number of computer codes for fault tree handling. The computer code used by Asea-Atom is called SUPER-TREE. As indicated by the name, the key feature is the concept of one super tree containing all the information necessary in the fault tree analysis, i.e. system fault trees, sequence fault trees and component data base. The code has proved to allow great flexibility in the choice of level of detail in the analysis

A new approach to enhance the performance of decision tree for classifying gene expression data.

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Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Pattern recognition of spruce trees. An integrated, analytical approach to forest damage

International Nuclear Information System (INIS)

Simmleit, N.; Schulten, H.R.

1989-01-01

In-source pyrolysis-field ionization mass spectrometry was used to fingerprint old needles taken from 90-year-old Norway spruce trees (Picea abies) grown in the Taunus mountains (Federal Republic of Germany). Biometric, physiological variables and elemental compositions of needle and forest soil samples were gathered for the same trees. The mass spectral and conventional data sets were evaluated by principal-component and multiple regression analysis. The results indicate that the mass signal pattern of antioxidants, the soil acidity, the water status, and the nutritional supply of the plant contribute most to the variance of damage symptoms observed in the forest stand investigated. The visual needle loss of the canopy can be predicted by antioxidant, soil acidity, and water status parameters, whereas a further classification according to the discoloration of the needles can only be achieved by adding a soil nutrient component. It is emphasized that multivariate statistical evaluation of complex data sets should be used for the investigation of environmental problems

Quantifying multi-dimensional functional trait spaces of trees: empirical versus theoretical approaches

Science.gov (United States)

Ogle, K.; Fell, M.; Barber, J. J.

2016-12-01

Empirical, field studies of plant functional traits have revealed important trade-offs among pairs or triplets of traits, such as the leaf (LES) and wood (WES) economics spectra. Trade-offs include correlations between leaf longevity (LL) vs specific leaf area (SLA), LL vs mass-specific leaf respiration rate (RmL), SLA vs RmL, and resistance to breakage vs wood density. Ordination analyses (e.g., PCA) show groupings of traits that tend to align with different life-history strategies or taxonomic groups. It is unclear, however, what underlies such trade-offs and emergent spectra. Do they arise from inherent physiological constraints on growth, or are they more reflective of environmental filtering? The relative importance of these mechanisms has implications for predicting biogeochemical cycling, which is influenced by trait distributions of the plant community. We address this question using an individual-based model of tree growth (ACGCA) to quantify the theoretical trait space of trees that emerges from physiological constraints. ACGCA's inputs include 32 physiological, anatomical, and allometric traits, many of which are related to the LES and WES. We fit ACGCA to 1.6 million USFS FIA observations of tree diameters and heights to obtain vectors of trait values that produce realistic growth, and we explored the structure of this trait space. No notable correlations emerged among the 496 trait pairs, but stepwise regressions revealed complicated multi-variate structure: e.g., relationships between pairs of traits (e.g., RmL and SLA) are governed by other traits (e.g., LL, radiation-use efficiency [RUE]). We also simulated growth under various canopy gap scenarios that impose varying degrees of environmental filtering to explore the multi-dimensional trait space (hypervolume) of trees that died vs survived. The centroid and volume of the hypervolumes differed among dead and live trees, especially under gap conditions leading to low mortality. Traits most predictive

Modeling Caribbean tree stem diameters from tree height and crown width measurements

Science.gov (United States)

Thomas Brandeis; KaDonna Randolph; Mike Strub

2009-01-01

Regression models to predict diameter at breast height (DBH) as a function of tree height and maximum crown radius were developed for Caribbean forests based on data collected by the U.S. Forest Service in the Commonwealth of Puerto Rico and Territory of the U.S. Virgin Islands. The model predicting DBH from tree height fit reasonably well (R2 = 0.7110), with...

Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees

Directory of Open Access Journals (Sweden)

Josef Smolle

2001-01-01

Full Text Available Objective: To evaluate the feasibility of the CART (Classification and Regression Tree procedure for the recognition of microscopic structures in tissue counter analysis. Methods: Digital microscopic images of H&E stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. Results: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p < 0.001. Conclusions: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

Online monitoring and conditional regression tree test: Useful tools for a better understanding of combined sewer network behavior.

Science.gov (United States)

Bersinger, T; Bareille, G; Pigot, T; Bru, N; Le Hécho, I

2018-06-01

A good knowledge of the dynamic of pollutant concentration and flux in a combined sewer network is necessary when considering solutions to limit the pollutants discharged by combined sewer overflow (CSO) into receiving water during wet weather. Identification of the parameters that influence pollutant concentration and flux is important. Nevertheless, few studies have obtained satisfactory results for the identification of these parameters using statistical tools. Thus, this work uses a large database of rain events (116 over one year) obtained via continuous measurement of rainfall, discharge flow and chemical oxygen demand (COD) estimated using online turbidity for the identification of these parameters. We carried out a statistical study of the parameters influencing the maximum COD concentration, the discharge flow and the discharge COD flux. In this study a new test was used that has never been used in this field: the conditional regression tree test. We have demonstrated that the antecedent dry weather period, the rain event average intensity and the flow before the event are the three main factors influencing the maximum COD concentration during a rainfall event. Regarding the discharge flow, it is mainly influenced by the overall rainfall height but not by the maximum rainfall intensity. Finally, COD discharge flux is influenced by the discharge volume and the maximum COD concentration. Regression trees seem much more appropriate than common tests like PCA and PLS for this type of study as they take into account the thresholds and cumulative effects of various parameters as a function of the target variable. These results could help to improve sewer and CSO management in order to decrease the discharge of pollutants into receiving waters. Copyright © 2017 Elsevier B.V. All rights reserved.

Model-Independent Evaluation of Tumor Markers and a Logistic-Tree Approach to Diagnostic Decision Support

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Weizeng Ni

2014-01-01

Full Text Available Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.

Urban tree mortality: a primer on demographic approaches

Science.gov (United States)

Lara A. Roman; John J. Battles; Joe R. McBride

2016-01-01

Realizing the benefits of tree planting programs depends on tree survival. Projections of urban forest ecosystem services and cost-benefit analyses are sensitive to assumptions about tree mortality rates. Long-term mortality data are needed to improve the accuracy of these models and optimize the public investment in tree planting. With more accurate population...

Detection of Differential Item Functioning with Nonlinear Regression: A Non-IRT Approach Accounting for Guessing

Science.gov (United States)

Drabinová, Adéla; Martinková, Patrícia

2017-01-01

In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

Using boosted regression trees to predict the near-saturated hydraulic conductivity of undisturbed soils

Science.gov (United States)

Koestel, John; Bechtold, Michel; Jorda, Helena; Jarvis, Nicholas

2015-04-01

The saturated and near-saturated hydraulic conductivity of soil is of key importance for modelling water and solute fluxes in the vadose zone. Hydraulic conductivity measurements are cumbersome at the Darcy scale and practically impossible at larger scales where water and solute transport models are mostly applied. Hydraulic conductivity must therefore be estimated from proxy variables. Such pedotransfer functions are known to work decently well for e.g. water retention curves but rather poorly for near-saturated and saturated hydraulic conductivities. Recently, Weynants et al. (2009, Revisiting Vereecken pedotransfer functions: Introducing a closed-form hydraulic model. Vadose Zone Journal, 8, 86-95) reported a coefficients of determination of 0.25 (validation with an independent data set) for the saturated hydraulic conductivity from lab-measurements of Belgian soil samples. In our study, we trained boosted regression trees on a global meta-database containing tension-disk infiltrometer data (see Jarvis et al. 2013. Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrology & Earth System Sciences, 17, 5185-5195) to predict the saturated hydraulic conductivity (Ks) and the conductivity at a tension of 10 cm (K10). We found coefficients of determination of 0.39 and 0.62 under a simple 10-fold cross-validation for Ks and K10. When carrying out the validation folded over the data-sources, i.e. the source publications, we found that the corresponding coefficients of determination reduced to 0.15 and 0.36, respectively. We conclude that the stricter source-wise cross-validation should be applied in future pedotransfer studies to prevent overly optimistic validation results. The boosted regression trees also allowed for an investigation of relevant predictors for estimating the near-saturated hydraulic conductivity. We found that land use and bulk density were most important to predict Ks. We also observed that Ks is large in fine

Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance

Science.gov (United States)

2013-01-01

Background Constructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation. Results We present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem. We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees. Conclusions Our new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as

Crime Modeling using Spatial Regression Approach

Science.gov (United States)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

A parameter tree approach to estimating system sensitivities to parameter sets

International Nuclear Information System (INIS)

Jarzemba, M.S.; Sagar, B.

2000-01-01

A post-processing technique for determining relative system sensitivity to groups of parameters and system components is presented. It is assumed that an appropriate parametric model is used to simulate system behavior using Monte Carlo techniques and that a set of realizations of system output(s) is available. The objective of our technique is to analyze the input vectors and the corresponding output vectors (that is, post-process the results) to estimate the relative sensitivity of the output to input parameters (taken singly and as a group) and thereby rank them. This technique is different from the design of experimental techniques in that a partitioning of the parameter space is not required before the simulation. A tree structure (which looks similar to an event tree) is developed to better explain the technique. Each limb of the tree represents a particular combination of parameters or a combination of system components. For convenience and to distinguish it from the event tree, we call it the parameter tree. To construct the parameter tree, the samples of input parameter values are treated as either a '+' or a '-' based on whether or not the sampled parameter value is greater than or less than a specified branching criterion (e.g., mean, median, percentile of the population). The corresponding system outputs are also segregated into similar bins. Partitioning the first parameter into a '+' or a '-' bin creates the first level of the tree containing two branches. At the next level, realizations associated with each first-level branch are further partitioned into two bins using the branching criteria on the second parameter and so on until the tree is fully populated. Relative sensitivities are then inferred from the number of samples associated with each branch of the tree. The parameter tree approach is illustrated by applying it to a number of preliminary simulations of the proposed high-level radioactive waste repository at Yucca Mountain, NV. Using a

Regression Nodes: Extending attack trees with data from social sciences

NARCIS (Netherlands)

Bullee, Jan-Willem; Montoya, L.; Pieters, Wolter; Junger, Marianne; Hartel, Pieter H.

In the field of security, attack trees are often used to assess security vulnerabilities probabilistically in relation to multi-step attacks. The nodes are usually connected via AND-gates, where all children must be executed, or via OR-gates, where only one action is necessary for the attack step to

Comparing Kriging and Regression Approaches for Mapping Soil Clay Content in a diverse Danish Landscape

DEFF Research Database (Denmark)

Adhikari, Kabindra; Bou Kheir, Rania; Greve, Mette Balslev

2013-01-01

Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable techn...... the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark....... technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6...

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

Increased tree establishment in Lithuanian peat bogs--insights from field and remotely sensed approaches.

Science.gov (United States)

Edvardsson, Johannes; Šimanauskienė, Rasa; Taminskas, Julius; Baužienė, Ieva; Stoffel, Markus

2015-02-01

Over the past century an ongoing establishment of Scots pine (Pinus sylvestris L.), sometimes at accelerating rates, is noted at three studied Lithuanian peat bogs, namely Kerėplis, Rėkyva and Aukštumala, all representing different degrees of tree coverage and geographic settings. Present establishment rates seem to depend on tree density on the bog surface and are most significant at sparsely covered sites where about three-fourth of the trees have established since the mid-1990s, whereas the initial establishment in general was during the early to mid-19th century. Three methods were used to detect, compare and describe tree establishment: (1) tree counts in small plots, (2) dendrochronological dating of bog pine trees, and (3) interpretation of aerial photographs and historical maps of the study areas. In combination, the different approaches provide complimentary information but also weigh up each other's drawbacks. Tree counts in plots provided a reasonable overview of age class distributions and enabled capturing of the most recently established trees with ages less than 50 years. The dendrochronological analysis yielded accurate tree ages and a good temporal resolution of long-term changes. Tree establishment and spread interpreted from aerial photographs and historical maps provided a good overview of tree spread and total affected area. It also helped to verify the results obtained with the other methods and an upscaling of findings to the entire peat bogs. The ongoing spread of trees in predominantly undisturbed peat bogs is related to warmer and/or drier climatic conditions, and to a minor degree to land-use changes. Our results therefore provide valuable insights into vegetation changes in peat bogs, also with respect to bog response to ongoing and future climatic changes. Copyright © 2014 Elsevier B.V. All rights reserved.

Sequence comparison alignment-free approach based on suffix tree and L-words frequency.

Science.gov (United States)

Soares, Inês; Goios, Ana; Amorim, António

2012-01-01

The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions). In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L-L-words--in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.

Interspecific variation in tree seedlings establishment in canopy gaps in relation to tree density

Energy Technology Data Exchange (ETDEWEB)

Reader, R.J.; Bonser, S.P.; Duralia, T.E.; Bricker, B.D. [Guelph Univ., ON (Canada). Dept. of Botany

1995-10-01

We tested whether interspecific variation in tree seedling establishment in canopy gaps was significantly related to interspecific variation in tree density, for seven deciduous forest tree species (Quercus alba, Hamamelis virginiana, Acer rubrum, Sassafras albidum, Quercus rubra, Prunus serotina, Ostrya virginiana). For each species, seedling establishment was calculated as the difference in seedling density before experimental gap creation versus three years after gap creation. In each of the six experimentally-created gap types (33% or 66% removal of tree basal area from 0.01ha, 0.05ha or 0.20ha patches), differences in seedling establishment among species were significantly related to differences in their density in the tree canopy. A regression model with log{sub e} tree density as the independent variable accounted for between 93% and 98% of interspecific variation in seedling establishment. Our results provide empirical support for models of tree dynamics in gaps that assume seedling establishment depends on canopy tree density. 17 refs, 1 fig, 3 tabs

Tree-based approaches for understanding growth patterns in the European regions

Directory of Open Access Journals (Sweden)

Paola Annoni

2016-09-01

transport infrastructure, human capital, labour market and research and innovation - and incorporates the institutional quality and two variables which aim to reflect the macroeconomic conditions in which the regions operate. Given the scarcity of reliable and comparable regional data at the EU level, large part of the analysis has been devoted to build reliable and consistent panel data on potential factors of growth. Two non-parametric, decision-tree techniques, randomized Classication and Regression Tree and Multivariate Adaptive Regression Splines, are employed for their ability to address data complexities such as non-linearities and interaction eects, which are generally a challenge for more traditional statistical procedures such as linear regression. Results show that the dependence of growth rates on the factors included in the analysis is clearly non-linear with important factor interactions. This means that growth is determined by the simultaneous presence of multiple stimulus factors rather than the presence of a single area of excellence. Results also conrm the critical importance of the macroeconomic framework together with human capital as major drivers of economic growth of countries and regions. This is overall in line with most of the economic literature, which has persistently underlined the major role of these factors on economic growth but with the novelty that the macroeconomic conditions are here incorporated. Human capital also has an important role, with low-skilled workforce having a higher detrimental eect on growth than high-skilled. Not surprisingly, other important factors are the quality of governance and, in line with the neoclassical growth theory, the stage of development, with less developed economies growing at a faster pace than the others. The evidence given by the model about the impact of other factors on economic growth such as those on the quality of infrastructure or the level of innovation seems to be more limited and

Dual Regression

OpenAIRE

Spady, Richard; Stouli, Sami

2012-01-01

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

KLASIFIKASI KARAKTERISTIK KECELAKAAN LALU LINTAS DI KOTA DENPASAR DENGAN PENDEKATAN CLASSIFICATION AND REGRESSION TREES (CART

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I GEDE AGUS JIWADIANA

2015-11-01

Full Text Available The aim of this research is to determine the classification characteristics of traffic accidents in Denpasar city in January-July 2014 by using Classification And Regression Trees (CART. Then, for determine the explanatory variables into the main classifier of CART. The result showed that optimum CART generate three terminal node. First terminal node, there are 12 people were classified as heavy traffic accident characteritics with single accident, and second terminal nodes, there are 68 people were classified as minor traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in district road and sub-district road. For third terminal node, there are 291 people were classified as medium traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in municipality road and explanatory variables into the main splitter to make of CART is type of traffic accident with maximum homogeneity measure of 0.03252.

Predicting number of hospitalization days based on health insurance claims data using bagged regression trees.

Science.gov (United States)

Xie, Yang; Schreier, Günter; Chang, David C W; Neubauer, Sandra; Redmond, Stephen J; Lovell, Nigel H

2014-01-01

Healthcare administrators worldwide are striving to both lower the cost of care whilst improving the quality of care given. Therefore, better clinical and administrative decision making is needed to improve these issues. Anticipating outcomes such as number of hospitalization days could contribute to addressing this problem. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We utilized a regression decision tree algorithm, along with insurance claim data from 300,000 individuals over three years, to provide predictions of number of days in hospital in the third year, based on medical admissions and claims data from the first two years. Our method performs well in the general population. For the population aged 65 years and over, the predictive model significantly improves predictions over a baseline method (predicting a constant number of days for each patient), and achieved a specificity of 70.20% and sensitivity of 75.69% in classifying these subjects into two categories of 'no hospitalization' and 'at least one day in hospital'.

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India.

Science.gov (United States)

Singh, Gyanendra; Sachdeva, S N; Pal, Mahesh

2016-11-01

This work examines the application of M5 model tree and conventionally used fixed/random effect negative binomial (FENB/RENB) regression models for accident prediction on non-urban sections of highway in Haryana (India). Road accident data for a period of 2-6 years on different sections of 8 National and State Highways in Haryana was collected from police records. Data related to road geometry, traffic and road environment related variables was collected through field studies. Total two hundred and twenty two data points were gathered by dividing highways into sections with certain uniform geometric characteristics. For prediction of accident frequencies using fifteen input parameters, two modeling approaches: FENB/RENB regression and M5 model tree were used. Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values. M5 model tree provides simple linear equations that are easy to interpret and provide better insight, indicating that this approach can effectively be used as an alternative to RENB approach if the sole purpose is to predict motor vehicle crashes. Sensitivity analysis using M5 model tree also suggests that its results reflect the physical conditions. Both models clearly indicate that to improve safety on Indian highways minor accesses to the highways need to be properly designed and controlled, the service roads to be made functional and dispersion of speeds is to be brought down. Copyright © 2016 Elsevier Ltd. All rights reserved.

Random forests of interaction trees for estimating individualized treatment effects in randomized trials.

Science.gov (United States)

Su, Xiaogang; Peña, Annette T; Liu, Lei; Levine, Richard A

2018-04-29

Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the "separate regression" approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial. Copyright © 2018 John Wiley & Sons, Ltd.

The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.

Science.gov (United States)

Liu, Chunping; Laporte, Audrey; Ferguson, Brian S

2008-09-01

In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency.

Fault tree handbook

International Nuclear Information System (INIS)

Haasl, D.F.; Roberts, N.H.; Vesely, W.E.; Goldberg, F.F.

1981-01-01

This handbook describes a methodology for reliability analysis of complex systems such as those which comprise the engineered safety features of nuclear power generating stations. After an initial overview of the available system analysis approaches, the handbook focuses on a description of the deductive method known as fault tree analysis. The following aspects of fault tree analysis are covered: basic concepts for fault tree analysis; basic elements of a fault tree; fault tree construction; probability, statistics, and Boolean algebra for the fault tree analyst; qualitative and quantitative fault tree evaluation techniques; and computer codes for fault tree evaluation. Also discussed are several example problems illustrating the basic concepts of fault tree construction and evaluation

Coded Splitting Tree Protocols

DEFF Research Database (Denmark)

Sørensen, Jesper Hemming; Stefanovic, Cedomir; Popovski, Petar

2013-01-01

This paper presents a novel approach to multiple access control called coded splitting tree protocol. The approach builds on the known tree splitting protocols, code structure and successive interference cancellation (SIC). Several instances of the tree splitting protocol are initiated, each...... instance is terminated prematurely and subsequently iterated. The combined set of leaves from all the tree instances can then be viewed as a graph code, which is decodable using belief propagation. The main design problem is determining the order of splitting, which enables successful decoding as early...

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways.

Science.gov (United States)

Sun, Yahui; Ma, Chenkai; Halgamuge, Saman

2017-12-28

Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

Log and tree sawing times for hardwood mills

Science.gov (United States)

Everette D. Rast

1974-01-01

Data on 6,850 logs and 1,181 trees were analyzed to predict sawing times. For both logs and trees, regression equations were derived that express (in minutes) sawing time per log or tree and per Mbf. For trees, merchantable height is expressed in number of logs as well as in feet. One of the major uses for the tables of average sawing times is as a bench mark against...

Comparing pseudo-absences generation techniques in Boosted Regression Trees models for conservation purposes: A case study on amphibians in a protected area.

Directory of Open Access Journals (Sweden)

Francesco Cerasoli

Full Text Available Boosted Regression Trees (BRT is one of the modelling techniques most recently applied to biodiversity conservation and it can be implemented with presence-only data through the generation of artificial absences (pseudo-absences. In this paper, three pseudo-absences generation techniques are compared, namely the generation of pseudo-absences within target-group background (TGB, testing both the weighted (WTGB and unweighted (UTGB scheme, and the generation at random (RDM, evaluating their performance and applicability in distribution modelling and species conservation. The choice of the target group fell on amphibians, because of their rapid decline worldwide and the frequent lack of guidelines for conservation strategies and regional-scale planning, which instead could be provided through an appropriate implementation of SDMs. Bufo bufo, Salamandrina perspicillata and Triturus carnifex were considered as target species, in order to perform our analysis with species having different ecological and distributional characteristics. The study area is the "Gran Sasso-Monti della Laga" National Park, which hosts 15 Natura 2000 sites and represents one of the most important biodiversity hotspots in Europe. Our results show that the model calibration ameliorates when using the target-group based pseudo-absences compared to the random ones, especially when applying the WTGB. Contrarily, model discrimination did not significantly vary in a consistent way among the three approaches with respect to the tree target species. Both WTGB and RDM clearly isolate the highly contributing variables, supplying many relevant indications for species conservation actions. Moreover, the assessment of pairwise variable interactions and their three-dimensional visualization further increase the amount of useful information for protected areas' managers. Finally, we suggest the use of RDM as an admissible alternative when it is not possible to individuate a suitable set of

Sequence Comparison Alignment-Free Approach Based on Suffix Tree and L-Words Frequency

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Inês Soares

2012-01-01

Full Text Available The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions. In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L—L-words—in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.

A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

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Hsin-Lun Wu

Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.

A decision tree approach using silvics to guide planning for forest restoration

Science.gov (United States)

Sharon M. Hermann; John S. Kush; John C. Gilbert

2013-01-01

We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

Science.gov (United States)

Salehzadeh, Reza

2017-01-01

Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

An overview of decision tree applied to power systems

DEFF Research Database (Denmark)

Liu, Leo; Rather, Zakir Hussain; Chen, Zhe

2013-01-01

The corrosive volume of available data in electric power systems motivate the adoption of data mining techniques in the emerging field of power system data analytics. The mainstream of data mining algorithm applied to power system, Decision Tree (DT), also named as Classification And Regression...... Tree (CART), has gained increasing interests because of its high performance in terms of computational efficiency, uncertainty manageability, and interpretability. This paper presents an overview of a variety of DT applications to power systems for better interfacing of power systems with data...... analytics. The fundamental knowledge of CART algorithm is also introduced which is then followed by examples of both classification tree and regression tree with the help of case study for security assessment of Danish power system....

Naive Fault Tree : formulation of the approach

NARCIS (Netherlands)

Rajabalinejad, M

2017-01-01

Naive Fault Tree (NFT) accepts a single value or a range of values for each basic event and returns values for the top event. This accommodates the need of commonly used Fault Trees (FT) for precise data making them prone to data concerns and limiting their area of application. This paper extends

Estimating carbon and showing impacts of drought using satellite data in regression-tree models

Science.gov (United States)

Boyte, Stephen; Wylie, Bruce K.; Howard, Danny; Dahal, Devendra; Gilmanov, Tagir G.

2018-01-01

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

International Nuclear Information System (INIS)

Kamarulzaman Ibrahim; Heng Khai Theng

2005-01-01

Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

Which sociodemographic factors are important on smoking behaviour of high school students? The contribution of classification and regression tree methodology in a broad epidemiological survey.

Science.gov (United States)

Ozge, C; Toros, F; Bayramkaya, E; Camdeviren, H; Sasmaz, T

2006-08-01

The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Using in-class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed.

Establishing a cause and effect relationship for ambient ozone exposure and tree growth in the forest: Progress and an experimental approach

International Nuclear Information System (INIS)

Manning, William J.

2005-01-01

Much has been written about the effects of ambient ozone on tree growth. Cause and effect has been established with seedlings in chambers. Results from multi-year studies with older tree seedlings, in open-top chambers, have been inconclusive, due to chamber effects. Extrapolation of results from chambers to trees in the forest is not possible. Predictive models for forest tree growth reductions caused by ozone have been developed, but not verified. Dendrochronological methods have been used to establish correlations between radial growth reductions in forest trees and ambient ozone exposure. The protective chemical ethylenediurea (EDU) has been used to protect tree seedlings from ozone injury. An experimental approach is advocated here that utilizes forest trees selected for sensitivity and non-sensitivity to ozone, dendrochronological methods, the protective chemical EDU, and monitoring data for ambient ozone, stomatal conductance, soil moisture potential, air temperature, PAR, etc. in long-term investigations to establish cause and effect relationships. - Progress is reviewed and an experimental approach is proposed to demonstrate a cause and effect relationship for ambient ozone and forest tree growth

Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach.

Science.gov (United States)

Ghadie, Mohamed A; Japkowicz, Nathalie; Perkins, Theodore J

2015-08-15

Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself. Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation. tperkins@ohri.ca Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Urban tree growth modeling

Science.gov (United States)

E. Gregory McPherson; Paula J. Peper

2012-01-01

This paper describes three long-term tree growth studies conducted to evaluate tree performance because repeated measurements of the same trees produce critical data for growth model calibration and validation. Several empirical and process-based approaches to modeling tree growth are reviewed. Modeling is more advanced in the fields of forestry and...

Generalized allometric regression to estimate biomass of Populus in short-rotation coppice

Energy Technology Data Exchange (ETDEWEB)

Ben Brahim, Mohammed; Gavaland, Andre; Cabanettes, Alain [INRA Centre de Toulouse, Castanet-Tolosane Cedex (France). Unite Agroforesterie et Foret Paysanne

2000-07-01

Data from four different stands were combined to establish a single generalized allometric equation to estimate above-ground biomass of individual Populus trees grown on short-rotation coppice. The generalized model was performed using diameter at breast height, the mean diameter and the mean height of each site as dependent variables and then compared with the stand-specific regressions using F-test. Results showed that this single regression estimates tree biomass well at each stand and does not introduce bias with increasing diameter.

Does intense monitoring matter? A quantile regression approach

Directory of Open Access Journals (Sweden)

Fekri Ali Shawtari

2017-06-01

Full Text Available Corporate governance has become a centre of attention in corporate management at both micro and macro levels due to adverse consequences and repercussion of insufficient accountability. In this study, we include the Malaysian stock market as sample to explore the impact of intense monitoring on the relationship between intellectual capital performance and market valuation. The objectives of the paper are threefold: i to investigate whether intense monitoring affects the intellectual capital performance of listed companies; ii to explore the impact of intense monitoring on firm value; iii to examine the extent to which the directors serving more than two board committees affects the linkage between intellectual capital performance and firms' value. We employ two approaches, namely, the Ordinary Least Square (OLS and the quantile regression approach. The purpose of the latter is to estimate and generate inference about conditional quantile functions. This method is useful when the conditional distribution does not have a standard shape such as an asymmetric, fat-tailed, or truncated distribution. In terms of variables, the intellectual capital is measured using the value added intellectual coefficient (VAIC, while the market valuation is proxied by firm's market capitalization. The findings of the quantile regression shows that some of the results do not coincide with the results of OLS. We found that intensity of monitoring does not influence the intellectual capital of all firms. It is also evident that intensity of monitoring does not influence the market valuation. However, to some extent, it moderates the relationship between intellectual capital performance and market valuation. This paper contributes to the existing literature as it presents new empirical evidences on the moderating effects of the intensity of monitoring of the board committees on the relationship between performance and intellectual capital.

Frugivores bias seed-adult tree associations through nonrandom seed dispersal: a phylogenetic approach.

Science.gov (United States)

Razafindratsima, Onja H; Dunham, Amy E

2016-08-01

Frugivores are the main seed dispersers in many ecosystems, such that behaviorally driven, nonrandom patterns of seed dispersal are a common process; but patterns are poorly understood. Characterizing these patterns may be essential for understanding spatial organization of fruiting trees and drivers of seed-dispersal limitation in biodiverse forests. To address this, we studied resulting spatial associations between dispersed seeds and adult tree neighbors in a diverse rainforest in Madagascar, using a temporal and phylogenetic approach. Data show that by using fruiting trees as seed-dispersal foci, frugivores bias seed dispersal under conspecific adults and under heterospecific trees that share dispersers and fruiting time with the dispersed species. Frugivore-mediated seed dispersal also resulted in nonrandom phylogenetic associations of dispersed seeds with their nearest adult neighbors, in nine out of the 16 months of our study. However, these nonrandom phylogenetic associations fluctuated unpredictably over time, ranging from clustered to overdispersed. The spatial and phylogenetic template of seed dispersal did not translate to similar patterns of association in adult tree neighborhoods, suggesting the importance of post-dispersal processes in structuring plant communities. Results suggest that frugivore-mediated seed dispersal is important for structuring early stages of plant-plant associations, setting the template for post-dispersal processes that influence ultimate patterns of plant recruitment. Importantly, if biased patterns of dispersal are common in other systems, frugivores may promote tree coexistence in biodiverse forests by limiting the frequency and diversity of heterospecific interactions of seeds they disperse. © 2016 by the Ecological Society of America.

A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

International Nuclear Information System (INIS)

Purba, Julwan Hendry

2014-01-01

Highlights: • We propose a fuzzy-based reliability approach to evaluate basic event reliabilities. • It implements the concepts of failure possibilities and fuzzy sets. • Experts evaluate basic event failure possibilities using qualitative words. • Triangular fuzzy numbers mathematically represent qualitative failure possibilities. • It is a very good alternative for conventional reliability approach. - Abstract: Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment

Software development to assist in fault tree construction

International Nuclear Information System (INIS)

Simic, Z.; Mikulicic, V.

1992-01-01

This paper reviews and classifies fault tree construction methods developed for system safety and reliability. We have outlined two generally different approaches: automatic and interactive fault tree construction. Automatic fault tree approach is no jet enough developed to covering various uses in practice. Interactive approach is intending to be support to the analyst (not vice verse like in automatic approach). The aim is not so high as automatic one but it is accessible. We have favored interactive approach as well because to our opinion the process of fault tree construction is very important for better system understanding. We have described our example of interactive fault tree construction approach. Computer code GIFFT (Graphical Interactive Fault Tree Tool) is in phase of intensive testing and final developing. (author) [hr

A Multi-stakeholder Approach to Moving Beyond Tree Mortality in the Sierra Nevada

Science.gov (United States)

Balachowski, J.; Buluc, L.; Fischer, C.; Ko, J.; Ostoja, S.

2017-12-01

The US Forest Service has estimated that 102 million trees have died in California since 2010. This die off event has been attributed to the combined effects of historical land management practices, fire suppression, insect outbreaks, and climate-related stressors. This tree mortality event represents the largest and most significant ecological disturbance in California in centuries, if not longer. Both scientists and managers recognize the need to rethink our approach to forest management in the face of a changing climate and increasingly frequent, uncharacteristically large wildfires, while budgets and staffing capacity continue to decrease. Addressing the uncertainly in managing under climate change with fewer financial resources will require multiple partners and stakeholders—including federal and state agencies, local governments, and non-governmental organizations—to work together to identify common goals and paths moving forward. The USDA California Climate Hub and USFS Region 5 convened a symposium on drought and tree mortality in July 2017. With nearly 170 attendees across a wide range of sectors, the event provided a meaningful opportunity for reflection, analysis, and consideration of next steps. Among the outcomes of this symposium was the identification of areas in which our capacity for individual and synergistic action is stronger, and those in which it is lacking that will thus require additional attention and effort. From this symposium, which included a series of smaller, stakeholder and partner working groups, we collectively identified research and information needs, possible policy adjustments, future management actions, and funding needs and opportunities. Here, we present these findings and suggest approaches for addressing the current tree mortality event based on the shared interests of multiple, diverse stakeholder groups.

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Science.gov (United States)

Madonsela, Sabelo; Cho, Moses Azong; Ramoelo, Abel; Mutanga, Onisimo; Naidoo, Laven

2018-04-01

Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90 m × 90 m across the study area. Within each plot, all trees with diameter at breast height of >10 cm were sampled and Shannon index - a common measure of species diversity which considers both species richness and abundance - was used to quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity (r2 = 0.21; p NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE = 0.47, p NDVI (RMSE = 0.49) or WCC (RMSE = 0.49) model during the senescence period. The result justifies our assertion

Regression analysis with categorized regression calibrated exposure: some interesting findings

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Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

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Baofeng Shi

2015-01-01

Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

Automatic localization of bifurcations and vessel crossings in digital fundus photographs using location regression

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Niemeijer, Meindert; Dumitrescu, Alina V.; van Ginneken, Bram; Abrámoff, Michael D.

2011-03-01

Parameters extracted from the vasculature on the retina are correlated with various conditions such as diabetic retinopathy and cardiovascular diseases such as stroke. Segmentation of the vasculature on the retina has been a topic that has received much attention in the literature over the past decade. Analysis of the segmentation result, however, has only received limited attention with most works describing methods to accurately measure the width of the vessels. Analyzing the connectedness of the vascular network is an important step towards the characterization of the complete vascular tree. The retinal vascular tree, from an image interpretation point of view, originates at the optic disc and spreads out over the retina. The tree bifurcates and the vessels also cross each other. The points where this happens form the key to determining the connectedness of the complete tree. We present a supervised method to detect the bifurcations and crossing points of the vasculature of the retina. The method uses features extracted from the vasculature as well as the image in a location regression approach to find those locations of the segmented vascular tree where the bifurcation or crossing occurs (from here, POI, points of interest). We evaluate the method on the publicly available DRIVE database in which an ophthalmologist has marked the POI.

Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

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Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

2008-01-01

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

Variances in the projections, resulting from CLIMEX, Boosted Regression Trees and Random Forests techniques

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Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh

2017-08-01

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm ( Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations

Regression trees modeling and forecasting of PM10 air pollution in urban areas

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Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.

2017-10-01

Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.

DupTree: a program for large-scale phylogenetic analyses using gene tree parsimony.

Science.gov (United States)

Wehe, André; Bansal, Mukul S; Burleigh, J Gordon; Eulenstein, Oliver

2008-07-01

DupTree is a new software program for inferring rooted species trees from collections of gene trees using the gene tree parsimony approach. The program implements a novel algorithm that significantly improves upon the run time of standard search heuristics for gene tree parsimony, and enables the first truly genome-scale phylogenetic analyses. In addition, DupTree allows users to examine alternate rootings and to weight the reconciliation costs for gene trees. DupTree is an open source project written in C++. DupTree for Mac OS X, Windows, and Linux along with a sample dataset and an on-line manual are available at http://genome.cs.iastate.edu/CBL/DupTree

Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

Science.gov (United States)

Ypma, Rolf J F; van Ballegooijen, W Marijn; Wallinga, Jacco

2013-11-01

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

Decision tree modeling using R.

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Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

An introduction to tree-structured modeling with application to quality of life data.

Science.gov (United States)

Su, Xiaogang; Azuero, Andres; Cho, June; Kvale, Elizabeth; Meneses, Karen M; McNees, M Patrick

2011-01-01

Investigators addressing nursing research are faced increasingly with the need to analyze data that involve variables of mixed types and are characterized by complex nonlinearity and interactions. Tree-based methods, also called recursive partitioning, are gaining popularity in various fields. In addition to efficiency and flexibility in handling multifaceted data, tree-based methods offer ease of interpretation. The aims of this study were to introduce tree-based methods, discuss their advantages and pitfalls in application, and describe their potential use in nursing research. In this article, (a) an introduction to tree-structured methods is presented, (b) the technique is illustrated via quality of life (QOL) data collected in the Breast Cancer Education Intervention study, and (c) implications for their potential use in nursing research are discussed. As illustrated by the QOL analysis example, tree methods generate interesting and easily understood findings that cannot be uncovered via traditional linear regression analysis. The expanding breadth and complexity of nursing research may entail the use of new tools to improve efficiency and gain new insights. In certain situations, tree-based methods offer an attractive approach that help address such needs.

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

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Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

Recursive Trees for Practical ORAM

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Moataz Tarik

2015-06-01

Full Text Available We present a new, general data structure that reduces the communication cost of recent tree-based ORAMs. Contrary to ORAM trees with constant height and path lengths, our new construction r-ORAM allows for trees with varying shorter path length. Accessing an element in the ORAM tree results in different communication costs depending on the location of the element. The main idea behind r-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees. While this approach results in a worst-case access cost (tree height at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs. Besides reducing communication cost, r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM’s client memory type. To prove r-ORAM’s soundness, we conduct a detailed overflow analysis. r-ORAM’s recursive approach is general in that it can be applied to all recent tree ORAMs, both constant and poly-log client memory ORAMs. Finally, we implement and benchmark r-ORAM in a practical setting to back up our theoretical claims.

An Approach to Indexing and Retrieval of Spatial Data with Reduced R+ Tree and K-NN Query Algorithm

OpenAIRE

S. Palaniappan; T.V. Rajinikanth; A. Govardhan

2015-01-01

Recently, “spatial data bases have been extensively adopted in the recent decade and various methods have been presented to store, browse, search and retrieve spatial objects”. In this study, a method is plotted for retrieving nearest neighbors from spatial data indexed by R+ tree. The approach uses a reduced R+tree for the purpose of representing the spatial data. Initially the spatial data is selected and R+tree is constructed accordingly. Then a function called joining nodes is applied to ...

Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data

DEFF Research Database (Denmark)

Greve, Mogens Humlekrog; Bou Kheir, Rania; Greve, Mette Balslev

2012-01-01

Soil texture is an important soil characteristic that drives crop production and field management, and is the basis for environmental monitoring (including soil quality and sustainability, hydrological and ecological processes, and climate change simulations). The combination of coarse sand, fine...... sand, silt, and clay in soil determines its textural classification. This study used Geographic Information Systems (GIS) and regression-tree modeling to precisely quantify the relationships between the soil texture fractions and different environmental parameters on a national scale, and to detect...... precipitation, seasonal precipitation to statistically explain soil texture fractions field/laboratory measurements (45,224 sampling sites) in the area of interest (Denmark). The developed strongest relationships were associated with clay and silt, variance being equal to 60%, followed by coarse sand (54...

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

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Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p falls (OR = 0.25 with p factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Analysing inequalities in Germany a structured additive distributional regression approach

CERN Document Server

Silbersdorff, Alexander

2017-01-01

This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.

Productivity of the supply system based on whole-tree bundling

Energy Technology Data Exchange (ETDEWEB)

Laitila, J. (Finnish Forest Research Inst., Joensuu (Finland)), Email: juha.laitila@metla.fi; Jylhae, P. (Finnish Forest Research Inst., Kannus (Finland)), Email: paula.jylha@metla.fi; Kaerhae, K. (Metsaeteho Oy, Helsinki (Finland)), Email: kalle.karha@metsateho.fi

2009-07-01

In the present study, time consumption models for bundle harvesting and forwarding were created by applying regression analyses. The time studies related to on-road transportation were created by applying regression analyses. The time studies related to on-road transportation were focused on comparing the terminal times spent on handling of whole-tree bundles and conventional 5-m pulpwood. The number of whole-tree bundles per truck load and the weights of the payloads were also recorded. The forwarding productivity of whole-tree bundles was about double compared to conventional pulpwood and whole-trees. In on-road transportation, the mean loading and unloading time of whole-tree bundles per truck load was 46 % higher compared to that of conventional 5-m pulpwood. The second prototype of the bundle harvester is under construction, and the time studies are to be continued after accomplishing the machine in the autumn 2009. (orig.)

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

Science.gov (United States)

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

An Integrated Approach of Model checking and Temporal Fault Tree for System Safety Analysis

Energy Technology Data Exchange (ETDEWEB)

Koh, Kwang Yong; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

2009-10-15

Digitalization of instruments and control systems in nuclear power plants offers the potential to improve plant safety and reliability through features such as increased hardware reliability and stability, and improved failure detection capability. It however makes the systems and their safety analysis more complex. Originally, safety analysis was applied to hardware system components and formal methods mainly to software. For software-controlled or digitalized systems, it is necessary to integrate both. Fault tree analysis (FTA) which has been one of the most widely used safety analysis technique in nuclear industry suffers from several drawbacks as described in. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA.

A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.

Science.gov (United States)

Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy

2015-06-20

The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology. Copyright © 2015 Elsevier B.V. All rights reserved.

Measuring performance in health care: case-mix adjustment by boosted decision trees.

Science.gov (United States)

Neumann, Anke; Holstein, Josiane; Le Gall, Jean-Roger; Lepage, Eric

2004-10-01

The purpose of this paper is to investigate the suitability of boosted decision trees for the case-mix adjustment involved in comparing the performance of various health care entities. First, we present logistic regression, decision trees, and boosted decision trees in a unified framework. Second, we study in detail their application for two common performance indicators, the mortality rate in intensive care and the rate of potentially avoidable hospital readmissions. For both examples the technique of boosting decision trees outperformed standard prognostic models, in particular linear logistic regression models, with regard to predictive power. On the other hand, boosting decision trees was computationally demanding and the resulting models were rather complex and needed additional tools for interpretation. Boosting decision trees represents a powerful tool for case-mix adjustment in health care performance measurement. Depending on the specific priorities set in each context, the gain in predictive power might compensate for the inconvenience in the use of boosted decision trees.

Retro-regression--another important multivariate regression improvement.

Science.gov (United States)

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

A Classification Regression Tree Analysis to Reduce Balance Impairments and Falls in the Older population: Impact on Resource Utilization and Clinical Decision-Making in USA Rehabilitation Service Delivery

Directory of Open Access Journals (Sweden)

Lucinda Pfalzer

2013-06-01

Full Text Available Background/Purpose: Over 1/3 of adults over age 65 experiences at least one fall each year. This pilot report uses a classification regression tree analysis (CART to model the outcomes for balance/risk of falls from the Gentiva® Safe Strides® Program (SSP. Methods/Outcomes: SSP is a home-based balance/fall prevention program designed to treat root causes of a patient

TREE STEM AND CANOPY BIOMASS ESTIMATES FROM TERRESTRIAL LASER SCANNING DATA

Directory of Open Access Journals (Sweden)

K. Olofsson

2017-10-01

Full Text Available In this study an automatic method for estimating both the tree stem and the tree canopy biomass is presented. The point cloud tree extraction techniques operate on TLS data and models the biomass using the estimated stem and canopy volume as independent variables. The regression model fit error is of the order of less than 5 kg, which gives a relative model error of about 5 % for the stem estimate and 10–15 % for the spruce and pine canopy biomass estimates. The canopy biomass estimate was improved by separating the models by tree species which indicates that the method is allometry dependent and that the regression models need to be recomputed for different areas with different climate and different vegetation.

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

Science.gov (United States)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.; Guo, L.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. One of the main challenges of setting up such a system is finding appropriate model parameter values for ungauged catchments. Previous studies have shown that the transfer of parameter sets from hydrologically similar gauged catchments is one of the best performing regionalization methods. However, a remaining key issue is the identification of suitable descriptors of similarity. In this study, we use decision tree learning to explore parameter set transferability in the full space of catchment descriptors. For this purpose, a semi-distributed rainfall-runoff model is set up for 35 catchments in ten Chinese provinces. Hourly runoff data from in total 858 storm events are used to calibrate the model and to evaluate the performance of parameter set transfers between catchments. We then present a novel technique that uses the splitting rules of classification and regression trees (CART) for finding suitable donor catchments for ungauged target catchments. The ability of the model to detect flood events in assumed ungauged catchments is evaluated in series of leave-one-out tests. We show that CART analysis increases the probability of detection of 10-year flood events in comparison to a conventional measure of physiographic-climatic similarity by up to 20%. Decision tree learning can outperform other regionalization approaches because it generates rules that optimally consider spatial proximity and physical similarity. Spatial proximity can be used as a selection criteria but is skipped in the case where no similar gauged catchments are in the vicinity. We conclude that the CART regionalization concept is particularly suitable for implementation in sparsely gauged and topographically complex environments where a proximity

Meteorological Factors and Tree Characteristics Influencing the Initiation and Rate of Stemflow from Deciduous Trees in an Urban Park

Science.gov (United States)

Schooling, J. T.; Carlyle-Moses, D. E.

2013-12-01

Stemflow, SF, represents that portion of precipitation that is intercepted by a tree's canopy and diverted to the ground at the tree base by flowing along branches and down the bole. The focused input of water and nutrients associated with SF have been shown to be of hydrological and biogeochemical importance in a number of plant communities and forest environments. Although the concentrated water volume and the nutrient / pollutant fluxes associated with SF in urban areas may be highly relevant for stormwater quantity and quality management, they have received only minor study in built environments. In an urban park in Kamloops, British Columbia, Canada, SF volumes generated from 40 deciduous trees representing 22 species were sampled on a precipitation event basis over a period of 16 months. Using this data, we derived the threshold rainfall depth required for SF initiation from each tree by taking the absolute value of the y-intercept of the linear regression of SF volume versus rainfall depth divided by the slope of that regression. The SF discharge rate once the threshold rainfall depth had been reached was taken as the slope of the linear regression equation. Thus, a simplified SF equation was developed: SFv = QSF x (Pg = Pg''), where SFv is stemflow volume (litres), QSF is the discharge rate (litres / mm), and Pg and Pg' represent the precipitation depth and the threshold precipitation depth, respectively. We then examined the influence of meteorological factors (precipitation type [rain / snow / rain + snow], precipitation depth, rainfall intensity, wind speed and direction, and vapour pressure deficit), and tree characteristics (tree diameter at breast height, tree height, leaf size and orientation, bark roughness, crown projection area, leaf area index, canopy cover fraction, branching angle, the proportion of the crown that was comprised of branches, and overlap with other tree canopies) on QSF and Pg' in order to expand on the simplified model and

Species trees from consensus single nucleotide polymorphism (SNP) data: Testing phylogenetic approaches with simulated and empirical data.

Science.gov (United States)

Schmidt-Lebuhn, Alexander N; Aitken, Nicola C; Chuah, Aaron

2017-11-01

Datasets of hundreds or thousands of SNPs (Single Nucleotide Polymorphisms) from multiple individuals per species are increasingly used to study population structure, species delimitation and shallow phylogenetics. The principal software tool to infer species or population trees from SNP data is currently the BEAST template SNAPP which uses a Bayesian coalescent analysis. However, it is computationally extremely demanding and tolerates only small amounts of missing data. We used simulated and empirical SNPs from plants (Australian Craspedia, Asteraceae, and Pelargonium, Geraniaceae) to compare species trees produced (1) by SNAPP, (2) using SVD quartets, and (3) using Bayesian and parsimony analysis with several different approaches to summarising data from multiple samples into one set of traits per species. Our aims were to explore the impact of tree topology and missing data on the results, and to test which data summarising and analyses approaches would best approximate the results obtained from SNAPP for empirical data. SVD quartets retrieved the correct topology from simulated data, as did SNAPP except in the case of a very unbalanced phylogeny. Both methods failed to retrieve the correct topology when large amounts of data were missing. Bayesian analysis of species level summary data scoring the two alleles of each SNP as independent characters and parsimony analysis of data scoring each SNP as one character produced trees with branch length distributions closest to the true trees on which SNPs were simulated. For empirical data, Bayesian inference and Dollo parsimony analysis of data scored allele-wise produced phylogenies most congruent with the results of SNAPP. In the case of study groups divergent enough for missing data to be phylogenetically informative (because of additional mutations preventing amplification of genomic fragments or bioinformatic establishment of homology), scoring of SNP data as a presence/absence matrix irrespective of allele

Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees

Science.gov (United States)

Sayegh, Arwa; Tate, James E.; Ropkins, Karl

2016-02-01

Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx, traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 °C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper

Estimation of structural attributes of walnut trees based on terrestrial laser scanning

Directory of Open Access Journals (Sweden)

J. Estornell

2017-06-01

Full Text Available Juglans regia L. (walnut is a tree of significant economic importance, usually cultivated for its seed used in the food market, and for its wood used in the furniture industry. The aim of this work was to develop regression models to predict crown parameters for walnut trees using a terrestrial laser scanner. A set of 30 trees was selected and the total height, crown height and crown diameter were measured in the field. The trees were also measured by a laser scanner and algorithms were applied to compute the crown volume, crown diameter, total and crown height. Linear regression models were calculated to estimate walnut tree parameters from TLS data. Good results were obtained with values of R2 between 0.90 and 0.98. In addition, to analyze whether coarser point cloud densities might affect the results, the point clouds for all trees were subsampled using different point densities: points every 0.005 m, 0.01 m, 0.05 m, 0.1 m, 0.25 m, 0.5 m, 1 m, and 2 m. New regression models were calculated to estimate field parameters. For total height and crown volume good estimations were obtained from TLS parameters derived for all subsampled point cloud (0.005 m – 2 m.

IND - THE IND DECISION TREE PACKAGE

Science.gov (United States)

Buntine, W.

1994-01-01

A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The

Prediction of Frost Occurrences Using Statistical Modeling Approaches

Directory of Open Access Journals (Sweden)

Hyojin Lee

2016-01-01

Full Text Available We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR, Probability of Detection (POD, and False Alarm Rate (FAR from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.

Tree diversity and species identity effects on soil fungi, protists and animals are context dependent.

Science.gov (United States)

Tedersoo, Leho; Bahram, Mohammad; Cajthaml, Tomáš; Põlme, Sergei; Hiiesalu, Indrek; Anslan, Sten; Harend, Helery; Buegger, Franz; Pritsch, Karin; Koricheva, Julia; Abarenkov, Kessy

2016-02-01

Plant species richness and the presence of certain influential species (sampling effect) drive the stability and functionality of ecosystems as well as primary production and biomass of consumers. However, little is known about these floristic effects on richness and community composition of soil biota in forest habitats owing to methodological constraints. We developed a DNA metabarcoding approach to identify the major eukaryote groups directly from soil with roughly species-level resolution. Using this method, we examined the effects of tree diversity and individual tree species on soil microbial biomass and taxonomic richness of soil biota in two experimental study systems in Finland and Estonia and accounted for edaphic variables and spatial autocorrelation. Our analyses revealed that the effects of tree diversity and individual species on soil biota are largely context dependent. Multiple regression and structural equation modelling suggested that biomass, soil pH, nutrients and tree species directly affect richness of different taxonomic groups. The community composition of most soil organisms was strongly correlated due to similar response to environmental predictors rather than causal relationships. On a local scale, soil resources and tree species have stronger effect on diversity of soil biota than tree species richness per se.

Corporate Social Responsibility and Financial Performance: A Two Least Regression Approach

Directory of Open Access Journals (Sweden)

Alexander Olawumi Dabor

2017-12-01

Full Text Available The objective of this study is to investigate the casuality between corporate social responsibility and firm financial performance. The study employed two least square regression approaches. Fifty-two firms were selected using the scientific method. The findings revealed that corporate social responsibility and firm performance in manufacturing sector are mutually related at 5%. The study recommended that management of manufacturing companies in Nigeria should expend on CSR to boost profitability and corporate image.

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Science.gov (United States)

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Three approaches to deal with inconsistent decision tables - Comparison of decision tree complexity

KAUST Repository

Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

2013-01-01

In inconsistent decision tables, there are groups of rows with equal values of conditional attributes and different decisions (values of the decision attribute). We study three approaches to deal with such tables. Instead of a group of equal rows, we consider one row given by values of conditional attributes and we attach to this row: (i) the set of all decisions for rows from the group (many-valued decision approach); (ii) the most common decision for rows from the group (most common decision approach); and (iii) the unique code of the set of all decisions for rows from the group (generalized decision approach). We present experimental results and compare the depth, average depth and number of nodes of decision trees constructed by a greedy algorithm in the framework of each of the three approaches. © 2013 Springer-Verlag.

Aproximación a la metodología basada en árboles de decisión (CART: Mortalidad hospitalaria del infarto agudo de miocardio Approach to the methodology of classification and regression trees

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Javier Trujillano

2008-02-01

Full Text Available Objetivo: : Realizar una aproximación a la metodología de árboles de decisión tipo CART (Classification and Regression Trees desarrollando un modelo para calcular la probabilidad de muerte hospitalaria en infarto agudo de miocardio (IAM. Método: Se utiliza el conjunto mínimo básico de datos al alta hospitalaria (CMBD de Andalucía, Cataluña, Madrid y País Vasco de los años 2001 y 2002, que incluye los casos con IAM como diagnóstico principal. Los 33.203 pacientes se dividen aleatoriamente (70 y 30 % en grupo de desarrollo (GD = 23.277 y grupo de validación (GV = 9.926. Como CART se utiliza un modelo inductivo basado en el algoritmo de Breiman, con análisis de sensibilidad mediante el índice de Gini y sistema de validación cruzada. Se compara con un modelo de regresión logística (RL y una red neuronal artificial (RNA (multilayer perceptron. Los modelos desarrollados se contrastan en el GV y sus propiedades se comparan con el área bajo la curva ROC (ABC (intervalo de confianza del 95%. Resultados: En el GD el CART con ABC = 0,85 (0,86-0,88, RL 0,87 (0,86-0,88 y RNA 0,85 (0,85-0,86. En el GV el CART con ABC = 0,85 (0,85-0,88, RL 0,86 (0,85-0,88 y RNA 0,84 (0,83-0,86. Conclusiones: Los 3 modelos obtienen resultados similares en su capacidad de discriminación. El modelo CART ofrece como ventaja su simplicidad de uso y de interpretación, ya que las reglas de decisión que generan pueden aplicarse sin necesidad de procesos matemáticos.Objective: To provide an overview of decision trees based on CART (Classification and Regression Trees methodology. As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI. Method: We employed the minimum data set (MDS of Andalusia, Catalonia, Madrid and the Basque Country (2001-2002, which included 33,203 patients with a diagnosis of AMI. The 33,203 patients were randomly divided (70% and 30% into the development (DS

Multiple Additive Regression Trees a Methodology for Predictive Data Mining for Fraud Detection

National Research Council Canada - National Science Library

da

2002-01-01

...) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits...

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

Science.gov (United States)

Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula; Magro, Fernando

2017-01-01

Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Integrated system fault diagnostics utilising digraph and fault tree-based approaches

International Nuclear Information System (INIS)

Bartlett, L.M.; Hurdle, E.E.; Kelly, E.M.

2009-01-01

With the growing intolerance to failures within systems, the issue of fault diagnosis has become ever prevalent. Information concerning these possible failures can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to fault diagnosis within engineering systems have focused on sequential testing procedures and real-time mechanisms. Both methods have been predominantly limited to single fault causes. Latest approaches also consider the issue of multiple faults in reflection to the characteristics of modern day systems designed for high reliability. In addition, a diagnostic capability is required in real time and for changeable system functionality. This paper focuses on two approaches which have been developed to cater for the demands of diagnosis within current engineering systems, namely application of the fault tree analysis technique and the method of digraphs. Both use a comparative approach to consider differences between actual system behaviour and that expected. The procedural guidelines are discussed for each method, with an experimental aircraft fuel system used to test and demonstrate the features of the techniques. The effectiveness of the approaches is compared and their future potential highlighted

A multi-scale relevance vector regression approach for daily urban water demand forecasting

Science.gov (United States)

Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin

2014-09-01

Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.

Wood density variation and tree ring distinctness in Gmelina arborea trees by x-ray densitometry

Directory of Open Access Journals (Sweden)

Roger Moya

2009-03-01

Full Text Available Due to its relationship with other properties, wood density is the main wood quality parameter. Modern, accuratemethods such as X-ray densitometry - are applied to determine the spatial distribution of density in wood sections and to evaluatewood quality. The objectives of this study were to determinate the influence of growing conditions on wood density variation andtree ring demarcation of gmelina trees from fast growing plantations in Costa Rica. The wood density was determined by X-raydensitometry method. Wood samples were cut from gmelina trees and were exposed to low X-rays. The radiographic films weredeveloped and scanned using a 256 gray scale with 1000 dpi resolution and the wood density was determined by CRAD and CERDsoftware. The results showed tree-ring boundaries were distinctly delimited in trees growing in site with rainfall lower than 2510 mm/year. It was demonstrated that tree age, climatic conditions and management of plantation affects wood density and its variability. Thespecific effect of variables on wood density was quantified by for multiple regression method. It was determined that tree yearexplained 25.8% of the total variation of density and 19.9% were caused by climatic condition where the tree growing. Wood densitywas less affected by the intensity of forest management with 5.9% of total variation.

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

Science.gov (United States)

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

The prediction of intelligence in preschool children using alternative models to regression.

Science.gov (United States)

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Coalescent methods for estimating phylogenetic trees.

Science.gov (United States)

Liu, Liang; Yu, Lili; Kubatko, Laura; Pearl, Dennis K; Edwards, Scott V

2009-10-01

We review recent models to estimate phylogenetic trees under the multispecies coalescent. Although the distinction between gene trees and species trees has come to the fore of phylogenetics, only recently have methods been developed that explicitly estimate species trees. Of the several factors that can cause gene tree heterogeneity and discordance with the species tree, deep coalescence due to random genetic drift in branches of the species tree has been modeled most thoroughly. Bayesian approaches to estimating species trees utilizes two likelihood functions, one of which has been widely used in traditional phylogenetics and involves the model of nucleotide substitution, and the second of which is less familiar to phylogeneticists and involves the probability distribution of gene trees given a species tree. Other recent parametric and nonparametric methods for estimating species trees involve parsimony criteria, summary statistics, supertree and consensus methods. Species tree approaches are an appropriate goal for systematics, appear to work well in some cases where concatenation can be misleading, and suggest that sampling many independent loci will be paramount. Such methods can also be challenging to implement because of the complexity of the models and computational time. In addition, further elaboration of the simplest of coalescent models will be required to incorporate commonly known issues such as deviation from the molecular clock, gene flow and other genetic forces.

Distribution of cavity trees in midwestern old-growth and second-growth forests

Science.gov (United States)

Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R. Thompson; David R. Larsen

2003-01-01

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

DEFF Research Database (Denmark)

Schlechtingen, Meik; Santos, Ilmar

2011-01-01

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

More Trees, More Poverty? The Socioeconomic Effects of Tree Plantations in Chile, 2001-2011

Science.gov (United States)

Andersson, Krister; Lawrence, Duncan; Zavaleta, Jennifer; Guariguata, Manuel R.

2016-01-01

Tree plantations play a controversial role in many nations' efforts to balance goals for economic development, ecological conservation, and social justice. This paper seeks to contribute to this debate by analyzing the socioeconomic impact of such plantations. We focus our study on Chile, a country that has experienced extraordinary growth of industrial tree plantations. Our analysis draws on a unique dataset with longitudinal observations collected in 180 municipal territories during 2001-2011. Employing panel data regression techniques, we find that growth in plantation area is associated with higher than average rates of poverty during this period.

Comparison of beta-binomial regression model approaches to analyze health-related quality of life data.

Science.gov (United States)

Najera-Zuloaga, Josu; Lee, Dae-Jin; Arostegui, Inmaculada

2017-01-01

Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.

Developing Models to Forcast Sales of Natural Christmas Trees

Science.gov (United States)

Lawrence D. Garrett; Thomas H. Pendleton

1977-01-01

A study of practices for marketing Christmas trees in Winston-Salem, North Carolina, and Denver, Colorado, revealed that such factors as retail lot competition, tree price, consumer traffic, and consumer income were very important in determining a particular retailer's sales. Analyses of 4 years of market data were used in developing regression models for...

Breaking the fault tree circular logic

International Nuclear Information System (INIS)

Lankin, M.

2000-01-01

Event tree - fault tree approach to model failures of nuclear plants as well as of other complex facilities is noticeably dominant now. This approach implies modeling an object in form of unidirectional logical graph - tree, i.e. graph without circular logic. However, genuine nuclear plants intrinsically demonstrate quite a few logical loops (circular logic), especially where electrical systems are involved. This paper shows the incorrectness of existing practice of circular logic breaking by elimination of part of logical dependencies and puts forward a formal algorithm, which enables the analyst to correctly model the failure of complex object, which involves logical dependencies between system and components, in form of fault tree. (author)

Detection of Citrus Trees from Uav Dsms

Science.gov (United States)

Ok, A. O.; Ozdarici-Ok, A.

2017-05-01

This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the state-of-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.

Long-term tree growth rate, water use efficiency, and tree ring nitrogen isotope composition of Pinus massoniana L. in response to global climate change and local nitrogen deposition in Southern China

Energy Technology Data Exchange (ETDEWEB)

Sun, Fangfang [Chinese Academy of Sciences, Guangzhou (China). South China Botanical Garden; Graduate Univ. of Chinese Academy of Sciences, Beijing (China); Griffith Univ., Nathan, QLD (Australia). Environmental Future Centre; Kuang, Yuanwen; Wen, Dazhi [Chinese Academy of Sciences, Guangzhou (China). South China Botanical Garden; Chinese Academy of Sciences, Guangzhou (China). Pearl River Delta Research Centre of Environmental Pollution and Control; Xu, Zhihong [Griffith Univ., Nathan, QLD (Australia). Environmental Future Centre; Li, Jianli; Zuo, Weidong [Agriculture and Forestry Technology Extension Centre, Nanhai District, Guangdong (China); Hou, Enqing [Chinese Academy of Sciences, Guangzhou (China). South China Botanical Garden; Graduate Univ. of Chinese Academy of Sciences, Beijing (China)

2010-12-15

We aimed to investigate long-term tree growth rates, water use efficiencies (WUE), and tree ring nitrogen (N) isotope compositions ({delta}{sup 15}N) of Masson pine (Pinus massoniana L.) in response to global climate change and local N deposition in Southern China. Tree annual growth rings of Masson pine were collected from four forest sites, viz. South China Botanical Garden (SBG), Xi Qiao Shan (XQS) Forest Park, Ding Hu Shan (DHS) Natural Reserve, and Nan Kun Shan (NKS) Natural Reserve in Southern China. The mean annual basal area increment (BAI), WUE, and {delta}{sup 15}N at every 5-year intervals of Masson pine during the last 50 years were determined. Regression analyses were used to quantify the relationships of BAI and WUE with atmospheric carbon dioxide concentration ([CO{sub 2}]), temperature, rainfall, and tree ring elemental concentrations at the four study sites. Tree BAI showed a quadratic relationship with rising [CO{sub 2}]. The tipping points of [CO{sub 2}] for BAI, the peaks of BAI when the critical [CO{sub 2}] was reached, occurred earlier at the sites of SBG, XQS, and DHS which were exposed to higher temperature, N deposition, and lower mineral nutrient availability, as compared with the tipping points of [CO{sub 2}] for BAI at the site of NKS which had higher rainfall, lower temperature, and better nutritional status. The average tipping point of [CO{sub 2}] at the four sites for the BAI response curves was 356 ppm, after which, the BAI would be expected to decrease quadratically with rising [CO{sub 2}]. The multiple regressions of BAI confirmed the relationships of long-term tree growth rate with rainfall, tree WUE, and nutrients and {delta}{sup 15}N in tree rings. Nonlinear relationships between BAI and tree ring {delta}{sup 15}N at DHS and negatively linear one at NKS reflected the fertilization effect of N deposition on tree growth rate initially, but this effect peaked or became negative once the forest approached or passed the N saturation

Can tree species diversity be assessed with Landsat data in a temperate forest?

Science.gov (United States)

Arekhi, Maliheh; Yılmaz, Osman Yalçın; Yılmaz, Hatice; Akyüz, Yaşar Feyza

2017-10-28

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

Refining discordant gene trees.

Science.gov (United States)

Górecki, Pawel; Eulenstein, Oliver

2014-01-01

Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism. We propose a natural extension of the well-established costs for comparing unrooted and non-binary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the Robinson-Foulds costs. Our binary refinement of Robinson-Foulds, gene duplication, and deep coalescence costs for unrooted and non-binary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.

Transferability of decision trees for land cover classification in a ...

African Journals Online (AJOL)

This paper attempts to derive classification rules from training data of four Landsat-8 scenes by using the classification and regression tree (CART) implementation of the decision tree algorithm. The transferability of the ruleset was evaluated by classifying two adjacent scenes. The classification of the four mosaicked scenes ...

Visual exploration of parameter influence on phylogenetic trees.

Science.gov (United States)

Hess, Martin; Bremm, Sebastian; Weissgraeber, Stephanie; Hamacher, Kay; Goesele, Michael; Wiemeyer, Josef; von Landesberger, Tatiana

2014-01-01

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Regression Benchmarking: An Approach to Quality Assurance in Performance

OpenAIRE

Bulej, Lubomír

2005-01-01

The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...

Penalized regression techniques for prediction: a case study for predicting tree mortality using remotely sensed vegetation indices

NARCIS (Netherlands)

Lazaridis, D.C.; Verbesselt, J.; Robinson, A.P.

2011-01-01

Constructing models can be complicated when the available fitting data are highly correlated and of high dimension. However, the complications depend on whether the goal is prediction instead of estimation. We focus on predicting tree mortality (measured as the number of dead trees) from change

Current and potential tree locations in tree line ecotone of Changbai Mountains, Northeast China: the controlling effects of topography.

Science.gov (United States)

Zong, Shengwei; Wu, Zhengfang; Xu, Jiawei; Li, Ming; Gao, Xiaofeng; He, Hongshi; Du, Haibo; Wang, Lei

2014-01-01

Tree line ecotone in the Changbai Mountains has undergone large changes in the past decades. Tree locations show variations on the four sides of the mountains, especially on the northern and western sides, which has not been fully explained. Previous studies attributed such variations to the variations in temperature. However, in this study, we hypothesized that topographic controls were responsible for causing the variations in the tree locations in tree line ecotone of the Changbai Mountains. To test the hypothesis, we used IKONOS images and WorldView-1 image to identify the tree locations and developed a logistic regression model using topographical variables to identify the dominant controls of the tree locations. The results showed that aspect, wetness, and slope were dominant controls for tree locations on western side of the mountains, whereas altitude, SPI, and aspect were the dominant factors on northern side. The upmost altitude a tree can currently reach was 2140 m asl on the northern side and 2060 m asl on western side. The model predicted results showed that habitats above the current tree line on the both sides were available for trees. Tree recruitments under the current tree line may take advantage of the available habitats at higher elevations based on the current tree location. Our research confirmed the controlling effects of topography on the tree locations in the tree line ecotone of Changbai Mountains and suggested that it was essential to assess the tree response to topography in the research of tree line ecotone.

Modelling the return distribution of salmon farming companies : a quantile regression approach

OpenAIRE

Jacobsen, Fredrik

2017-01-01

The salmon farming industry has gained increased attention from investors, portfolio managers, financial analysts and other stakeholders the recent years. Despite this development, very little is known about the risk and return of salmon farming company stocks, and especially how the relationship between risk and return varies under different market conditions, given the volatile nature of the salmon farming industry. We approach this problem by using quantile regression to examine the relati...

Quantifying ozone uptake at the canopy level of spruce, pine and larch trees at the alpine timberline: an approach based on sap flow measurement

International Nuclear Information System (INIS)

Wieser, G.; Matyssek, R.; Koestner, B.; Oberhuber, W.

2003-01-01

Sap-flow based measurements can be used to estimate ozone uptake at whole-tree and stand levels. - Micro-climatic and ambient ozone data were combined with measurements of sap flow through tree trunks in order to estimate whole-tree ozone uptake of adult Norway spruce (Picea abies), cembran pine (Pinus cembra), and European larch (Larix decidua) trees. Sap flow was monitored by means of the heat balance approach in two trees of each species during the growing season of 1998. In trees making up the stand canopy, the ozone uptake by evergreen foliages was significantly higher than by deciduous ones, when scaled to the ground area. However, if expressed per unit of whole-tree foliage area, ozone flux through the stomata into the needle mesophyll was 1.09, 1.18 and 1.40 nmol m -2 s -1 in Picea abies, Pinus cembra and Larix decidua, respectively. These fluxes are consistent with findings from measurements of needle gas exchange, published from the same species at the study site. It is concluded that the sap flow-based approach offers an inexpensive, spatially and temporally integrating way for estimating ozone uptake at the whole-tree and stand level, intrinsically covering the effect of boundary layers on ozone flux

An automated approach to the design of decision tree classifiers

Science.gov (United States)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Constructing phylogenetic trees using interacting pathways.

Science.gov (United States)

Wan, Peng; Che, Dongsheng

2013-01-01

Phylogenetic trees are used to represent evolutionary relationships among biological species or organisms. The construction of phylogenetic trees is based on the similarities or differences of their physical or genetic features. Traditional approaches of constructing phylogenetic trees mainly focus on physical features. The recent advancement of high-throughput technologies has led to accumulation of huge amounts of biological data, which in turn changed the way of biological studies in various aspects. In this paper, we report our approach of building phylogenetic trees using the information of interacting pathways. We have applied hierarchical clustering on two domains of organisms-eukaryotes and prokaryotes. Our preliminary results have shown the effectiveness of using the interacting pathways in revealing evolutionary relationships.

Modeling time-to-event (survival) data using classification tree analysis.

Science.gov (United States)

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Estimating phylogenetic trees from genome-scale data.

Science.gov (United States)

Liu, Liang; Xi, Zhenxiang; Wu, Shaoyuan; Davis, Charles C; Edwards, Scott V

2015-12-01

The heterogeneity of signals in the genomes of diverse organisms poses challenges for traditional phylogenetic analysis. Phylogenetic methods known as "species tree" methods have been proposed to directly address one important source of gene tree heterogeneity, namely the incomplete lineage sorting that occurs when evolving lineages radiate rapidly, resulting in a diversity of gene trees from a single underlying species tree. Here we review theory and empirical examples that help clarify conflicts between species tree and concatenation methods, and misconceptions in the literature about the performance of species tree methods. Considering concatenation as a special case of the multispecies coalescent model helps explain differences in the behavior of the two methods on phylogenomic data sets. Recent work suggests that species tree methods are more robust than concatenation approaches to some of the classic challenges of phylogenetic analysis, including rapidly evolving sites in DNA sequences and long-branch attraction. We show that approaches, such as binning, designed to augment the signal in species tree analyses can distort the distribution of gene trees and are inconsistent. Computationally efficient species tree methods incorporating biological realism are a key to phylogenetic analysis of whole-genome data. © 2015 New York Academy of Sciences.

Fuzzy tree automata and syntactic pattern recognition.

Science.gov (United States)

Lee, E T

1982-04-01

An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

A Weibull-based compositional approach for hierarchical dynamic fault trees

International Nuclear Information System (INIS)

Chiacchio, F.; Cacioppo, M.; D'Urso, D.; Manno, G.; Trapani, N.; Compagno, L.

2013-01-01

The solution of a dynamic fault tree (DFT) for the reliability assessment can be achieved using a wide variety of techniques. These techniques have a strong theoretical foundation as both the analytical and the simulation methods have been extensively developed. Nevertheless, they all present the same limits that appear with the increasing of the size of the fault trees (i.e., state space explosion, time-consuming simulations), compromising the resolution. We have tested the feasibility of a composition algorithm based on a Weibull distribution, addressed to the resolution of a general class of dynamic fault trees characterized by non-repairable basic events and generally distributed failure times. The proposed composition algorithm is used to generalize the traditional hierarchical technique that, as previous literature have extensively confirmed, is able to reduce the computational effort of a large DFT through the modularization of independent parts of the tree. The results of this study are achieved both through simulation and analytical techniques, thus confirming the capability to solve a quite general class of dynamic fault trees and overcome the limits of traditional techniques.

ESTIMATION OF HEIGHT OF EUCALYPTUS TREES WITH NEUROEVOLUTION OF AUGMENTING TOPOLOGIES (NEAT

Directory of Open Access Journals (Sweden)

Daniel Henrique Breda Binoti

2018-02-01

Full Text Available ABSTRACT The aim of this study was to evaluate the method of neuroevolution of augmenting topologies (NEAT to adjust the weights and the topology of artificial neural networks (ANNs in the estimation of tree height in a clonal population of eucalyptus, and compare with estimates obtained by a hypsometric regression model. To estimate the total tree height (Ht, the RNAs and the regression model, we used as variables a diameter of 1.3 m height (dbh and the dominant height (Hd. The RNAs were adjusted and applied to the computer system NeuroForest, varying the size of the initial population (the genetic algorithm parameter and the density of initial connections. Estimates of the total height of the trees obtained with the use of RNA and the regression model were evaluated based on the correlation coefficient, the percentage of errors scatter plot, the percentage frequency histogram of percentage errors, and the root mean square error (root mean square error - RMSE. Various settings which resulted in superior statistics to the hypsometric regression model were found. Connections had the highest correlation and the lowest RMSE% with a population size value of 300 and an initial density of 0.1 RNA. The NEAT methodology proved effective in estimating the height of trees in clonal population of eucalyptus.

SLE as a Mating of Trees in Euclidean Geometry

Science.gov (United States)

Holden, Nina; Sun, Xin

2018-05-01

The mating of trees approach to Schramm-Loewner evolution (SLE) in the random geometry of Liouville quantum gravity (LQG) has been recently developed by Duplantier et al. (Liouville quantum gravity as a mating of trees, 2014. arXiv:1409.7055). In this paper we consider the mating of trees approach to SLE in Euclidean geometry. Let {η} be a whole-plane space-filling SLE with parameter {κ > 4} , parameterized by Lebesgue measure. The main observable in the mating of trees approach is the contour function, a two-dimensional continuous process describing the evolution of the Minkowski content of the left and right frontier of {η} . We prove regularity properties of the contour function and show that (as in the LQG case) it encodes all the information about the curve {η} . We also prove that the uniform spanning tree on {Z^2} converges to SLE8 in the natural topology associated with the mating of trees approach.

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

"Trees and Things That Live in Trees": Three Children with Special Needs Experience the Project Approach

Science.gov (United States)

Griebling, Susan; Elgas, Peg; Konerman, Rachel

2015-01-01

The authors report on research conducted during a project investigation undertaken with preschool children, ages 3-5. The report focuses on three children with special needs and the positive outcomes for each child as they engaged in the project Trees and Things That Live in Trees. Two of the children were diagnosed with developmental delays, and…

Mechanisms of neuroblastoma regression

Science.gov (United States)

Brodeur, Garrett M.; Bagatell, Rochelle

2014-01-01

Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

Risk assessment for enterprise resource planning (ERP) system implementations: a fault tree analysis approach

Science.gov (United States)

Zeng, Yajun; Skibniewski, Miroslaw J.

2013-08-01

Enterprise resource planning (ERP) system implementations are often characterised with large capital outlay, long implementation duration, and high risk of failure. In order to avoid ERP implementation failure and realise the benefits of the system, sound risk management is the key. This paper proposes a probabilistic risk assessment approach for ERP system implementation projects based on fault tree analysis, which models the relationship between ERP system components and specific risk factors. Unlike traditional risk management approaches that have been mostly focused on meeting project budget and schedule objectives, the proposed approach intends to address the risks that may cause ERP system usage failure. The approach can be used to identify the root causes of ERP system implementation usage failure and quantify the impact of critical component failures or critical risk events in the implementation process.

Application of Goal Tree-Success Tree model as the knowledge-base of operator advisory systems

International Nuclear Information System (INIS)

Kim, I.S.; Modarres, M.

1987-01-01

The most important portion of an expert system development is the articulation of knowledge by the expert and its satisfactory formulation in a suitable knowledge representation scheme for mechanization by a computer. A 'deep knowledge' approach called Goal Tree-Success Tree model is devised to represent complex dynamic domain knowledge. This approach can hierarchically model the underlying principles of a given process domain (for example nuclear power plant operations domain). The Goal Tree-Success Tree can then be used to represent the knowledge-base and provide means of selecting an efficient search routine in the inference engine of an expert system. A prototype expert system has been developed to demonstrate the method. This expert system models the operation of a typical system used in the pressurized water reactors. The expert system is modeled for real-time operations if an interface between plant parameters and the expert system is established. The real-time operation provides an ability to quickly remedy minor disturbances that can quickly lead to a system malfunction or trip. A description of both the Goal Tree-Success Tree model and the prototype expert system is presented. (orig.)

Modeling stem increment in individual Pinus occidentalis Sw. trees in La Sierra, Dominican Republic

Energy Technology Data Exchange (ETDEWEB)

Bueno, S.; Bevilacqua, E.

2010-07-01

One of the most common and important tree characteristics used in forest management decision-making is tree diameter-at-breast height (DBH). This paper presents results on an evaluation of two growth functions developed to model stem diameter increases in individual Pinus occidentalis Sw. trees in La Sierra, Dominican Republic. The first model was developed in order to predict future DBH (FDM) at different intervals of time and the other for predicting growth, that is, periodic annual diameter increment (PADIM). Each model employed two statistical techniques for fitting model parameters: stepwise ordinary least squares (OLS) regression, and mixed models. The two statistical approaches varied in how they accounted for the repeated measurements on individual trees over time, affecting standard error estimates and statistical inference of model parameters. Each approach was evaluated based on six goodness of- fit statistics, using both calibration and validation data sets. The objectives were 1) to determine the best model for predicting future tree DBH; 2) to determine the best model for predicting periodic annual diameter increment, both models using tree size, age, site index and different indices of competitive status; and 3) compare which of these two modeling approaches predicts better the future DBH. OLS provided a better fit for both of the growth functions, especially in regards to bias. Both models showed advantages and disadvantages when they were used to predict growth and future diameter. For the prediction of future diameter with FDM, accuracy of predictions were within one centimeter for a five-year projection interval. The PADIM presented negligible bias in estimating future diameter, although there was a small increase in bias as time of prediction increased. As expected, each model was the best in estimating the response variable it was developed for.. However, a closer examination of the distribution of errors showed a slight advantage of the FDM

Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Science.gov (United States)

Heddam, Salim; Kisi, Ozgur

2018-04-01

In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three models. The water quality data selected consisted of daily measured of water temperature (TE, °C), pH (std. unit), specific conductance (SC, μS/cm) and discharge (DI cfs), are used as inputs to the LSSVM, MARS and M5T models. The three models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three models and (ii) the best model among all others differs from one station to another.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Science.gov (United States)

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Linking definitions, mechanisms, and modeling of drought-induced tree death.

Science.gov (United States)

Anderegg, William R L; Berry, Joseph A; Field, Christopher B

2012-12-01

Tree death from drought and heat stress is a critical and uncertain component in forest ecosystem responses to a changing climate. Recent research has illuminated how tree mortality is a complex cascade of changes involving interconnected plant systems over multiple timescales. Explicit consideration of the definitions, dynamics, and temporal and biological scales of tree mortality research can guide experimental and modeling approaches. In this review, we draw on the medical literature concerning human death to propose a water resource-based approach to tree mortality that considers the tree as a complex organism with a distinct growth strategy. This approach provides insight into mortality mechanisms at the tree and landscape scales and presents promising avenues into modeling tree death from drought and temperature stress. Copyright © 2012 Elsevier Ltd. All rights reserved.

Drawing Contour Trees in the Plane.

Science.gov (United States)

Heine, C; Schneider, D; Carr, Hamish; Scheuermann, G

2011-11-01

The contour tree compactly describes scalar field topology. From the viewpoint of graph drawing, it is a tree with attributes at vertices and optionally on edges. Standard tree drawing algorithms emphasize structural properties of the tree and neglect the attributes. Applying known techniques to convey this information proves hard and sometimes even impossible. We present several adaptions of popular graph drawing approaches to the problem of contour tree drawing and evaluate them. We identify five esthetic criteria for drawing contour trees and present a novel algorithm for drawing contour trees in the plane that satisfies four of these criteria. Our implementation is fast and effective for contour tree sizes usually used in interactive systems (around 100 branches) and also produces readable pictures for larger trees, as is shown for an 800 branch example.

Analysis of Logic Programs Using Regular Tree Languages

DEFF Research Database (Denmark)

Gallagher, John Patrick

2012-01-01

The eld of nite tree automata provides fundamental notations and tools for reasoning about set of terms called regular or recognizable tree languages. We consider two kinds of analysis using regular tree languages, applied to logic programs. The rst approach is to try to discover automatically...... a tree automaton from a logic program, approximating its minimal Herbrand model. In this case the input for the analysis is a program, and the output is a tree automaton. The second approach is to expose or check properties of the program that can be expressed by a given tree automaton. The input...... to the analysis is a program and a tree automaton, and the output is an abstract model of the program. These two contrasting abstract interpretations can be used in a wide range of analysis and verication problems....

Monthly paleostreamflow reconstruction from annual tree-ring chronologies

Science.gov (United States)

Stagge, J. H.; Rosenberg, D. E.; DeRose, R. J.; Rittenour, T. M.

2018-02-01

Paleoclimate reconstructions are increasingly used to characterize annual climate variability prior to the instrumental record, to improve estimates of climate extremes, and to provide a baseline for climate-change projections. To date, paleoclimate records have seen limited engineering use to estimate hydrologic risks because water systems models and managers usually require streamflow input at the monthly scale. This study explores the hypothesis that monthly streamflows can be adequately modeled by statistically decomposing annual flow reconstructions. To test this hypothesis, a multiple linear regression model for monthly streamflow reconstruction is presented that expands the set of predictors to include annual streamflow reconstructions, reconstructions of global circulation, and potential differences among regional tree-ring chronologies related to tree species and geographic location. This approach is used to reconstruct 600 years of monthly streamflows at two sites on the Bear and Logan rivers in northern Utah. Nash-Sutcliffe Efficiencies remain above zero (0.26-0.60) for all months except April and Pearson's correlation coefficients (R) are 0.94 and 0.88 for the Bear and Logan rivers, respectively, confirming that the model can adequately reproduce monthly flows during the reference period (10/1942 to 9/2015). Incorporating a flexible transition between the previous and concurrent annual reconstructed flows was the most important factor for model skill. Expanding the model to include global climate indices and regional tree-ring chronologies produced smaller, but still significant improvements in model fit. The model presented here is the only approach currently available to reconstruct monthly streamflows directly from tree-ring chronologies and climate reconstructions, rather than using resampling of the observed record. With reasonable estimates of monthly flow that extend back in time many centuries, water managers can challenge systems models with a

A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

International Nuclear Information System (INIS)

Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.

2013-01-01

Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability

Multi-pruning of decision trees for knowledge representation and classification

KAUST Repository

Azad, Mohammad

2016-06-09

We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

Multi-pruning of decision trees for knowledge representation and classification

KAUST Repository

Azad, Mohammad; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2016-01-01

We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

Energy Technology Data Exchange (ETDEWEB)

Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

2012-02-01

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

Integrating cyber attacks within fault trees

International Nuclear Information System (INIS)

Nai Fovino, Igor; Masera, Marcelo; De Cian, Alessio

2009-01-01

In this paper, a new method for quantitative security risk assessment of complex systems is presented, combining fault-tree analysis, traditionally used in reliability analysis, with the recently introduced Attack-tree analysis, proposed for the study of malicious attack patterns. The combined use of fault trees and attack trees helps the analyst to effectively face the security challenges posed by the introduction of modern ICT technologies in the control systems of critical infrastructures. The proposed approach allows considering the interaction of malicious deliberate acts with random failures. Formal definitions of fault tree and attack tree are provided and a mathematical model for the calculation of system fault probabilities is presented.

Integrating cyber attacks within fault trees

Energy Technology Data Exchange (ETDEWEB)

Nai Fovino, Igor [Joint Research Centre - EC, Institute for the Protection and Security of the Citizen, Ispra, VA (Italy)], E-mail: igor.nai@jrc.it; Masera, Marcelo [Joint Research Centre - EC, Institute for the Protection and Security of the Citizen, Ispra, VA (Italy); De Cian, Alessio [Department of Electrical Engineering, University di Genova, Genoa (Italy)

2009-09-15

In this paper, a new method for quantitative security risk assessment of complex systems is presented, combining fault-tree analysis, traditionally used in reliability analysis, with the recently introduced Attack-tree analysis, proposed for the study of malicious attack patterns. The combined use of fault trees and attack trees helps the analyst to effectively face the security challenges posed by the introduction of modern ICT technologies in the control systems of critical infrastructures. The proposed approach allows considering the interaction of malicious deliberate acts with random failures. Formal definitions of fault tree and attack tree are provided and a mathematical model for the calculation of system fault probabilities is presented.

Optimization and analysis of decision trees and rules: Dynamic programming approach

KAUST Repository

Alkhalid, Abdulaziz

2013-08-01

This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

Optimization and analysis of decision trees and rules: Dynamic programming approach

KAUST Repository

Alkhalid, Abdulaziz; Amin, Talha M.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

International Nuclear Information System (INIS)

Baser, Furkan; Demirhan, Haydar

2017-01-01

Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.

Mathematical analysis and modeling of epidemics of rubber tree root diseases: Probability of infection of an individual tree

Energy Technology Data Exchange (ETDEWEB)

Chadoeuf, J.; Joannes, H.; Nandris, D.; Pierrat, J.C.

1988-12-01

The spread of root diseases in rubber tree (Hevea brasiliensis) due to Rigidoporus lignosus and Phellinus noxius was investigated epidemiologically using data collected every 6 month during a 6-year survey in a plantation. The aim of the present study is to see what factors could predict whether a given tree would be infested at the following inspection. Using a qualitative regression method we expressed the probability of pathogenic attack on a tree in terms of three factors: the state of health of the surrounding trees, the method used to clear the forest prior to planting, and evolution with time. The effects of each factor were ranked, and the roles of the various classes of neighbors were established and quantified. Variability between successive inspections was small, and the method of forest clearing was important only while primary inocula in the soil were still infectious. The state of health of the immediate neighbors was most significant; more distant neighbors in the same row had some effect; interrow spread was extremely rare. This investigation dealt only with trees as individuals, and further study of the interrelationships of groups of trees is needed.

Can dendrochronology procedures estimate historical Tree Water Footprint?

Science.gov (United States)

Fernandes, Tarcísio J. G.; Del Campo, Antonio D.; Molina, Antonio J.

2013-04-01

Whole estimates of tree water use are becoming increasingly important in forest science and forest scientists have long sought to develop reliable techniques to estimate tree water use. In this sense accurately determining or estimate the quantity of water transpired by trees and forests is important and can be used to determine "green" water footprint. The use of dendrochronology is relative common in the study of effects and interactions between growth and climatic variables, but few studies deal with the relationship with water footprint. The main objective of this study is determining the historical tree water-use in a planted stand by dendrochronological approaches. This study was performed in South-eastern Spain, in an area covered by 50-60 years old Pinus halepensis Mil. plantations with high tree density (ca.1288/ha) due to low forest management. The experimental set-up consisted of two plots (30x30m), one corresponding to a thinning treatment performed in 2008 (t10) and the other thinned in 1998 (t1) to assess the mid-term effects of thinning. After one year of thinning four representative trees were select in each plot to measure transpiration by heat pulse sensor (sapflow velocity, vs). The accumulated daily values of transpiration (L day-1) were estimated multiplying the values of vs by sapwood area of each selected tree. After transpiration measurements two cores per tree were taken for establishing the tree-rings chronologies. The cores were prepared, their ring-width were measured and standardised in basal area increment index (BAI-i) following usual dendrochronological methods. The dendrochronology analyses showed a general variability in ring width during the initial growth (15 years), while in the following years the width rings were very small, conditioned by climate. The year after thinning (1999 or 2009) all trees in the treatments showed significant increases in ring width. The average vs for t1 and t10 were 3.59 cm h-1 and 1.95 cm h-1, and

Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

Science.gov (United States)

Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-09-01

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Regression Tree-Based Methodology for Customizing Building Energy Benchmarks to Individual Commercial Buildings

Science.gov (United States)

Kaskhedikar, Apoorva Prakash

According to the U.S. Energy Information Administration, commercial buildings represent about 40% of the United State's energy consumption of which office buildings consume a major portion. Gauging the extent to which an individual building consumes energy in excess of its peers is the first step in initiating energy efficiency improvement. Energy Benchmarking offers initial building energy performance assessment without rigorous evaluation. Energy benchmarking tools based on the Commercial Buildings Energy Consumption Survey (CBECS) database are investigated in this thesis. This study proposes a new benchmarking methodology based on decision trees, where a relationship between the energy use intensities (EUI) and building parameters (continuous and categorical) is developed for different building types. This methodology was applied to medium office and school building types contained in the CBECS database. The Random Forest technique was used to find the most influential parameters that impact building energy use intensities. Subsequently, correlations which were significant were identified between EUIs and CBECS variables. Other than floor area, some of the important variables were number of workers, location, number of PCs and main cooling equipment. The coefficient of variation was used to evaluate the effectiveness of the new model. The customization technique proposed in this thesis was compared with another benchmarking model that is widely used by building owners and designers namely, the ENERGY STAR's Portfolio Manager. This tool relies on the standard Linear Regression methods which is only able to handle continuous variables. The model proposed uses data mining technique and was found to perform slightly better than the Portfolio Manager. The broader impacts of the new benchmarking methodology proposed is that it allows for identifying important categorical variables, and then incorporating them in a local, as against a global, model framework for EUI

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients.

Science.gov (United States)

Aguiar, Fabio S; Almeida, Luciana L; Ruffino-Netto, Antonio; Kritski, Afranio Lineu; Mello, Fernanda Cq; Werneck, Guilherme L

2012-08-07

Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in

Classification and regression tree (CART model to predict pulmonary tuberculosis in hospitalized patients

Directory of Open Access Journals (Sweden)

Aguiar Fabio S

2012-08-01

Full Text Available Abstract Background Tuberculosis (TB remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART model was generated and validated. The area under the ROC curve (AUC, sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with

Schistosoma mansoni reinfection: Analysis of risk factors by classification and regression tree (CART modeling.

Directory of Open Access Journals (Sweden)

Andréa Gazzinelli

Full Text Available Praziquantel (PZQ is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination. However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection. The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. mansoni one year after successful treatment with PZQ in school-aged children in Northeastern Minas Gerais state Brazil. Parasitological, socioeconomic, demographic, and water contact information were surveyed in 506 S. mansoni-infected individuals, aged 6 to 15 years, resident in these endemic areas. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. mansoni eggs in the feces on two consecutive days of Kato-Katz fecal thick smear. These individuals were surveyed again 12 months from the date of successful treatment with PZQ. A classification and regression tree modeling (CART was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. mansoni reinfection was their "heavy" infection at baseline. Additional analyses, excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. mansoni reinfection. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection. We suggest that control measures that target

A different approach to estimate nonlinear regression model using numerical methods

Science.gov (United States)

Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

2017-11-01

This research paper concerns with the computational methods namely the Gauss-Newton method, Gradient algorithm methods (Newton-Raphson method, Steepest Descent or Steepest Ascent algorithm method, the Method of Scoring, the Method of Quadratic Hill-Climbing) based on numerical analysis to estimate parameters of nonlinear regression model in a very different way. Principles of matrix calculus have been used to discuss the Gradient-Algorithm methods. Yonathan Bard [1] discussed a comparison of gradient methods for the solution of nonlinear parameter estimation problems. However this article discusses an analytical approach to the gradient algorithm methods in a different way. This paper describes a new iterative technique namely Gauss-Newton method which differs from the iterative technique proposed by Gorden K. Smyth [2]. Hans Georg Bock et.al [10] proposed numerical methods for parameter estimation in DAE’s (Differential algebraic equation). Isabel Reis Dos Santos et al [11], Introduced weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel. For large-scale non smooth convex minimization the Hager and Zhang (HZ) conjugate gradient Method and the modified HZ (MHZ) method were presented by Gonglin Yuan et al [12].

[Quantitative models between canopy hyperspectrum and its component features at apple tree prosperous fruit stage].

Science.gov (United States)

Wang, Ling; Zhao, Geng-xing; Zhu, Xi-cun; Lei, Tong; Dong, Fang

2010-10-01

Hyperspectral technique has become the basis of quantitative remote sensing. Hyperspectrum of apple tree canopy at prosperous fruit stage consists of the complex information of fruits, leaves, stocks, soil and reflecting films, which was mostly affected by component features of canopy at this stage. First, the hyperspectrum of 18 sample apple trees with reflecting films was compared with that of 44 trees without reflecting films. It could be seen that the impact of reflecting films on reflectance was obvious, so the sample trees with ground reflecting films should be separated to analyze from those without ground films. Secondly, nine indexes of canopy components were built based on classified digital photos of 44 apple trees without ground films. Thirdly, the correlation between the nine indexes and canopy reflectance including some kinds of conversion data was analyzed. The results showed that the correlation between reflectance and the ratio of fruit to leaf was the best, among which the max coefficient reached 0.815, and the correlation between reflectance and the ratio of leaf was a little better than that between reflectance and the density of fruit. Then models of correlation analysis, linear regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the hyperspectral reflectance and the ratio of fruit to leaf with the softwares of DPS and LIBSVM. It was feasible that all of the four models in 611-680 nm characteristic band are feasible to be used to predict, while the model accuracy of BP neural network and support vector regression was better than one-variable linear regression and multi-variable regression, and the accuracy of support vector regression model was the best. This study will be served as a reliable theoretical reference for the yield estimation of apples based on remote sensing data.

A Metric on Phylogenetic Tree Shapes.

Science.gov (United States)

Colijn, C; Plazzotta, G

2018-01-01

The shapes of evolutionary trees are influenced by the nature of the evolutionary process but comparisons of trees from different processes are hindered by the challenge of completely describing tree shape. We present a full characterization of the shapes of rooted branching trees in a form that lends itself to natural tree comparisons. We use this characterization to define a metric, in the sense of a true distance function, on tree shapes. The metric distinguishes trees from random models known to produce different tree shapes. It separates trees derived from tropical versus USA influenza A sequences, which reflect the differing epidemiology of tropical and seasonal flu. We describe several metrics based on the same core characterization, and illustrate how to extend the metric to incorporate trees' branch lengths or other features such as overall imbalance. Our approach allows us to construct addition and multiplication on trees, and to create a convex metric on tree shapes which formally allows computation of average tree shapes. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Cafts: computer aided fault tree analysis

International Nuclear Information System (INIS)

Poucet, A.

1985-01-01

The fault tree technique has become a standard tool for the analysis of safety and reliability of complex system. In spite of the costs, which may be high for a complete and detailed analysis of a complex plant, the fault tree technique is popular and its benefits are fully recognized. Due to this applications of these codes have mostly been restricted to simple academic examples and rarely concern complex, real world systems. In this paper an interactive approach to fault tree construction is presented. The aim is not to replace the analyst, but to offer him an intelligent tool which can assist him in modeling complex systems. Using the CAFTS-method, the analyst interactively constructs a fault tree in two phases: (1) In a first phase he generates an overall failure logic structure of the system; the macrofault tree. In this phase, CAFTS features an expert system approach to assist the analyst. It makes use of a knowledge base containing generic rules on the behavior of subsystems and components; (2) In a second phase the macrofault tree is further refined and transformed in a fully detailed and quantified fault tree. In this phase a library of plant-specific component failure models is used

Global variation in woodpecker species richness shaped by tree availability

DEFF Research Database (Denmark)

Ilsoe, Sigrid Kistrup; Kissling, W. Daniel; Fjeldsa, Jon

2017-01-01

. Location: Global. Methods: We used spatial and non-spatial regressions to test for relationships between broad-scale woodpecker species richness and predictor variables describing current and deep-time availability of trees, current climate, Quaternary climate change, human impact, topographical...... a negative indirect effect on woodpecker species richness. Main conclusions: Global species richness of woodpeckers is primarily shaped by current tree cover and precipitation, reflecting a strong biotic association between woodpeckers and trees. Human influence can have a negative effect on woodpecker....... As an example, woodpeckers (Picidae) are closely associated with trees and woody habitats because of multiple morphological and ecological specializations. In this study, we test whether this strong biotic association causes woodpecker diversity to be closely linked to tree availability at a global scale...

Long-term summer sunshine/moisture stress reconstruction from tree-ring widths from Bosnia and Herzegovina

Directory of Open Access Journals (Sweden)

S. Poljanšek

2013-01-01

Full Text Available We present the first summer sunshine reconstruction from tree-ring data for the western part of the Balkan Peninsula. Summer sunshine is tightly connected with moisture stress in trees, because the moisture stress and therefore the width of annual tree-rings is under the influence of the direct and interactive effects of sunshine duration (temperature, precipitation, cloud cover and evapotranspiration. The reconstruction is based on a calibrated z-scored mean chronology, calculated from tree-ring width measurements from 7 representative black pine (Pinus nigra Arnold sites in Bosnia and Herzegovina (BiH. A combined regression and scaling approach was used for the reconstruction of the summer sunshine. We found a significant negative correlation (r = −0.54, p < 0.0001 with mean June–July sunshine hours from Osijek meteorological station (Croatia. The developed model was used for reconstruction of summer sunshine for the time period 1660–2010. We identified extreme summer events and compared them to available documentary historical sources of drought, volcanic eruptions and other reconstructions from the broader region. All extreme summers with low sunshine hours (1712, 1810, 1815, 1843, 1899 and 1966 are connected with volcanic eruptions.

Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent.

Science.gov (United States)

Allman, Elizabeth S; Degnan, James H; Rhodes, John A

2011-06-01

Gene trees are evolutionary trees representing the ancestry of genes sampled from multiple populations. Species trees represent populations of individuals-each with many genes-splitting into new populations or species. The coalescent process, which models ancestry of gene copies within populations, is often used to model the probability distribution of gene trees given a fixed species tree. This multispecies coalescent model provides a framework for phylogeneticists to infer species trees from gene trees using maximum likelihood or Bayesian approaches. Because the coalescent models a branching process over time, all trees are typically assumed to be rooted in this setting. Often, however, gene trees inferred by traditional phylogenetic methods are unrooted. We investigate probabilities of unrooted gene trees under the multispecies coalescent model. We show that when there are four species with one gene sampled per species, the distribution of unrooted gene tree topologies identifies the unrooted species tree topology and some, but not all, information in the species tree edges (branch lengths). The location of the root on the species tree is not identifiable in this situation. However, for 5 or more species with one gene sampled per species, we show that the distribution of unrooted gene tree topologies identifies the rooted species tree topology and all its internal branch lengths. The length of any pendant branch leading to a leaf of the species tree is also identifiable for any species from which more than one gene is sampled.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

Science.gov (United States)

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT project.

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Manal Alghamdi

Full Text Available Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE. The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree and achieved high accuracy of prediction (AUC = 0.92. The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

An Assessment of the Effectiveness of Tree-Based Models for Multi-Variate Flood Damage Assessment in Australia

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Roozbeh Hasanzadeh Nafari

2016-07-01

Full Text Available Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

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Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Directory of Open Access Journals (Sweden)

Drzewiecki Wojciech

2016-12-01

Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.

Contaminant Gradients in Trees: Directional Tree Coring Reveals Boundaries of Soil and Soil-Gas Contamination with Potential Applications in Vapor Intrusion Assessment.

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Wilson, Jordan L; Samaranayake, V A; Limmer, Matthew A; Schumacher, John G; Burken, Joel G

2017-12-19

Contaminated sites pose ecological and human-health risks through exposure to contaminated soil and groundwater. Whereas we can readily locate, monitor, and track contaminants in groundwater, it is harder to perform these tasks in the vadose zone. In this study, tree-core samples were collected at a Superfund site to determine if the sample-collection location around a particular tree could reveal the subsurface location, or direction, of soil and soil-gas contaminant plumes. Contaminant-centroid vectors were calculated from tree-core data to reveal contaminant distributions in directional tree samples at a higher resolution, and vectors were correlated with soil-gas characterization collected using conventional methods. Results clearly demonstrated that directional tree coring around tree trunks can indicate gradients in soil and soil-gas contaminant plumes, and the strength of the correlations were directly proportionate to the magnitude of tree-core concentration gradients (spearman's coefficient of -0.61 and -0.55 in soil and tree-core gradients, respectively). Linear regression indicates agreement between the concentration-centroid vectors is significantly affected by in planta and soil concentration gradients and when concentration centroids in soil are closer to trees. Given the existing link between soil-gas and vapor intrusion, this study also indicates that directional tree coring might be applicable in vapor intrusion assessment.

Genomics-assisted breeding in fruit trees.

Science.gov (United States)

Iwata, Hiroyoshi; Minamikawa, Mai F; Kajiya-Kanegae, Hiromi; Ishimori, Motoyuki; Hayashi, Takeshi

2016-01-01

Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.

SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

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M. A. Dogon-Yaro

2016-09-01

Full Text Available Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Spatial variability of biotic and abiotic tree establishment constraints across a treeline ecotone in the Alaska range.

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Stueve, Kirk M; Isaacs, Rachel E; Tyrrell, Lucy E; Densmore, Roseann V

2011-02-01

Throughout interior Alaska (U.S.A.), a gradual warming trend in mean monthly temperatures occurred over the last few decades (approximatlely 2-4 degrees C). The accompanying increases in woody vegetation at many alpine treeline (hereafter treeline) locations provided an opportunity to examine how biotic and abiotic local site conditions interact to control tree establishment patterns during warming. We devised a landscape ecological approach to investigate these relationships at an undisturbed treeline in the Alaska Range. We identified treeline changes between 1953 (aerial photography) and 2005 (satellite imagery) in a geographic information system (GIS) and linked them with corresponding local site conditions derived from digital terrain data, ancillary climate data, and distance to 1953 trees. Logistic regressions enabled us to rank the importance of local site conditions in controlling tree establishment. We discovered a spatial transition in the importance of tree establishment controls. The biotic variable (proximity to 1953 trees) was the most important tree establishment predictor below the upper tree limit, providing evidence of response lags with the abiotic setting and suggesting that tree establishment is rarely in equilibrium with the physical environment or responding directly to warming. Elevation and winter sun exposure were important predictors of tree establishment at the upper tree limit, but proximity to trees persisted as an important tertiary predictor, indicating that tree establishment may achieve equilibrium with the physical environment. However, even here, influences from the biotic variable may obscure unequivocal correlations with the abiotic setting (including temperature). Future treeline expansion will likely be patchy and challenging to predict without considering the spatial variability of influences from biotic and abiotic local site conditions.

Nonbinary Tree-Based Phylogenetic Networks.

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Jetten, Laura; van Iersel, Leo

2018-01-01

Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.

Scenario tree airline fleet planning for demand uncertainty

NARCIS (Netherlands)

Repko, M.G.J.; Lopes dos Santos, Bruno F.

2017-01-01

This paper proposes an innovative multi-period modeling approach to solve the airline fleet planning problem under demand uncertainty. The problem is modeled using a scenario tree approach. The tree is composed of nodes, which represent points of decision in multiple time stages of the planning

Comparing Two Different Approaches to the Modeling of the Common Cause Failures in Fault Trees

International Nuclear Information System (INIS)

Vukovic, I.; Mikulicic, V.; Vrbanic, I.

2002-01-01

The potential for common cause failures in systems that perform critical functions has been recognized as very important contributor to risk associated with operation of nuclear power plants. Consequentially, modeling of common cause failures (CCF) in fault trees has become one among the essential elements in any probabilistic safety assessment (PSA). Detailed and realistic representation of CCF potential in fault tree structure is sometimes very challenging task. This is especially so in the cases where a common cause group involves more than two components. During the last ten years the difficulties associated with this kind of modeling have been overcome to some degree by development of integral PSA tools with high capabilities. Some of them allow for the definition of CCF groups and their automated expanding in the process of Boolean resolution and generation of minimal cutsets. On the other hand, in PSA models developed and run by more traditional tools, CCF-potential had to be modeled in the fault trees explicitly. With explicit CCF modeling, fault trees can grow very large, especially in the cases when they involve CCF groups with 3 or more members, which can become an issue for the management of fault trees and basic events with traditional non-integral PSA models. For these reasons various simplifications had to be made. Speaking in terms of an overall PSA model, there are also some other issues that need to be considered, such as maintainability and accessibility of the model. In this paper a comparison is made between the two approaches to CCF modeling. Analysis is based on a full-scope Level 1 PSA model for internal initiating events that had originally been developed by a traditional PSA tool and later transferred to a new-generation PSA tool with automated CCF modeling capabilities. Related aspects and issues mentioned above are discussed in the paper. (author)

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

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Jingyuan Zhao

2012-01-01

Full Text Available We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD penalty (Fan and Li, 2001 and Jeffrey’s Prior penalty (Firth, 1993, a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008. The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005 and the LASSO-patternsearch algorithm (Shi et al. 2007.

Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

KAUST Repository

Azad, Mohammad

2018-06-06

Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle

Analysis of event tree with imprecise inputs by fuzzy set theory

International Nuclear Information System (INIS)

Ahn, Kwang Il; Chun, Moon Hyun

1990-01-01

Fuzzy set theory approach is proposed as a method to analyze event trees with imprecise or linguistic input variables such as 'likely' or 'improbable' instead of the numerical probability. In this paper, it is shown how the fuzzy set theory can be applied to the event tree analysis. The result of this study shows that the fuzzy set theory approach can be applied as an acceptable and effective tool for analysis of the event tree with fuzzy type of inputs. Comparisons of the fuzzy theory approach with the probabilistic approach of computing probabilities of final states of the event tree through subjective weighting factors and LHS technique show that the two approaches have common factors and give reasonable results

Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis.

Science.gov (United States)

Cheng, Feon W; Gao, Xiang; Bao, Le; Mitchell, Diane C; Wood, Craig; Sliwinski, Martin J; Smiciklas-Wright, Helen; Still, Christopher D; Rolston, David D K; Jensen, Gordon L

2017-07-01

To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. The conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.1 ± 4.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.2 ± 1.7 years of follow-up, 221 individuals developed functional limitation. Higher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6). Higher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses. © 2017 The Obesity Society.

Tree analysis modeling of the associations between PHQ-9 depressive symptoms and doctor diagnosis of depression in primary care.

Science.gov (United States)

Chin, Weng-Yee; Wan, Eric Yuk Fai; Dowrick, Christopher; Arroll, Bruce; Lam, Cindy Lo Kuen

2018-04-26

The aim of this study was to explore the relationship between patient self-reported Patient Health Questionnaire-9 (PHQ-9) symptoms and doctor diagnosis of depression using a tree analysis approach. This was a secondary analysis on a dataset obtained from 10 179 adult primary care patients and 59 primary care physicians (PCPs) across Hong Kong. Patients completed a waiting room survey collecting data on socio-demographics and the PHQ-9. Blinded doctors documented whether they thought the patient had depression. Data were analyzed using multiple logistic regression and conditional inference decision tree modeling. PCPs diagnosed 594 patients with depression. Logistic regression identified gender, age, employment status, past history of depression, family history of mental illness and recent doctor visit as factors associated with a depression diagnosis. Tree analyses revealed different pathways of association between PHQ-9 symptoms and depression diagnosis for patients with and without past depression. The PHQ-9 symptom model revealed low mood, sense of worthlessness, fatigue, sleep disturbance and functional impairment as early classifiers. The PHQ-9 total score model revealed cut-off scores of >12 and >15 were most frequently associated with depression diagnoses in patients with and without past depression. A past history of depression is the most significant factor associated with the diagnosis of depression. PCPs appear to utilize a hypothetical-deductive problem-solving approach incorporating pre-test probability, with different associated factors for patients with and without past depression. Diagnostic thresholds may be too low for patients with past depression and too high for those without, potentially leading to over and under diagnosis of depression.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

West Virginia's big trees: setting the record straight

Science.gov (United States)

Melissa Thomas-Van Gundy; Robert. Whetsell

2016-01-01

People love big trees, people love to find big trees, and people love to find big trees in the place they call home. Having been suspicious for years, my coauthor and historian Rob Whetsell, approached me with a species identification challenge. There are several photographs of giant trees used by many people to illustrate the past forests of West Virginia,...

Estimating phylogenetic relationships despite discordant gene trees across loci: the species tree of a diverse species group of feather mites (Acari: Proctophyllodidae).

Science.gov (United States)

Knowles, Lacey L; Klimov, Pavel B

2011-11-01

With the increased availability of multilocus sequence data, the lack of concordance of gene trees estimated for independent loci has focused attention on both the biological processes producing the discord and the methodologies used to estimate phylogenetic relationships. What has emerged is a suite of new analytical tools for phylogenetic inference--species tree approaches. In contrast to traditional phylogenetic methods that are stymied by the idiosyncrasies of gene trees, approaches for estimating species trees explicitly take into account the cause of discord among loci and, in the process, provides a direct estimate of phylogenetic history (i.e. the history of species divergence, not divergence of specific loci). We illustrate the utility of species tree estimates with an analysis of a diverse group of feather mites, the pinnatus species group (genus Proctophyllodes). Discord among four sequenced nuclear loci is consistent with theoretical expectations, given the short time separating speciation events (as evident by short internodes relative to terminal branch lengths in the trees). Nevertheless, many of the relationships are well resolved in a Bayesian estimate of the species tree; the analysis also highlights ambiguous aspects of the phylogeny that require additional loci. The broad utility of species tree approaches is discussed, and specifically, their application to groups with high speciation rates--a history of diversification with particular prevalence in host/parasite systems where species interactions can drive rapid diversification.

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses.

Science.gov (United States)

Fouquier, Jennifer; Rideout, Jai Ram; Bolyen, Evan; Chase, John; Shiffer, Arron; McDonald, Daniel; Knight, Rob; Caporaso, J Gregory; Kelley, Scott T

2016-02-24

Fungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child. We applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

Science.gov (United States)

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

A computer-oriented approach to fault-tree construction. Topical report No. 1

International Nuclear Information System (INIS)

Chu, B.B.

1976-11-01

Fault Tree Analysis is one of the major tools for the safety and reliability analysis of large systems. A methodology for systematically constructing fault trees for general complex systems is developed and applied, via the computer program CAT, to several systems. First, a means of representing component behavior by decision tables is presented. In order to use these tables, a procedure for constructing and editing fault trees, either manually or by computer, is described. In order to verify the methodology the computer program CAT has been developed and used to construct fault trees for two systems

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

Science.gov (United States)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

An object-based approach for tree species extraction from digital orthophoto maps

Science.gov (United States)

Jamil, Akhtar; Bayram, Bulent

2018-05-01

Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1 : 5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85 % for all sample images.

Tree growth and competition in an old-growth Picea abies forest of boreal Sweden: influence of tree spatial patterning

Science.gov (United States)

Fraver, Shawn; D'Amato, Anthony W.; Bradford, John B.; Jonsson, Bengt Gunnar; Jönsson, Mari; Esseen, Per-Anders

2013-01-01

Question: What factors best characterize tree competitive environments in this structurally diverse old-growth forest, and do these factors vary spatially within and among stands? Location: Old-growth Picea abies forest of boreal Sweden. Methods: Using long-term, mapped permanent plot data augmented with dendrochronological analyses, we evaluated the effect of neighbourhood competition on focal tree growth by means of standard competition indices, each modified to include various metrics of trees size, neighbour mortality weighting (for neighbours that died during the inventory period), and within-neighbourhood tree clustering. Candidate models were evaluated using mixed-model linear regression analyses, with mean basal area increment as the response variable. We then analysed stand-level spatial patterns of competition indices and growth rates (via kriging) to determine if the relationship between these patterns could further elucidate factors influencing tree growth. Results: Inter-tree competition clearly affected growth rates, with crown volume being the size metric most strongly influencing the neighbourhood competitive environment. Including neighbour tree mortality weightings in models only slightly improved descriptions of competitive interactions. Although the within-neighbourhood clustering index did not improve model predictions, competition intensity was influenced by the underlying stand-level tree spatial arrangement: stand-level clustering locally intensified competition and reduced tree growth, whereas in the absence of such clustering, inter-tree competition played a lesser role in constraining tree growth. Conclusions: Our findings demonstrate that competition continues to influence forest processes and structures in an old-growth system that has not experienced major disturbances for at least two centuries. The finding that the underlying tree spatial pattern influenced the competitive environment suggests caution in interpreting traditional tree

Calibration of the Diameter Distribution Derived from the Area-based Approach with Individual Tree-based Diameter Estimates Using the Airborne Laser Scanning

Science.gov (United States)

Xu, Q.; Hou, Z.; Maltamo, M.; Tokola, T.

2015-12-01

Diameter distributions of trees are important indicators of current forest stand structure and future dynamics. A new method was proposed in the study to combine the diameter distributions derived from the area-based approach (ABA) and the diameter distribution derived from the individual tree detection (ITD) in order to obtain more accurate forest stand attributes. Since dominant trees can be reliably detected and measured by the Lidar data via the ITD, the focus of the study is to retrieve the suppressed trees (trees that were missed by the ITD) from the ABA. Replacement and histogram matching were respectively employed at the plot level to retrieve the suppressed trees. Cut point was detected from the ITD-derived diameter distribution for each sample plot to distinguish dominant trees from the suppressed trees. The results showed that calibrated diameter distributions were more accurate in terms of error index and the entire growing stock estimates. Compared with the best performer between the ABA and the ITD, calibrated diameter distributions decreased the relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points respectively. Calibration improved the estimation of pulpwood fraction significantly, resulting in a negligible bias of the estimated entire growing stock.

The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity.

Science.gov (United States)

Hallquist, Michael N; Hwang, Kai; Luna, Beatriz

2013-11-15

Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability. Copyright © 2013 Elsevier Inc. All rights reserved.

The spectral dimension of random trees

International Nuclear Information System (INIS)

Destri, Claudio; Donetti, Luca

2002-01-01

We present a simple yet rigorous approach to the determination of the spectral dimension of random trees, based on the study of the massless limit of the Gaussian model on such trees. As a by-product, we obtain evidence in favour of a new scaling hypothesis for the Gaussian model on generic bounded graphs and in favour of a previously conjectured exact relation between spectral and connectivity dimensions on more general tree-like structures

Part 2: Conserving and Planting Trees at Development Sites

Science.gov (United States)

Karen Cappiella; Tom Schueler; Tiffany Wright

2006-01-01

This manual presents specific ways to enable developers, engineers or landscape architects to incorporate more trees into a development site. The proposed approach focuses on protecting existing trees, planting trees in storm water treatment practices, and planting trees in other open spaces at the development site. This manual introduces conceptual designs for storm...

Automated Tree Crown Delineation and Biomass Estimation from Airborne LiDAR data: A Comparison of Statistical and Machine Learning Methods

Science.gov (United States)

Gleason, C. J.; Im, J.

2011-12-01

Airborne LiDAR remote sensing has been used effectively in assessing forest biomass because of its canopy penetrating effects and its ability to accurately describe the canopy surface. Current research in assessing biomass using airborne LiDAR focuses on either the individual tree as a base unit of study or statistical representations of a small aggregation of trees (i.e., plot level), and both methods usually rely on regression against field data to model the relationship between the LiDAR-derived data (e.g., volume) and biomass. This study estimates biomass for mixed forests and coniferous plantations (Picea Abies) within Heiberg Memorial Forest, Tully, NY, at both the plot and individual tree level. Plots are regularly spaced with a radius of 13m, and field data include diameter at breast height (dbh), tree height, and tree species. Field data collection and LiDAR data acquisition were seasonally coincident and both obtained in August of 2010. Resulting point cloud density was >5pts/m2. LiDAR data were processed to provide a canopy height surface, and a combination of watershed segmentation, active contouring, and genetic algorithm optimization was applied to delineate individual trees from the surface. This updated delineation method was shown to be more accurate than traditional watershed segmentation. Once trees had been delineated, four biomass estimation models were applied and compared: support vector regression (SVR), linear mixed effects regression (LME), random forest (RF), and Cubist regression. Candidate variables to be used in modeling were derived from the LiDAR surface, and include metrics of height, width, and volume per delineated tree footprint. Previously published allometric equations provided field estimates of biomass to inform the regressions and calculate their accuracy via leave-one-out cross validation. This study found that for forests such as found in the study area, aggregation of individual trees to form a plot-based estimate of

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

International Nuclear Information System (INIS)

Chen, Kuilin; Yu, Jie

2014-01-01

Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

Standards for Standardized Logistic Regression Coefficients

Science.gov (United States)

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Direct evaluation of fault trees using object-oriented programming techniques

Science.gov (United States)

Patterson-Hine, F. A.; Koen, B. V.

1989-01-01

Object-oriented programming techniques are used in an algorithm for the direct evaluation of fault trees. The algorithm combines a simple bottom-up procedure for trees without repeated events with a top-down recursive procedure for trees with repeated events. The object-oriented approach results in a dynamic modularization of the tree at each step in the reduction process. The algorithm reduces the number of recursive calls required to solve trees with repeated events and calculates intermediate results as well as the solution of the top event. The intermediate results can be reused if part of the tree is modified. An example is presented in which the results of the algorithm implemented with conventional techniques are compared to those of the object-oriented approach.

Fault tree analysis for vital area identification

International Nuclear Information System (INIS)

Varnado, G.B.; Ortiz, N.R.

1978-01-01

This paper discusses the use of fault tree analysis to identify those areas of nuclear fuel cycle facilities which must be protected to prevent acts of sabotage that could lead to sifnificant release of radioactive material. By proper manipulation of the fault trees for a plant, an analyst can identify vital areas in a manner consistent with regulatory definitions. This paper discusses the general procedures used in the analysis of any nuclear facility. In addition, a structured, generic approach to the development of the fault trees for nuclear power reactors is presented along with selected results of the application of the generic approach to several plants

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

Directory of Open Access Journals (Sweden)

Iliev Iliycho

2018-01-01

Full Text Available Subject of investigation is a new high-powered strontium bromide (SrBr2 vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

LINEAR MIXED MODEL TO DESCRIBE THE BASAL AREA INCREMENT FOR INDIVUDUAL CEDRO (Cedrela odorata L.TREES IN OCCIDENTAL AMAZON, BRAZIL

Directory of Open Access Journals (Sweden)

Thiago Augusto da Cunha

2013-01-01

Full Text Available Reliable growth data from trees are important to establish a rational forest management. Characteristics from trees, like the size, crown architecture and competition indices have been used to mathematically describe the increment efficiently when associated with them. However, the precise role of these effects in the growth-modeling destined to tropical trees needs to be further studied. Here it is reconstructed the basal area increment (BAI of individual Cedrela odorata trees, sampled at Amazon forest, to develop a growth- model using potential-predictors like: (1 classical tree size; (2 morphometric data; (3 competition and (4 social position including liana loads. Despite the large variation in tree size and growth, we observed that these kinds of predictor variables described well the BAI in level of individual tree. The fitted mixed model achieve a high efficiency (R2=92.7 % and predicted 3-years BAI over bark for trees of Cedrela odorata ranging from 10 to 110 cm at diameter at breast height. Tree height, steam slenderness and crown formal demonstrated high influence in the BAI growth model and explaining most of the growth variance (Partial R2=87.2%. Competition variables had negative influence on the BAI, however, explained about 7% of the total variation. The introduction of a random parameter on the regressions model (mixed modelprocedure has demonstrated a better significance approach to the data observed and showed more realistic predictions than the fixed model.

On Directed Edge-Disjoint Spanning Trees in Product Networks, An Algorithmic Approach

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A.R. Touzene

2014-12-01

Full Text Available In (Ku et al. 2003, the authors have proposed a construction of edge-disjoint spanning trees EDSTs in undirected product networks. Their construction method focuses more on showing the existence of a maximum number (n1+n2-1 of EDSTs in product network of two graphs, where factor graphs have respectively n1 and n2 EDSTs. In this paper, we propose a new systematic and algorithmic approach to construct (n1+n2 directed routed EDST in the product networks. The direction of an edge is added to support bidirectional links in interconnection networks. Our EDSTs can be used straightforward to develop efficient collective communication algorithms for both models store-and-forward and wormhole.

Optimizing continuous cover management of boreal forest when timber prices and tree growth are stochastic

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Timo Pukkala

2015-03-01

Full Text Available Background Decisions on forest management are made under risk and uncertainty because the stand development cannot be predicted exactly and future timber prices are unknown. Deterministic calculations may lead to biased advice on optimal forest management. The study optimized continuous cover management of boreal forest in a situation where tree growth, regeneration, and timber prices include uncertainty. Methods Both anticipatory and adaptive optimization approaches were used. The adaptive approach optimized the reservation price function instead of fixed cutting years. The future prices of different timber assortments were described by cross-correlated auto-regressive models. The high variation around ingrowth model was simulated using a model that describes the cross- and autocorrelations of the regeneration results of different species and years. Tree growth was predicted with individual tree models, the predictions of which were adjusted on the basis of a climate-induced growth trend, which was stochastic. Residuals of the deterministic diameter growth model were also simulated. They consisted of random tree factors and cross- and autocorrelated temporal terms. Results Of the analyzed factors, timber price caused most uncertainty in the calculation of the net present value of a certain management schedule. Ingrowth and climate trend were less significant sources of risk and uncertainty than tree growth. Stochastic anticipatory optimization led to more diverse post-cutting stand structures than obtained in deterministic optimization. Cutting interval was shorter when risk and uncertainty were included in the analyses. Conclusions Adaptive optimization and management led to 6%–14% higher net present values than obtained in management that was based on anticipatory optimization. Increasing risk aversion of the forest landowner led to earlier cuttings in a mature stand. The effect of risk attitude on optimization results was small.

Runtime Optimizations for Tree-Based Machine Learning Models

NARCIS (Netherlands)

N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

2014-01-01

htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

Pathological assessment of liver fibrosis regression

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WANG Bingqiong

2017-03-01

Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.

Relationships for Cost and Uncertainty of Decision Trees

KAUST Repository

Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2013-01-01

This chapter is devoted to the design of new tools for the study of decision trees. These tools are based on dynamic programming approach and need the consideration of subtables of the initial decision table. So this approach is applicable only to relatively small decision tables. The considered tools allow us to compute: 1. Theminimum cost of an approximate decision tree for a given uncertainty value and a cost function. 2. The minimum number of nodes in an exact decision tree whose depth is at most a given value. For the first tool we considered various cost functions such as: depth and average depth of a decision tree and number of nodes (and number of terminal and nonterminal nodes) of a decision tree. The uncertainty of a decision table is equal to the number of unordered pairs of rows with different decisions. The uncertainty of approximate decision tree is equal to the maximum uncertainty of a subtable corresponding to a terminal node of the tree. In addition to the algorithms for such tools we also present experimental results applied to various datasets acquired from UCI ML Repository [4]. © Springer-Verlag Berlin Heidelberg 2013.

The stopping rules for winsorized tree

Science.gov (United States)

Ch'ng, Chee Keong; Mahat, Nor Idayu

2017-11-01

Winsorized tree is a modified tree-based classifier that is able to investigate and to handle all outliers in all nodes along the process of constructing the tree. It overcomes the tedious process of constructing a classical tree where the splitting of branches and pruning go concurrently so that the constructed tree would not grow bushy. This mechanism is controlled by the proposed algorithm. In winsorized tree, data are screened for identifying outlier. If outlier is detected, the value is neutralized using winsorize approach. Both outlier identification and value neutralization are executed recursively in every node until predetermined stopping criterion is met. The aim of this paper is to search for significant stopping criterion to stop the tree from further splitting before overfitting. The result obtained from the conducted experiment on pima indian dataset proved that the node could produce the final successor nodes (leaves) when it has achieved the range of 70% in information gain.

Meta-learning in decision tree induction

CERN Document Server

Grąbczewski, Krzysztof

2014-01-01

The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimen...

New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning.

Science.gov (United States)

Malhi, Yadvinder; Jackson, Tobias; Patrick Bentley, Lisa; Lau, Alvaro; Shenkin, Alexander; Herold, Martin; Calders, Kim; Bartholomeus, Harm; Disney, Mathias I

2018-04-06

Terrestrial laser scanning (TLS) opens up the possibility of describing the three-dimensional structures of trees in natural environments with unprecedented detail and accuracy. It is already being extensively applied to describe how ecosystem biomass and structure vary between sites, but can also facilitate major advances in developing and testing mechanistic theories of tree form and forest structure, thereby enabling us to understand why trees and forests have the biomass and three-dimensional structure they do. Here we focus on the ecological challenges and benefits of understanding tree form, and highlight some advances related to capturing and describing tree shape that are becoming possible with the advent of TLS. We present examples of ongoing work that applies, or could potentially apply, new TLS measurements to better understand the constraints on optimization of tree form. Theories of resource distribution networks, such as metabolic scaling theory, can be tested and further refined. TLS can also provide new approaches to the scaling of woody surface area and crown area, and thereby better quantify the metabolism of trees. Finally, we demonstrate how we can develop a more mechanistic understanding of the effects of avoidance of wind risk on tree form and maximum size. Over the next few years, TLS promises to deliver both major empirical and conceptual advances in the quantitative understanding of trees and tree-dominated ecosystems, leading to advances in understanding the ecology of why trees and ecosystems look and grow the way they do.

Quantification and Visualization of Variation in Anatomical Trees

Energy Technology Data Exchange (ETDEWEB)

Amenta, Nina; Datar, Manasi; Dirksen, Asger; de Bruihne, Marleen; Feragen, Aasa; Ge, Xiaoyin; Holst Pedersen, Jesper; Howard, Marylesa; Owen, Megan; Petersen, Jens; Shi, Jie; Xu, Qiuping

2015-07-01

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.

Tree filtering for high density airborne LiDAR data

NARCIS (Netherlands)

Abd Rahman, M.Z.; Gorte, B.G.H.

2008-01-01

A high resolution Airborne LiDAR data creates better opportunity for an individual tree measurement and provides valuable results for more precise forest inventory. This paper presents tree filtering approach that able to separate dominant tree and undergrowth vegetation. The results can be used for

Linking and Cutting Spanning Trees

Directory of Open Access Journals (Sweden)

Luís M. S. Russo

2018-04-01

Full Text Available We consider the problem of uniformly generating a spanning tree for an undirected connected graph. This process is useful for computing statistics, namely for phylogenetic trees. We describe a Markov chain for producing these trees. For cycle graphs, we prove that this approach significantly outperforms existing algorithms. For general graphs, experimental results show that the chain converges quickly. This yields an efficient algorithm due to the use of proper fast data structures. To obtain the mixing time of the chain we describe a coupling, which we analyze for cycle graphs and simulate for other graphs.

New approaches to phylogenetic tree search and their application to large numbers of protein alignments.

Science.gov (United States)

Whelan, Simon

2007-10-01

Phylogenetic tree estimation plays a critical role in a wide variety of molecular studies, including molecular systematics, phylogenetics, and comparative genomics. Finding the optimal tree relating a set of sequences using score-based (optimality criterion) methods, such as maximum likelihood and maximum parsimony, may require all possible trees to be considered, which is not feasible even for modest numbers of sequences. In practice, trees are estimated using heuristics that represent a trade-off between topological accuracy and speed. I present a series of novel algorithms suitable for score-based phylogenetic tree reconstruction that demonstrably improve the accuracy of tree estimates while maintaining high computational speeds. The heuristics function by allowing the efficient exploration of large numbers of trees through novel hill-climbing and resampling strategies. These heuristics, and other computational approximations, are implemented for maximum likelihood estimation of trees in the program Leaphy, and its performance is compared to other popular phylogenetic programs. Trees are estimated from 4059 different protein alignments using a selection of phylogenetic programs and the likelihoods of the tree estimates are compared. Trees estimated using Leaphy are found to have equal to or better likelihoods than trees estimated using other phylogenetic programs in 4004 (98.6%) families and provide a unique best tree that no other program found in 1102 (27.1%) families. The improvement is particularly marked for larger families (80 to 100 sequences), where Leaphy finds a unique best tree in 81.7% of families.

Automatic design of decision-tree induction algorithms

CERN Document Server

Barros, Rodrigo C; Freitas, Alex A

2015-01-01

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning, and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain o

Estimating tree cavity distributions from historical FIA data

Science.gov (United States)

Mark D. Nelson; Charlotte. Roy

2012-01-01

Tree cavities provide important habitat features for a variety of wildlife species. We describe an approach for using historical FIA data to estimate the number of trees containing cavities during the 1990s in seven states of the Upper Midwest. We estimated a total of 280 million cavity-containing trees. Iowa and Missouri had the highest percentages of cavity-...

A novel approach to internal crown characterization for coniferous tree species classification

Science.gov (United States)

Harikumar, A.; Bovolo, F.; Bruzzone, L.

2016-10-01

The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier.

A Practical pedestrian approach to parsimonious regression with inaccurate inputs

Directory of Open Access Journals (Sweden)

Seppo Karrila

2014-04-01

Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.

Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees.

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Conrad, Anna O; Bonello, Pierluigi

2015-01-01

New approaches for identifying disease resistant trees are needed as the incidence of diseases caused by non-native and invasive pathogens increases. These approaches must be rapid, reliable, cost-effective, and should have the potential to be adapted for high-throughput screening or phenotyping. Within the context of trees and tree diseases, we summarize vibrational spectroscopic and chemometric methods that have been used to distinguish between groups of trees which vary in disease susceptibility or other important characteristics based on chemical fingerprint data. We also provide specific examples from the literature of where these approaches have been used successfully. Finally, we discuss future application of these approaches for wide-scale screening and phenotyping efforts aimed at identifying disease resistant trees and managing forest diseases.

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

A Bayesian nonparametric approach to causal inference on quantiles.

Science.gov (United States)

Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

2018-02-25

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

Proactive data mining with decision trees

CERN Document Server

Dahan, Haim; Rokach, Lior; Maimon, Oded

2014-01-01

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting crite

Genome-wide comparative analysis of phylogenetic trees: the prokaryotic forest of life.

Science.gov (United States)

Puigbò, Pere; Wolf, Yuri I; Koonin, Eugene V

2012-01-01

Genome-wide comparison of phylogenetic trees is becoming an increasingly common approach in evolutionary genomics, and a variety of approaches for such comparison have been developed. In this article, we present several methods for comparative analysis of large numbers of phylogenetic trees. To compare phylogenetic trees taking into account the bootstrap support for each internal branch, the Boot-Split Distance (BSD) method is introduced as an extension of the previously developed Split Distance method for tree comparison. The BSD method implements the straightforward idea that comparison of phylogenetic trees can be made more robust by treating tree splits differentially depending on the bootstrap support. Approaches are also introduced for detecting tree-like and net-like evolutionary trends in the phylogenetic Forest of Life (FOL), i.e., the entirety of the phylogenetic trees for conserved genes of prokaryotes. The principal method employed for this purpose includes mapping quartets of species onto trees to calculate the support of each quartet topology and so to quantify the tree and net contributions to the distances between species. We describe the application of these methods to analyze the FOL and the results obtained with these methods. These results support the concept of the Tree of Life (TOL) as a central evolutionary trend in the FOL as opposed to the traditional view of the TOL as a "species tree."

Comparison of Greedy Algorithms for Decision Tree Optimization

KAUST Repository

Alkhalid, Abdulaziz

2013-01-01

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values of average depth, depth, number of nodes, number of terminal nodes, and number of nonterminal nodes of decision trees. We compare average depth, depth, number of nodes, number of terminal nodes and number of nonterminal nodes of constructed trees with minimum values of the considered parameters obtained based on a dynamic programming approach. We report experiments performed on data sets from UCI ML Repository and randomly generated binary decision tables. As a result, for depth, average depth, and number of nodes we propose a number of good heuristics. © Springer-Verlag Berlin Heidelberg 2013.

Systems analysis approach to probabilistic modeling of fault trees

International Nuclear Information System (INIS)

Bartholomew, R.J.; Qualls, C.R.

1985-01-01

A method of probabilistic modeling of fault tree logic combined with stochastic process theory (Markov modeling) has been developed. Systems are then quantitatively analyzed probabilistically in terms of their failure mechanisms including common cause/common mode effects and time dependent failure and/or repair rate effects that include synergistic and propagational mechanisms. The modeling procedure results in a state vector set of first order, linear, inhomogeneous, differential equations describing the time dependent probabilities of failure described by the fault tree. The solutions of this Failure Mode State Variable (FMSV) model are cumulative probability distribution functions of the system. A method of appropriate synthesis of subsystems to form larger systems is developed and applied to practical nuclear power safety systems

An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

OpenAIRE

浅野, 美代子; マーコ, ユー K.W.

2007-01-01

This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

Pareto Efficient Solution of Attack-Defence Trees

NARCIS (Netherlands)

Aslanyan, Zaruhi; Nielson, Flemming

Attack-defence trees are a promising approach for representing threat scenarios and possible countermeasures in a concise and intuitive manner. An attack-defence tree describes the interaction between an attacker and a defender, and is evaluated by assigning parameters to the nodes, such as

QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIP OF ANTIMALARIAL COMPOUND OF ARTEMISININ DERIVATIVES USING PRINCIPAL COMPONENT REGRESSION APPROACH

Directory of Open Access Journals (Sweden)

Paul Robert Martin Werfette

2010-06-01

Full Text Available Analysis of quantitative structure - activity relationship (QSAR for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation,  (;;   Keywords: QSAR, antimalarial, artemisinin, principal component regression

Partitioning of late gestation energy expenditure in ewes using indirect calorimetry and a linear regression approach

DEFF Research Database (Denmark)

Kiani, Alishir; Chwalibog, André; Nielsen, Mette O

2007-01-01

Late gestation energy expenditure (EE(gest)) originates from energy expenditure (EE) of development of conceptus (EE(conceptus)) and EE of homeorhetic adaptation of metabolism (EE(homeorhetic)). Even though EE(gest) is relatively easy to quantify, its partitioning is problematic. In the present...... study metabolizable energy (ME) intake ranges for twin-bearing ewes were 220-440, 350- 700, 350-900 kJ per metabolic body weight (W0.75) at week seven, five, two pre-partum respectively. Indirect calorimetry and a linear regression approach were used to quantify EE(gest) and then partition to EE......(conceptus) and EE(homeorhetic). Energy expenditure of basal metabolism of the non-gravid tissues (EE(bmng)), derived from the intercept of the linear regression equation of retained energy [kJ/W0.75] and ME intake [kJ/W(0.75)], was 298 [kJ/ W0.75]. Values of the intercepts of the regression equations at week seven...

Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil.

Directory of Open Access Journals (Sweden)

Matheus Henrique Nunes

Full Text Available Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects.

Species trees for the tree swallows (Genus Tachycineta): an alternative phylogenetic hypothesis to the mitochondrial gene tree.

Science.gov (United States)

Dor, Roi; Carling, Matthew D; Lovette, Irby J; Sheldon, Frederick H; Winkler, David W

2012-10-01

The New World swallow genus Tachycineta comprises nine species that collectively have a wide geographic distribution and remarkable variation both within- and among-species in ecologically important traits. Existing phylogenetic hypotheses for Tachycineta are based on mitochondrial DNA sequences, thus they provide estimates of a single gene tree. In this study we sequenced multiple individuals from each species at 16 nuclear intron loci. We used gene concatenated approaches (Bayesian and maximum likelihood) as well as coalescent-based species tree inference to reconstruct phylogenetic relationships of the genus. We examined the concordance and conflict between the nuclear and mitochondrial trees and between concatenated and coalescent-based inferences. Our results provide an alternative phylogenetic hypothesis to the existing mitochondrial DNA estimate of phylogeny. This new hypothesis provides a more accurate framework in which to explore trait evolution and examine the evolution of the mitochondrial genome in this group. Copyright © 2012 Elsevier Inc. All rights reserved.

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction

Science.gov (United States)

De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David

2016-01-01

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction.

Science.gov (United States)

De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David

2016-05-01

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

Dependencies in event trees analyzed by Petri nets

International Nuclear Information System (INIS)

Nývlt, Ondřej; Rausand, Marvin

2012-01-01

This paper discusses how non-marked Petri nets can be used to model and analyze event trees where the pivotal (branching) events are dependent and modeled by fault trees. The dependencies may, for example, be caused by shared utilities, shared components, or general common cause failures that are modeled by beta-factor models. These dependencies are cumbersome to take into account when using standard event-/fault tree modeling techniques, and may lead to significant errors in the calculated end-state probabilities of the event tree if they are not properly analyzed. A new approach is proposed in this paper, where the whole event tree is modeled by a non-marked Petri net and where P-invariants, representing the structural properties of the Petri net, are used to obtain the frequency of each end-state of the event tree with dependencies. The new approach is applied to a real example of an event tree analysis of the Strahov highway tunnel in Prague, Czech Republic, including two types of dependencies (shared Programmable Logic Controllers and Common Cause Failures). - Highlights: ► In this paper, we model and analyze event trees (ET) using Petri nets. ► The pivotal events of the modeled event trees are dependent (e.g., shared PLCs, CCF). ► A new method based on P-invariants to obtain probabilities of end states is proposed. ► Method is shown in the case study of the Stahov tunnel in the Czech Republic.

Comparison of Greedy Algorithms for Decision Tree Optimization

KAUST Repository

Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

2013-01-01

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values

Behavior of cesium-134 in the tea tree

International Nuclear Information System (INIS)

Xu Yinliang; Chen Kaixuan; Chen Chuangqun

1996-01-01

The radioactivity changes of 134 Cs in the aged and the young leaves followed an exponential regression function after spraying 134 Cs in the tea trees. Contamination by spraying 134 Cs greatly harmed tea tree and by irrigating or mixing 134 Cs with soil resulted in a potential endangerment. The concentrating ability of tea leaves for 134 CS was very low and K value was 0.02. After the fresh tea leaves were processed to dry tea, the content of 134 Cs decreased by about 13.3%. When the tea leaves were soaked in hot water, the extraction ratio was around 83.6%

Chilling and heat requirements for flowering in temperate fruit trees.

Science.gov (United States)

Guo, Liang; Dai, Junhu; Ranjitkar, Sailesh; Yu, Haiying; Xu, Jianchu; Luedeling, Eike

2014-08-01

Climate change has affected the rates of chilling and heat accumulation, which are vital for flowering and production, in temperate fruit trees, but few studies have been conducted in the cold-winter climates of East Asia. To evaluate tree responses to variation in chill and heat accumulation rates, partial least squares regression was used to correlate first flowering dates of chestnut (Castanea mollissima Blume) and jujube (Zizyphus jujube Mill.) in Beijing, China, with daily chill and heat accumulation between 1963 and 2008. The Dynamic Model and the Growing Degree Hour Model were used to convert daily records of minimum and maximum temperature into horticulturally meaningful metrics. Regression analyses identified the chilling and forcing periods for chestnut and jujube. The forcing periods started when half the chilling requirements were fulfilled. Over the past 50 years, heat accumulation during tree dormancy increased significantly, while chill accumulation remained relatively stable for both species. Heat accumulation was the main driver of bloom timing, with effects of variation in chill accumulation negligible in Beijing’s cold-winter climate. It does not seem likely that reductions in chill will have a major effect on the studied species in Beijing in the near future. Such problems are much more likely for trees grown in locations that are substantially warmer than their native habitats, such as temperate species in the subtropics and tropics.

Chilling and heat requirements for flowering in temperate fruit trees

Science.gov (United States)

Guo, Liang; Dai, Junhu; Ranjitkar, Sailesh; Yu, Haiying; Xu, Jianchu; Luedeling, Eike

2014-08-01

Climate change has affected the rates of chilling and heat accumulation, which are vital for flowering and production, in temperate fruit trees, but few studies have been conducted in the cold-winter climates of East Asia. To evaluate tree responses to variation in chill and heat accumulation rates, partial least squares regression was used to correlate first flowering dates of chestnut ( Castanea mollissima Blume) and jujube ( Zizyphus jujube Mill.) in Beijing, China, with daily chill and heat accumulation between 1963 and 2008. The Dynamic Model and the Growing Degree Hour Model were used to convert daily records of minimum and maximum temperature into horticulturally meaningful metrics. Regression analyses identified the chilling and forcing periods for chestnut and jujube. The forcing periods started when half the chilling requirements were fulfilled. Over the past 50 years, heat accumulation during tree dormancy increased significantly, while chill accumulation remained relatively stable for both species. Heat accumulation was the main driver of bloom timing, with effects of variation in chill accumulation negligible in Beijing's cold-winter climate. It does not seem likely that reductions in chill will have a major effect on the studied species in Beijing in the near future. Such problems are much more likely for trees grown in locations that are substantially warmer than their native habitats, such as temperate species in the subtropics and tropics.

A regression approach for Zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

Discrete Discriminant analysis based on tree-structured graphical models

DEFF Research Database (Denmark)

Perez de la Cruz, Gonzalo; Eslava, Guillermina

The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression.......The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

Science.gov (United States)

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

Categorizing ideas about trees: a tree of trees.

Science.gov (United States)

Fisler, Marie; Lecointre, Guillaume

2013-01-01

The aim of this study is to explore whether matrices and MP trees used to produce systematic categories of organisms could be useful to produce categories of ideas in history of science. We study the history of the use of trees in systematics to represent the diversity of life from 1766 to 1991. We apply to those ideas a method inspired from coding homologous parts of organisms. We discretize conceptual parts of ideas, writings and drawings about trees contained in 41 main writings; we detect shared parts among authors and code them into a 91-characters matrix and use a tree representation to show who shares what with whom. In other words, we propose a hierarchical representation of the shared ideas about trees among authors: this produces a "tree of trees." Then, we categorize schools of tree-representations. Classical schools like "cladists" and "pheneticists" are recovered but others are not: "gradists" are separated into two blocks, one of them being called here "grade theoreticians." We propose new interesting categories like the "buffonian school," the "metaphoricians," and those using "strictly genealogical classifications." We consider that networks are not useful to represent shared ideas at the present step of the study. A cladogram is made for showing who is sharing what with whom, but also heterobathmy and homoplasy of characters. The present cladogram is not modelling processes of transmission of ideas about trees, and here it is mostly used to test for proximity of ideas of the same age and for categorization.

A study of Solar-Enso correlation with southern Brazil tree ring index (1955- 1991)

Science.gov (United States)

Rigozo, N.; Nordemann, D.; Vieira, L.; Echer, E.

The effects of solar activity and El Niño-Southern Oscillation on tree growth in Southern Brazil were studied by correlation analysis. Trees for this study were native Araucaria (Araucaria Angustifolia)from four locations in Rio Grande do Sul State, in Southern Brazil: Canela (29o18`S, 50o51`W, 790 m asl), Nova Petropolis (29o2`S, 51o10`W, 579 m asl), Sao Francisco de Paula (29o25`S, 50o24`W, 930 m asl) and Sao Martinho da Serra (29o30`S, 53o53`W, 484 m asl). From these four sites, an average tree ring Index for this region was derived, for the period 1955-1991. Linear correlations were made on annual and 10 year running averages of this tree ring Index, of sunspot number Rz and SOI. For annual averages, the correlation coefficients were low, and the multiple regression between tree ring and SOI and Rz indicates that 20% of the variance in tree rings was explained by solar activity and ENSO variability. However, when the 10 year running averages correlations were made, the coefficient correlations were much higher. A clear anticorrelation is observed between SOI and Index (r=-0.81) whereas Rz and Index show a positive correlation (r=0.67). The multiple regression of 10 year running averages indicates that 76% of the variance in tree ring INdex was explained by solar activity and ENSO. These results indicate that the effects of solar activity and ENSO on tree rings are better seen on long timescales.

DIOECY EFFECT ON GROWTH OF PLANTED Araucaria angustifolia Bert. O. Kuntze TREES

Directory of Open Access Journals (Sweden)

Afonso Figueiredo Filho

2015-09-01

Full Text Available The aim of the study was to evaluate the influence of dioecy on the growth in diameter at breast height (DBH, individual basal area, total height and individual volume of planted Araucaria angustifolia trees. The data came from 60 trees (30 male trees and 30 female trees sampled from a 30-year-old plantation in Paraná State. Complete stem analysis was used to recover historical tree growth. The Chapman-Richards model was fitted in order to represent the growth and yield of the dendrometric variables for female and male Araucaria trees. Weighted non-linear least squared method was used in the fitting process and the inverse variance was used as weight to solve the problem of heteroscedasticity. The test to verify the equality of parameters and the identity of non-linear regression models proposed by Regazzi (2003 was used to test the influence of dioecy on growth. Dioecy significantly influenced the growth of Araucaria, and female trees have higher growth in diameter, individual basal area and individual volume, while male trees showed better height development. The asymptotic coefficient of the Chapman-Richards model showed that male trees have a higher asymptotic height than female trees.

Multicasting in Wireless Communications (Ad-Hoc Networks): Comparison against a Tree-Based Approach

Science.gov (United States)

Rizos, G. E.; Vasiliadis, D. C.

2007-12-01

We examine on-demand multicasting in ad hoc networks. The Core Assisted Mesh Protocol (CAMP) is a well-known protocol for multicast routing in ad-hoc networks, generalizing the notion of core-based trees employed for internet multicasting into multicast meshes that have much richer connectivity than trees. On the other hand, wireless tree-based multicast routing protocols use much simpler structures for determining route paths, using only parent-child relationships. In this work, we compare the performance of the CAMP protocol against the performance of wireless tree-based multicast routing protocols, in terms of two important factors, namely packet delay and ratio of dropped packets.

Representing cognitive activities and errors in HRA trees

International Nuclear Information System (INIS)

Gertman, D.I.

1992-01-01

A graphic representation method is presented herein for adapting an existing technology--human reliability analysis (HRA) event trees, used to support event sequence logic structures and calculations--to include a representation of the underlying cognitive activity and corresponding errors associated with human performance. The analyst is presented with three potential means of representing human activity: the NUREG/CR-1278 HRA event tree approach; the skill-, rule- and knowledge-based paradigm; and the slips, lapses, and mistakes paradigm. The above approaches for representing human activity are integrated in order to produce an enriched HRA event tree -- the cognitive event tree system (COGENT)-- which, in turn, can be used to increase the analyst's understanding of the basic behavioral mechanisms underlying human error and the representation of that error in probabilistic risk assessment. Issues pertaining to the implementation of COGENT are also discussed

Characteristics of the tree-drawing test in chronic schizophrenia.

Science.gov (United States)

Kaneda, Ayako; Yasui-Furukori, Norio; Saito, Manabu; Sugawara, Norio; Nakagami, Taku; Furukori, Hanako; Kaneko, Sunao

2010-04-01

A tree-drawing test acts as both a projective psychological examination as well as a supplementary psychodiagnostic tool. There is little information relating the characteristics of schizophrenia and the tree-drawing test. The present study compared the structural and morphological differences in the results of the tree-drawing test between schizophrenic patients and healthy individuals, as well as between schizophrenic patients who responded well to treatment and those who responded poorly. The subjects included 202 chronic schizophrenic patients and 113 healthy individuals. The schizophrenic patients were categorized as 'good responders' or 'poor responders' based on their response to medical treatments. The tree-drawing test was performed on all subjects. The tree drawn by each subject was analyzed structurally and morphologically. There were significant differences between the trunk and branches drawn by schizophrenic patients and those drawn by healthy controls. There were no significant differences between the good responders and the poor responders in any aspect of the tree drawings. Multiple regression models showed that the ratio of the tree area to the total area of the drawing paper, the width of the trunk, the trunk base opening, and the size of the branch ends were significantly associated with schizophrenia. The present study suggests that the trees drawn by schizophrenic patients are significantly different from those drawn by healthy individuals, but among schizophrenic patients, it is difficult to distinguish between good responders and poor responders using the tree-drawing test.

Tree agency and urban forest governance

DEFF Research Database (Denmark)

Konijnendijk, Cecil Cornelis

2016-01-01

governance also involving businesses and civic society. However, governance theory usually does not consider the role of non-human agency, which can be considered problematic due to, for example, the important role of urban trees in place making. The purpose of this paper is to provide further insight...... into the importance of considering tree agency in governance. Design/methodology/approach – Taking an environmental governance and actor network theory perspective, the paper presents a critical view of current urban forest governance, extending the perspective to include not only a wide range of human actors......, but also trees as important non-human actors. Findings – Urban forest governance has become more complex and involves a greater range of actors and actor networks. However, the agency of trees in urban forest governance is seldom well developed. Trees, in close association with local residents, create...

Omics studies of citrus, grape and rosaceae fruit trees.

Science.gov (United States)

Shiratake, Katsuhiro; Suzuki, Mami

2016-01-01

Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

Science.gov (United States)

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

A support vector machine based test for incongruence between sets of trees in tree space

Science.gov (United States)

2012-01-01

Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA.

Directory of Open Access Journals (Sweden)

Tyson L Swetnam

Full Text Available A significant concern about Metabolic Scaling Theory (MST in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1 tree condition and physical form, (2 the level of inter-tree competition (e.g. open vs closed stand structure, (3 increasing tree age, and (4 differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381 were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926. Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058, as did recently dead standing trees (n = 375. Exponent value of the mean-tree model that disregarded tree condition (n = 3,740 was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST.

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA.

Science.gov (United States)

Swetnam, Tyson L; O'Connor, Christopher D; Lynch, Ann M

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1) tree condition and physical form, (2) the level of inter-tree competition (e.g. open vs closed stand structure), (3) increasing tree age, and (4) differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381) were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926). Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058), as did recently dead standing trees (n = 375). Exponent value of the mean-tree model that disregarded tree condition (n = 3,740) was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST.

Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model

Energy Technology Data Exchange (ETDEWEB)

Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung [Dept. of Radiology, Seoul National University College of Medicine, Seoul (Korea, Republic of)

2015-12-15

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model

International Nuclear Information System (INIS)

Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

Science.gov (United States)

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Socio-economic determinants of growing trees on farms in the middle hills of Nepal

DEFF Research Database (Denmark)

Oli, B.N.; Treue, Thorsten; Larsen, Helle Overgaard

2015-01-01

were found. The Shannon–Wiener index was 2.46 and Simpson’s Dominance index was 0.15. Trees on farmland contributed on average 43 % of households’ firewood and fodder consumption. Apparent determinants of tree growing were identified through OLS regression; they included size of land and livestock......On-farm tree growing is potentially important for livelihood strategies and forest conservation, and varies greatly according to local contexts. A detailed knowledge base is therefore needed, requiring, inter alia, the documentation of factors associated with growing trees on farms. The present...... study surveyed 304 randomly sampled households in ten community forestry user groups in Nepal, eliciting data on demographics, income and consumption of tree products. All trees on households’ farm land were registered by species. Farmers had on average 65 trees per hectare and a total of 92 species...

Seeing beyond fertiliser trees : a case study of a community based participatory approach to agroforestry research and development in western Kenya

NARCIS (Netherlands)

Kiptot, E.

2007-01-01

Key words: village committee approach, agroforestry, improved tree fallows, biomass transfer, realist evaluation, soil fertility, adoption, dissemination. The thesis explores and describes various processes that take place in the implementation of a community based participatory initiative

Macroscopic Models of Clique Tree Growth for Bayesian Networks

Data.gov (United States)

National Aeronautics and Space Administration — In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to...

Rooted triple consensus and anomalous gene trees

Directory of Open Access Journals (Sweden)

Schmidt Heiko A

2008-04-01

Full Text Available Abstract Background Anomalous gene trees (AGTs are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs. Results Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, we tested whether AGTs substantially interfere with the reconstruction of the metazoan phylogeny. Conclusion Evidence of AGTs was not found in this data set, suggesting that erroneously reconstructed gene trees are the most significant challenge in the reconstruction of phylogenetic relationships among species with current data. The new method does however rule out the erroneous reconstruction of deep or poorly resolved splits in the presence of lineage sorting.

Logic regression and its extensions.

Science.gov (United States)

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Decision tree approach for classification of remotely sensed satellite ...

Indian Academy of Sciences (India)

sensed satellite data using open source support. Richa Sharma .... Decision tree classification techniques have been .... the USGS Earth Resource Observation Systems. (EROS) ... for shallow water, 11% were for sparse and dense built-up ...

Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

International Nuclear Information System (INIS)

Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

2015-01-01

Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis

Spatial soil zinc content distribution from terrain parameters: A GIS-based decision-tree model in Lebanon

Energy Technology Data Exchange (ETDEWEB)

Bou Kheir, Rania, E-mail: rania.boukheir@agrsci.d [Lebanese University, Faculty of Letters and Human Sciences, Department of Geography, GIS Research Laboratory, P.O. Box 90-1065, Fanar (Lebanon); Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark); Greve, Mogens H. [Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark); Abdallah, Chadi [National Council for Scientific Research, Remote Sensing Center, P.O. Box 11-8281, Beirut (Lebanon); Dalgaard, Tommy [Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark)

2010-02-15

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. - GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.

Spatial soil zinc content distribution from terrain parameters: A GIS-based decision-tree model in Lebanon

International Nuclear Information System (INIS)

Bou Kheir, Rania; Greve, Mogens H.; Abdallah, Chadi; Dalgaard, Tommy

2010-01-01

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. - GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Decision tree approach for classification of remotely sensed satellite

Indian Academy of Sciences (India)

DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

Visualizing phylogenetic tree landscapes.

Science.gov (United States)

Wilgenbusch, James C; Huang, Wen; Gallivan, Kyle A

2017-02-02

Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. Different partitions within these new alignments (e.g., genes, codon positions, and structural features) often favor hundreds if not thousands of competing phylogenies. Summarizing and comparing phylogenies obtained from multi-source data sets using current consensus tree methods discards valuable information and can disguise potential methodological problems. Discovery of efficient and accurate dimensionality reduction methods used to display at once in 2- or 3- dimensions the relationship among these competing phylogenies will help practitioners diagnose the limits of current evolutionary models and potential problems with phylogenetic reconstruction methods when analyzing large multi-source data sets. We introduce several dimensionality reduction methods to visualize in 2- and 3-dimensions the relationship among competing phylogenies obtained from gene partitions found in three mid- to large-size mitochondrial genome alignments. We test the performance of these dimensionality reduction methods by applying several goodness-of-fit measures. The intrinsic dimensionality of each data set is also estimated to determine whether projections in 2- and 3-dimensions can be expected to reveal meaningful relationships among trees from different data partitions. Several new approaches to aid in the comparison of different phylogenetic landscapes are presented. Curvilinear Components Analysis (CCA) and a stochastic gradient decent (SGD) optimization method give the best representation of the original tree-to-tree distance matrix for each of the three- mitochondrial genome alignments and greatly outperformed the method currently used to visualize tree landscapes. The CCA + SGD method converged at least as fast as previously applied methods for visualizing tree landscapes. We demonstrate for all three mtDNA alignments that 3D

A practical method for accurate quantification of large fault trees

International Nuclear Information System (INIS)

Choi, Jong Soo; Cho, Nam Zin

2007-01-01

This paper describes a practical method to accurately quantify top event probability and importance measures from incomplete minimal cut sets (MCS) of a large fault tree. The MCS-based fault tree method is extensively used in probabilistic safety assessments. Several sources of uncertainties exist in MCS-based fault tree analysis. The paper is focused on quantification of the following two sources of uncertainties: (1) the truncation neglecting low-probability cut sets and (2) the approximation in quantifying MCSs. The method proposed in this paper is based on a Monte Carlo simulation technique to estimate probability of the discarded MCSs and the sum of disjoint products (SDP) approach complemented by the correction factor approach (CFA). The method provides capability to accurately quantify the two uncertainties and estimate the top event probability and importance measures of large coherent fault trees. The proposed fault tree quantification method has been implemented in the CUTREE code package and is tested on the two example fault trees