A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

Science.gov (United States)

Huang, C.; Townshend, J.R.G.

2003-01-01

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

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

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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%,…

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

Science.gov (United States)

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

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

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.

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.

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

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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...

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

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Stefanie M. Herrmann

2013-10-01

Full Text Available Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1 a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview as the dependent variable, and (2 ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS Normalized Difference Vegetation Index (NDVI composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

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

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

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Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic

Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model.

Science.gov (United States)

Suchetana, Bihu; Rajagopalan, Balaji; Silverstein, JoAnn

2017-11-15

A regression tree-based diagnostic approach is developed to evaluate factors affecting US wastewater treatment plant compliance with ammonia discharge permit limits using Discharge Monthly Report (DMR) data from a sample of 106 municipal treatment plants for the period of 2004-2008. Predictor variables used to fit the regression tree are selected using random forests, and consist of the previous month's effluent ammonia, influent flow rates and plant capacity utilization. The tree models are first used to evaluate compliance with existing ammonia discharge standards at each facility and then applied assuming more stringent discharge limits, under consideration in many states. The model predicts that the ability to meet both current and future limits depends primarily on the previous month's treatment performance. With more stringent discharge limits predicted ammonia concentration relative to the discharge limit, increases. In-sample validation shows that the regression trees can provide a median classification accuracy of >70%. The regression tree model is validated using ammonia discharge data from an operating wastewater treatment plant and is able to accurately predict the observed ammonia discharge category approximately 80% of the time, indicating that the regression tree model can be applied to predict compliance for individual treatment plants providing practical guidance for utilities and regulators with an interest in controlling ammonia discharges. The proposed methodology is also used to demonstrate how to delineate reliable sources of demand and supply in a point source-to-point source nutrient credit trading scheme, as well as how planners and decision makers can set reasonable discharge limits in future. Copyright © 2017 Elsevier B.V. All rights reserved.

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

Science.gov (United States)

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

Science.gov (United States)

Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

2008-02-18

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

Directory of Open Access Journals (Sweden)

Suduan Chen

2014-01-01

Full Text Available As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

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...

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.

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

Science.gov (United States)

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...

Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

Science.gov (United States)

Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

2017-01-01

Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

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...

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

Full Text Available Altitude exposure increases the body’s need for iron (Gassmann and Muckenthaler, 2015, primarily to support accelerated erythropoiesis, yet clear supplementation guidelines do not exist. Athletes are typically recommended to ingest a daily oral iron supplement to facilitate altitude adaptations, and to help maintain iron balance. However, there is some debate as to whether athletes with otherwise healthy iron stores should be supplemented, due in part to concerns of iron overload. Excess iron in vital organs is associated with an increased risk of a number of conditions including cancer, liver disease and heart failure. Therefore clear guidelines are warranted and athletes should be discouraged from ‘self-prescribing” supplementation without medical advice. In the absence of prospective-controlled studies, decision tree analysis can be used to describe a data set, with the resultant regression tree serving as guide for clinical decision making. Here, we present a regression tree in the context of iron supplementation during altitude exposure, to examine the association between pre-altitude ferritin (Ferritin-Pre and the haemoglobin mass (Hbmass response, based on daily iron supplement dose. De-identified ferritin and Hbmass data from 178 athletes engaged in altitude training were extracted from the Australian Institute of Sport (AIS database. Altitude exposure was predominantly achieved via normobaric Live high: Train low (n = 147 at a simulated altitude of 3000 m for 2 to 4 weeks. The remaining athletes engaged in natural altitude training at venues ranging from 1350 to 2800 m for 3-4 weeks. Thus, the “hypoxic dose” ranged from ~890 km.h to ~1400 km.h. Ethical approval was granted by the AIS Human Ethics Committee, and athletes provided written informed consent. An in depth description and traditional analysis of the complete data set is presented elsewhere (Govus et al., 2015. Iron supplementation was prescribed by a sports physician

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

Science.gov (United States)

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)....

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.

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...

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.

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.

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

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

Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

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Chen Xiaoyu

2007-12-01

Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

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....

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

Science.gov (United States)

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.

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

DIF Trees: Using Classification Trees to Detect Differential Item Functioning

Science.gov (United States)

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…

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

Science.gov (United States)

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

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....

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

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

Science.gov (United States)

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.

Introduction to fault tree analysis

International Nuclear Information System (INIS)

Barlow, R.E.; Lambert, H.E.

1975-01-01

An elementary, engineering oriented introduction to fault tree analysis is presented. The basic concepts, techniques and applications of fault tree analysis, FTA, are described. The two major steps of FTA are identified as (1) the construction of the fault tree and (2) its evaluation. The evaluation of the fault tree can be qualitative or quantitative depending upon the scope, extensiveness and use of the analysis. The advantages, limitations and usefulness of FTA are discussed

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.

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.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

Science.gov (United States)

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

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.

Value tree analysis

International Nuclear Information System (INIS)

Keeney, R.; Renn, O.; Winterfeldt, D. von; Kotte, U.

1985-01-01

What are the targets and criteria on which national energy policy should be based. What priorities should be set, and how can different social interests be matched. To answer these questions, a new instrument of decision theory is presented which has been applied with good results to controversial political issues in the USA. The new technique is known under the name of value tree analysis. Members of important West German organisations (BDI, VDI, RWE, the Catholic and Protestant Church, Deutscher Naturschutzring, and ecological research institutions) were asked about the goals of their organisations. These goals were then ordered systematically and arranged in a hierarchical tree structure. The value trees of different groups can be combined into a catalogue of social criteria of acceptability and policy assessment. The authors describe the philosophy and methodology of value tree analysis and give an outline of its application in the development of a socially acceptable energy policy. (orig.) [de

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

Science.gov (United States)

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.

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.

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.

Fault tree analysis

International Nuclear Information System (INIS)

1981-09-01

Suggestion are made concerning the method of the fault tree analysis, the use of certain symbols in the examination of system failures. This purpose of the fault free analysis is to find logical connections of component or subsystem failures leading to undesirable occurrances. The results of these examinations are part of the system assessment concerning operation and safety. The objectives of the analysis are: systematical identification of all possible failure combinations (causes) leading to a specific undesirable occurrance, finding of reliability parameters such as frequency of failure combinations, frequency of the undesirable occurrance or non-availability of the system when required. The fault tree analysis provides a near and reconstructable documentation of the examination. (orig./HP) [de

Using decision trees and their ensembles for analysis of NIR spectroscopic data

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey V.

and interpretation of the models. In this presentation, we are going to discuss an applicability of decision trees based methods (including gradient boosting) for solving classification and regression tasks with NIR spectra as predictors. We will cover such aspects as evaluation, optimization and validation......Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the kaggle.......com — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

Science.gov (United States)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

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

Science.gov (United States)

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).

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

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

Directory of Open Access Journals (Sweden)

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.

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

Human reliability analysis using event trees

International Nuclear Information System (INIS)

Heslinga, G.

1983-01-01

The shut-down procedure of a technologically complex installation as a nuclear power plant consists of a lot of human actions, some of which have to be performed several times. The procedure is regarded as a chain of modules of specific actions, some of which are analyzed separately. The analysis is carried out by making a Human Reliability Analysis event tree (HRA event tree) of each action, breaking down each action into small elementary steps. The application of event trees in human reliability analysis implies more difficulties than in the case of technical systems where event trees were mainly used until now. The most important reason is that the operator is able to recover a wrong performance; memory influences play a significant role. In this study these difficulties are dealt with theoretically. The following conclusions can be drawn: (1) in principle event trees may be used in human reliability analysis; (2) although in practice the operator will recover his fault partly, theoretically this can be described as starting the whole event tree again; (3) compact formulas have been derived, by which the probability of reaching a specific failure consequence on passing through the HRA event tree after several times of recovery is to be calculated. (orig.)

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.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

Science.gov (United States)

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

Directory of Open Access Journals (Sweden)

Yoonseok Shin

2015-01-01

Full Text Available Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Fault tree analysis: concepts and techniques

International Nuclear Information System (INIS)

Fussell, J.B.

1976-01-01

Concepts and techniques of fault tree analysis have been developed over the past decade and now predictions from this type analysis are important considerations in the design of many systems such as aircraft, ships and their electronic systems, missiles, and nuclear reactor systems. Routine, hardware-oriented fault tree construction can be automated; however, considerable effort is needed in this area to get the methodology into production status. When this status is achieved, the entire analysis of hardware systems will be automated except for the system definition step. Automated analysis is not undesirable; to the contrary, when verified on adequately complex systems, automated analysis could well become a routine analysis. It could also provide an excellent start for a more in-depth fault tree analysis that includes environmental effects, common mode failure, and human errors. The automated analysis is extremely fast and frees the analyst from the routine hardware-oriented fault tree construction, as well as eliminates logic errors and errors of oversight in this part of the analysis. Automated analysis then affords the analyst a powerful tool to allow his prime efforts to be devoted to unearthing more subtle aspects of the modes of failure of the system

[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.

Human action analysis with randomized trees

CERN Document Server

Yu, Gang; Liu, Zicheng

2014-01-01

This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction.

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....

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

Multi-level tree analysis of pulmonary artery/vein trees in non-contrast CT images

Science.gov (United States)

Gao, Zhiyun; Grout, Randall W.; Hoffman, Eric A.; Saha, Punam K.

2012-02-01

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

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.

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Identifying the critical success factors in the coverage of low vision services using the classification analysis and regression tree methodology.

Science.gov (United States)

Chiang, Peggy Pei-Chia; Xie, Jing; Keeffe, Jill Elizabeth

2011-04-25

To identify the critical success factors (CSF) associated with coverage of low vision services. Data were collected from a survey distributed to Vision 2020 contacts, government, and non-government organizations (NGOs) in 195 countries. The Classification and Regression Tree Analysis (CART) was used to identify the critical success factors of low vision service coverage. Independent variables were sourced from the survey: policies, epidemiology, provision of services, equipment and infrastructure, barriers to services, human resources, and monitoring and evaluation. Socioeconomic and demographic independent variables: health expenditure, population statistics, development status, and human resources in general, were sourced from the World Health Organization (WHO), World Bank, and the United Nations (UN). The findings identified that having >50% of children obtaining devices when prescribed (χ(2) = 44; P 3 rehabilitation workers per 10 million of population (χ(2) = 4.50; P = 0.034), higher percentage of population urbanized (χ(2) = 14.54; P = 0.002), a level of private investment (χ(2) = 14.55; P = 0.015), and being fully funded by government (χ(2) = 6.02; P = 0.014), are critical success factors associated with coverage of low vision services. This study identified the most important predictors for countries with better low vision coverage. The CART is a useful and suitable methodology in survey research and is a novel way to simplify a complex global public health issue in eye care.

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

Directory of Open Access Journals (Sweden)

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.

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

Regression analysis with categorized regression calibrated exposure: some interesting findings

Directory of Open Access Journals (Sweden)

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

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.

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.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

Science.gov (United States)

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

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.

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models.

Science.gov (United States)

Chen, Wei; Li, Hui; Hou, Enke; Wang, Shengquan; Wang, Guirong; Panahi, Mahdi; Li, Tao; Peng, Tao; Guo, Chen; Niu, Chao; Xiao, Lele; Wang, Jiale; Xie, Xiaoshen; Ahmad, Baharin Bin

2018-09-01

The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers. Copyright © 2018 Elsevier B.V. All rights reserved.

Fault tree analysis of a research reactor

International Nuclear Information System (INIS)

Hall, J.A.; O'Dacre, D.F.; Chenier, R.J.; Arbique, G.M.

1986-08-01

Fault Tree Analysis Techniques have been used to assess the safety system of the ZED-2 Research Reactor at the Chalk River Nuclear Laboratories. This turned out to be a strong test of the techniques involved. The resulting fault tree was large and because of inter-links in the system structure the tree was not modularized. In addition, comprehensive documentation was required. After a brief overview of the reactor and the analysis, this paper concentrates on the computer tools that made the job work. Two types of tools were needed; text editing and forms management capability for large volumes of component and system data, and the fault tree codes themselves. The solutions (and failures) are discussed along with the tools we are already developing for the next analysis

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

Science.gov (United States)

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

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

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

Science.gov (United States)

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...

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 (...

Failure diagnosis and fault tree analysis

International Nuclear Information System (INIS)

Weber, G.

1982-07-01

In this report a methodology of failure diagnosis for complex systems is presented. Systems which can be represented by fault trees are considered. This methodology is based on switching algebra, failure diagnosis of digital circuits and fault tree analysis. Relations between these disciplines are shown. These relations are due to Boolean algebra and Boolean functions used throughout. It will be shown on this basis that techniques of failure diagnosis and fault tree analysis are useful to solve the following problems: 1. describe an efficient search of all failed components if the system is failed. 2. Describe an efficient search of all states which are close to a system failure if the system is still operating. The first technique will improve the availability, the second the reliability and safety. For these problems, the relation to methods of failure diagnosis for combinational circuits is required. Moreover, the techniques are demonstrated for a number of systems which can be represented by fault trees. (orig./RW) [de

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Regression Analysis and the Sociological Imagination

Science.gov (United States)

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Computer-aided Fault Tree Analysis

International Nuclear Information System (INIS)

Willie, R.R.

1978-08-01

A computer-oriented methodology for deriving minimal cut and path set families associated with arbitrary fault trees is discussed first. Then the use of the Fault Tree Analysis Program (FTAP), an extensive FORTRAN computer package that implements the methodology is described. An input fault tree to FTAP may specify the system state as any logical function of subsystem or component state variables or complements of these variables. When fault tree logical relations involve complements of state variables, the analyst may instruct FTAP to produce a family of prime implicants, a generalization of the minimal cut set concept. FTAP can also identify certain subsystems associated with the tree as system modules and provide a collection of minimal cut set families that essentially expresses the state of the system as a function of these module state variables. Another FTAP feature allows a subfamily to be obtained when the family of minimal cut sets or prime implicants is too large to be found in its entirety; this subfamily consists only of sets that are interesting to the analyst in a special sense

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...

Posbist fault tree analysis of coherent systems

International Nuclear Information System (INIS)

Huang, H.-Z.; Tong Xin; Zuo, Ming J.

2004-01-01

When the failure probability of a system is extremely small or necessary statistical data from the system is scarce, it is very difficult or impossible to evaluate its reliability and safety with conventional fault tree analysis (FTA) techniques. New techniques are needed to predict and diagnose such a system's failures and evaluate its reliability and safety. In this paper, we first provide a concise overview of FTA. Then, based on the posbist reliability theory, event failure behavior is characterized in the context of possibility measures and the structure function of the posbist fault tree of a coherent system is defined. In addition, we define the AND operator and the OR operator based on the minimal cut of a posbist fault tree. Finally, a model of posbist fault tree analysis (posbist FTA) of coherent systems is presented. The use of the model for quantitative analysis is demonstrated with a real-life safety system

Preliminary hazard analysis using sequence tree method

International Nuclear Information System (INIS)

Huang Huiwen; Shih Chunkuan; Hung Hungchih; Chen Minghuei; Yih Swu; Lin Jiinming

2007-01-01

A system level PHA using sequence tree method was developed to perform Safety Related digital I and C system SSA. The conventional PHA is a brainstorming session among experts on various portions of the system to identify hazards through discussions. However, this conventional PHA is not a systematic technique, the analysis results strongly depend on the experts' subjective opinions. The analysis quality cannot be appropriately controlled. Thereby, this research developed a system level sequence tree based PHA, which can clarify the relationship among the major digital I and C systems. Two major phases are included in this sequence tree based technique. The first phase uses a table to analyze each event in SAR Chapter 15 for a specific safety related I and C system, such as RPS. The second phase uses sequence tree to recognize what I and C systems are involved in the event, how the safety related systems work, and how the backup systems can be activated to mitigate the consequence if the primary safety systems fail. In the sequence tree, the defense-in-depth echelons, including Control echelon, Reactor trip echelon, ESFAS echelon, and Indication and display echelon, are arranged to construct the sequence tree structure. All the related I and C systems, include digital system and the analog back-up systems are allocated in their specific echelon. By this system centric sequence tree based analysis, not only preliminary hazard can be identified systematically, the vulnerability of the nuclear power plant can also be recognized. Therefore, an effective simplified D3 evaluation can be performed as well. (author)

Analysis of large fault trees based on functional decomposition

International Nuclear Information System (INIS)

Contini, Sergio; Matuzas, Vaidas

2011-01-01

With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

Analysis of large fault trees based on functional decomposition

Energy Technology Data Exchange (ETDEWEB)

Contini, Sergio, E-mail: sergio.contini@jrc.i [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy); Matuzas, Vaidas [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy)

2011-03-15

With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

Polylinear regression analysis in radiochemistry

International Nuclear Information System (INIS)

Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

1995-01-01

A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

Commercial application of fault tree analysis

International Nuclear Information System (INIS)

Crosetti, P.A.; Bruce, R.A.

1970-01-01

The potential for general application of Fault Tree Analysis to commercial products appears attractive based not only on the successful extension from the aerospace safety technology to the nuclear reactor reliability and availability technology, but also because combinatorial hazards are common to commercial operations and therefore lend themselves readily to evaluation by Fault Tree Analysis. It appears reasonable to conclude that the technique has application within the commercial industrial community where the occurrence of a specified consequence or final event would be of sufficient concern to management to justify such a rigorous analysis as an aid to decision making. (U.S.)

Guideliness for system modeling: fault tree [analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, Yoon Hwan; Yang, Joon Eon; Kang, Dae Il; Hwang, Mee Jeong

2004-07-01

This document, the guidelines for system modeling related to Fault Tree Analysis(FTA), is intended to provide the guidelines with the analyzer to construct the fault trees in the level of the capability category II of ASME PRA standard. Especially, they are to provide the essential and basic guidelines and the related contents to be used in support of revising the Ulchin 3 and 4 PSA model for risk monitor within the capability category II of ASME PRA standard. Normally the main objective of system analysis is to assess the reliability of system modeled by Event Tree Analysis (ETA). A variety of analytical techniques can be used for the system analysis, however, FTA method is used in this procedures guide. FTA is the method used for representing the failure logic of plant systems deductively using AND, OR or NOT gates. The fault tree should reflect all possible failure modes that may contribute to the system unavailability. This should include contributions due to the mechanical failures of the components, Common Cause Failures (CCFs), human errors and outages for testing and maintenance. This document identifies and describes the definitions and the general procedures of FTA and the essential and basic guidelines for reving the fault trees. Accordingly, the guidelines for FTA will be capable to guide the FTA to the level of the capability category II of ASME PRA standard.

Guideliness for system modeling: fault tree [analysis

International Nuclear Information System (INIS)

Lee, Yoon Hwan; Yang, Joon Eon; Kang, Dae Il; Hwang, Mee Jeong

2004-07-01

This document, the guidelines for system modeling related to Fault Tree Analysis(FTA), is intended to provide the guidelines with the analyzer to construct the fault trees in the level of the capability category II of ASME PRA standard. Especially, they are to provide the essential and basic guidelines and the related contents to be used in support of revising the Ulchin 3 and 4 PSA model for risk monitor within the capability category II of ASME PRA standard. Normally the main objective of system analysis is to assess the reliability of system modeled by Event Tree Analysis (ETA). A variety of analytical techniques can be used for the system analysis, however, FTA method is used in this procedures guide. FTA is the method used for representing the failure logic of plant systems deductively using AND, OR or NOT gates. The fault tree should reflect all possible failure modes that may contribute to the system unavailability. This should include contributions due to the mechanical failures of the components, Common Cause Failures (CCFs), human errors and outages for testing and maintenance. This document identifies and describes the definitions and the general procedures of FTA and the essential and basic guidelines for reving the fault trees. Accordingly, the guidelines for FTA will be capable to guide the FTA to the level of the capability category II of ASME PRA standard

LOSP-initiated event tree analysis for BWR

International Nuclear Information System (INIS)

Watanabe, Norio; Kondo, Masaaki; Uno, Kiyotaka; Chigusa, Takeshi; Harami, Taikan

1989-03-01

As a preliminary study of 'Japanese Model Plant PSA', a LOSP (loss of off-site power)-initiated Event Tree Analysis for a Japanese typical BWR was carried out solely based on the open documents such as 'Safety Analysis Report'. The objectives of this analysis are as follows; - to delineate core-melt accident sequences initiated by LOSP, - to evaluate the importance of core-melt accident sequences in terms of occurrence frequency, and - to develop a foundation of plant information and analytical procedures for efficiently performing further 'Japanese Model Plant PSA'. This report describes the procedure and results of the LOSP-initiated Event Tree Analysis. In this analysis, two types of event trees, Functional Event Tree and Systemic Event Tree, were developed to delineate core-melt accident sequences and to quantify their frequencies. Front-line System Event Tree was prepared as well to provide core-melt sequence delineation for accident progression analysis of Level 2 PSA which will be followed in a future. Applying U.S. operational experience data such as component failure rates and a LOSP frequency, we obtained the following results; - The total frequency of core-melt accident sequences initiated by LOSP is estimated at 5 x 10 -4 per reactor-year. - The dominant sequences are 'Loss of Decay Heat Removal' and 'Loss of Emergency Electric Power Supply', which account for more than 90% of the total core-melt frequency. In this analysis, a higher value of 0.13/R·Y was used for the LOSP frequency than experiences in Japan and any recovery action was not considered. In fact, however, there has been no experience of LOSP event in Japanese nuclear power plants so far and it is also expected that offsite power and/or PCS would be recovered before core melt. Considering Japanese operating experience and recovery factors will reduce the total core-melt frequency to less than 10 -6 per reactor-year. (J.P.N.)

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

Science.gov (United States)

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.

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

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.

Methods of fault tree analysis and their limits

International Nuclear Information System (INIS)

Weber, G.G.

1984-12-01

Some recent methodological developments of fault tree analysis are discussed and limits of fault tree analysis and a criterion for admissibility of structure functions are given. It is shown that there are interesting relations to switching theory and to stochastic processes. (orig./HP) [de

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

Directory of Open Access Journals (Sweden)

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.

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

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

Directory of Open Access Journals (Sweden)

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.

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

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART

Directory of Open Access Journals (Sweden)

José A. Delgado

2012-01-01

Full Text Available Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR imagery and related techniques to model structural parameters at the stand level (n = 490 in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively served to model structural parameters. Classification and Regression Tree Analysis (CART was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD (R2 = 0.8; RMSE = 0.13 m with an average error of 17% while basal area (BA models produced an average error of 22% (RMSE = 5.79 m2/ha. When the measured number of trees per unit area (N was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from

Computer-aided event tree analysis by the impact vector method

International Nuclear Information System (INIS)

Lima, J.E.P.

1984-01-01

In the development of the Probabilistic Risk Analysis of Angra I, the ' large event tree/small fault tree' approach was adopted for the analysis of the plant behavior in an emergency situation. In this work, the event tree methodology is presented along with the adaptations which had to be made in order to attain a correct description of the safety system performances according to the selected analysis method. The problems appearing in the application of the methodology and their respective solutions are presented and discussed, with special emphasis to the impact vector technique. A description of the ETAP code ('Event Tree Analysis Program') developed for constructing and quantifying event trees is also given in this work. A preliminary version of the small-break LOCA analysis for Angra 1 is presented as an example of application of the methodology and of the code. It is shown that the use of the ETAP code sigmnificantly contributes to decreasing the time spent in event tree analyses, making it viable the practical application of the analysis approach referred above. (author) [pt

Preface to Berk's "Regression Analysis: A Constructive Critique"

OpenAIRE

de Leeuw, Jan

2003-01-01

It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

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.

Hierarchical regression analysis in structural Equation Modeling

NARCIS (Netherlands)

de Jong, P.F.

1999-01-01

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main

Multivariate Regression Analysis and Slaughter Livestock,

Science.gov (United States)

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

[The Application of the Fault Tree Analysis Method in Medical Equipment Maintenance].

Science.gov (United States)

Liu, Hongbin

2015-11-01

In this paper, the traditional fault tree analysis method is presented, detailed instructions for its application characteristics in medical instrument maintenance is made. It is made significant changes when the traditional fault tree analysis method is introduced into the medical instrument maintenance: gave up the logic symbolic, logic analysis and calculation, gave up its complicated programs, and only keep its image and practical fault tree diagram, and the fault tree diagram there are also differences: the fault tree is no longer a logical tree but the thinking tree in troubleshooting, the definition of the fault tree's nodes is different, the composition of the fault tree's branches is also different.

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.

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

Science.gov (United States)

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.

A fault tree analysis strategy using binary decision diagrams

International Nuclear Information System (INIS)

Reay, Karen A.; Andrews, John D.

2002-01-01

The use of binary decision diagrams (BDDs) in fault tree analysis provides both an accurate and efficient means of analysing a system. There is a problem, however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an analysis strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of 'minimal' subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events

Generalized fault tree analysis combined with state analysis

International Nuclear Information System (INIS)

Caldarola, L.

1980-02-01

An analytical theory has been developed which allows one to calculate the occurrence probability of the top event of a fault tree with multistate (two or more than two states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of boolean algebra is required. This is called 'boolean algebra with restrictions on variables' and its basic rules are the same as those of the traditional boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. It is also shown that the boolean algebra with restrictions on variables facilitates the task of formally combining fault tree analysis with state analysis. The computer program MUSTAFA 1 based on the above theory has been developed. It can analyse fault trees of system containing statistically independent as well as dependent components with two or more than two states. MUSTAFA 1 can handle coherent as well as non coherent boolean functions. (orig.) 891 HP/orig. 892 MB [de

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

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

Science.gov (United States)

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

Probabilistic risk assessment course documentation. Volume 4. System reliability and analysis techniques sessions B/C - event trees/fault trees

International Nuclear Information System (INIS)

Haasl, D.; Young, J.

1985-08-01

This course will employ a combination of lecture material and practical problem solving in order to develop competence and understanding of th principles and techniques of event tree and fault tree analysis. The role of these techniques in the overall context of PRA will be described. The emphasis of this course will be on the basic, traditional methods of event tree and fault tree analysis

Bayesian logistic regression analysis

NARCIS (Netherlands)

Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

2012-01-01

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

Application Research of Fault Tree Analysis in Grid Communication System Corrective Maintenance

Science.gov (United States)

Wang, Jian; Yang, Zhenwei; Kang, Mei

2018-01-01

This paper attempts to apply the fault tree analysis method to the corrective maintenance field of grid communication system. Through the establishment of the fault tree model of typical system and the engineering experience, the fault tree analysis theory is used to analyze the fault tree model, which contains the field of structural function, probability importance and so on. The results show that the fault tree analysis can realize fast positioning and well repairing of the system. Meanwhile, it finds that the analysis method of fault tree has some guiding significance to the reliability researching and upgrading f the system.

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...

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

Science.gov (United States)

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

A compendium of computer codes in fault tree analysis

International Nuclear Information System (INIS)

Lydell, B.

1981-03-01

In the past ten years principles and methods for a unified system reliability and safety analysis have been developed. Fault tree techniques serve as a central feature of unified system analysis, and there exists a specific discipline within system reliability concerned with the theoretical aspects of fault tree evaluation. Ever since the fault tree concept was established, computer codes have been developed for qualitative and quantitative analyses. In particular the presentation of the kinetic tree theory and the PREP-KITT code package has influenced the present use of fault trees and the development of new computer codes. This report is a compilation of some of the better known fault tree codes in use in system reliability. Numerous codes are available and new codes are continuously being developed. The report is designed to address the specific characteristics of each code listed. A review of the theoretical aspects of fault tree evaluation is presented in an introductory chapter, the purpose of which is to give a framework for the validity of the different codes. (Auth.)

[RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree].

Science.gov (United States)

Du, Hua Qiang; Sun, Xiao Yan; Han, Ning; Mao, Fang Jie

2017-10-01

By synergistically using the object-based image analysis (OBIA) and the classification and regression tree (CART) methods, the distribution information, the indexes (including diameter at breast, tree height, and crown closure), and the aboveground carbon storage (AGC) of moso bamboo forest in Shanchuan Town, Anji County, Zhejiang Province were investigated. The results showed that the moso bamboo forest could be accurately delineated by integrating the multi-scale ima ge segmentation in OBIA technique and CART, which connected the image objects at various scales, with a pretty good producer's accuracy of 89.1%. The investigation of indexes estimated by regression tree model that was constructed based on the features extracted from the image objects reached normal or better accuracy, in which the crown closure model archived the best estimating accuracy of 67.9%. The estimating accuracy of diameter at breast and tree height was relatively low, which was consistent with conclusion that estimating diameter at breast and tree height using optical remote sensing could not achieve satisfactory results. Estimation of AGC reached relatively high accuracy, and accuracy of the region of high value achieved above 80%.

A framework for sensitivity analysis of decision trees.

Science.gov (United States)

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

Directory of Open Access Journals (Sweden)

Yu-sheng Cheng

2014-01-01

Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

Common pitfalls in statistical analysis: Linear regression analysis

Directory of Open Access Journals (Sweden)

Rakesh Aggarwal

2017-01-01

Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Fault tree technique: advances in probabilistic and logical analysis

International Nuclear Information System (INIS)

Clarotti, C.A.; Amendola, A.; Contini, S.; Squellati, G.

1982-01-01

Fault tree reliability analysis is used for assessing the risk associated to systems of increasing complexity (phased mission systems, systems with multistate components, systems with non-monotonic structure functions). Much care must be taken to make sure that fault tree technique is not used beyond its correct validity range. To this end a critical review of mathematical foundations of reliability fault tree analysis is carried out. Limitations are enlightened and potential solutions to open problems are suggested. Moreover an overview is given on the most recent developments in the implementation of an integrated software (SALP-MP, SALP-NOT, SALP-CAFT Codes) for the analysis of a wide class of systems

Modular representation and analysis of fault trees

Energy Technology Data Exchange (ETDEWEB)

Olmos, J; Wolf, L [Massachusetts Inst. of Tech., Cambridge (USA). Dept. of Nuclear Engineering

1978-08-01

An analytical method to describe fault tree diagrams in terms of their modular compositions is developed. Fault tree structures are characterized by recursively relating the top tree event to all its basic component inputs through a set of equations defining each of the modulus for the fault tree. It is shown that such a modular description is an extremely valuable tool for making a quantitative analysis of fault trees. The modularization methodology has been implemented into the PL-MOD computer code, written in PL/1 language, which is capable of modularizing fault trees containing replicated components and replicated modular gates. PL-MOD in addition can handle mutually exclusive inputs and explicit higher order symmetric (k-out-of-n) gates. The step-by-step modularization of fault trees performed by PL-MOD is demonstrated and it is shown how this procedure is only made possible through an extensive use of the list processing tools available in PL/1. A number of nuclear reactor safety system fault trees were analyzed. PL-MOD performed the modularization and evaluation of the modular occurrence probabilities and Vesely-Fussell importance measures for these systems very efficiently. In particular its execution time for the modularization of a PWR High Pressure Injection System reduced fault tree was 25 times faster than that necessary to generate its equivalent minimal cut-set description using MOCUS, a code considered to be fast by present standards.

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

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.

Identifying changes in dissolved organic matter content and characteristics by fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis during wastewater treatment.

Science.gov (United States)

Yu, Huibin; Song, Yonghui; Liu, Ruixia; Pan, Hongwei; Xiang, Liancheng; Qian, Feng

2014-10-01

The stabilization of latent tracers of dissolved organic matter (DOM) of wastewater was analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis (CART) in wastewater treatment performance. DOM of water samples collected from primary sedimentation, anaerobic, anoxic, oxic and secondary sedimentation tanks in a large-scale wastewater treatment plant contained four fluorescence components: tryptophan-like (C1), tyrosine-like (C2), microbial humic-like (C3) and fulvic-like (C4) materials extracted by self-organizing map. These components showed good positive linear correlations with dissolved organic carbon of DOM. C1 and C2 were representative components in the wastewater, and they were removed to a higher extent than those of C3 and C4 in the treatment process. C2 was a latent parameter determined by CART to differentiate water samples of oxic and secondary sedimentation tanks from the successive treatment units, indirectly proving that most of tyrosine-like material was degraded by anaerobic microorganisms. C1 was an accurate parameter to comprehensively separate the samples of the five treatment units from each other, indirectly indicating that tryptophan-like material was decomposed by anaerobic and aerobic bacteria. EEM fluorescence spectroscopy in combination with self-organizing map and CART analysis can be a nondestructive effective method for characterizing structural component of DOM fractions and monitoring organic matter removal in wastewater treatment process. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regression Association Analysis of Yield-Related Traits with RAPD Molecular Markers in Pistachio (Pistacia vera L.

Directory of Open Access Journals (Sweden)

Saeid Mirzaei

2017-10-01

Full Text Available Introduction: The pistachio (Pistacia vera, a member of the cashew family, is a small tree originating from Central Asia and the Middle East. The tree produces seeds that are widely consumed as food. Pistacia vera often is confused with other species in the genus Pistacia that are also known as pistachio. These other species can be distinguished by their geographic distributions and their seeds which are much smaller and have a soft shell. Continual advances in crop improvement through plant breeding are driven by the available genetic diversity. Therefore, the recognition and measurement of such diversity is crucial to breeding programs. In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL identification and marker assisted selection (MAS. The germplasm-regression-combined association studies not only allow mapping of genes/QTLs with higher level of confidence, but also allow detection of genes/QTLs, which will otherwise escape detection in linkage-based QTL studies based on the planned populations. The development of the marker-based technology offers a fast, reliable, and easy way to perform multiple regression analysis and comprise an alternative approach to breeding in diverse species of plants. The availability of many makers and morphological traits can help to regression analysis between these markers and morphological traits. Materials and Methods: In this study, 20 genotypes of Pistachio were studied and yield related traits were measured. Young well-expanded leaves were collected for DNA extraction and total genomic DNA was extracted. Genotyping was performed using 15 RAPD primers and PCR amplification products were visualized by gel electrophoresis. The reproducible RAPD fragments were scored on the basis of present (1 or absent (0 bands and a binary matrix constructed using each molecular marker. Association analysis between

Fault tree analysis. Implementation of the WAM-codes

International Nuclear Information System (INIS)

Bento, J.P.; Poern, K.

1979-07-01

The report describes work going on at Studsvik at the implementation of the WAM code package for fault tree analysis. These codes originally developed under EPRI contract by Sciences Applications Inc, allow, in contrast with other fault tree codes, all Boolean operations, thus allowing modeling of ''NOT'' conditions and dependent components. To concretize the implementation of these codes, the auxiliary feed-water system of the Swedish BWR Oskarshamn 2 was chosen for the reliability analysis. For this system, both the mean unavailability and the probability density function of the top event - undesired event - of the system fault tree were calculated, the latter using a Monte-Carlo simulation technique. The present study is the first part of a work performed under contract with the Swedish Nuclear Power Inspectorate. (author)

Fault tree analysis for reactor systems

International Nuclear Information System (INIS)

Crosetti, P.A.

1971-01-01

Reliability analysis is playing an increasingly important role in quantitative assessment of system performance for assuring nuclear safety, improving plant performance and plant life, and reducing plant operating costs. The complexity of today's nuclear plants warrant the use of techniques which will provide a comprehensive evaluation of systems in their total context. In particular, fault tree analysis with probability evaluation can play a key role in assuring nuclear safety, in improving plant performance and plant life, and in reducing plant operating costs. The technique provides an all inclusive, versatile mathematical tool for analyzing complex systems. Its application can include a complete plant as well as any of the systems and subsystems. Fault tree analysis provides an objective basis for analyzing system design, performing trade-off studies, analyzing common mode failures, demonstrating compliance with AEC requirements, and justifying system changes or additions. The logic of the approach makes it readily understandable and, therefore, it serves as an effective visibility tool for both engineering and management. (U.S.)

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.

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,...

A comparison between fault tree analysis and reliability graph with general gates

International Nuclear Information System (INIS)

Kim, Man Cheol; Seong, Poong Hyun; Jung, Woo Sik

2004-01-01

Currently, level-1 probabilistic safety assessment (PSA) is performed on the basis of event tree analysis and fault tree analysis. Kim and Seong developed a new method for system reliability analysis named reliability graph with general gates (RGGG). The RGGG is an extension of conventional reliability graph, and it utilizes the transformation of system structures to equivalent Bayesian networks for quantitative calculation. The RGGG is considered to be intuitive and easy-to-use while as powerful as fault tree analysis. As an example, Kim and Seong already showed that the Bayesian network model for digital plant protection system (DPPS), which is transformed from the RGGG model for DPPS, can be shown in 1 page, while the fault tree model for DPPS consists of 64 pages of fault trees. Kim and Seong also insisted that Bayesian network model for DPPS is more intuitive because the one-to-one matching between each node in the Bayesian network model and an actual component of DPPS is possible. In this paper, we are going to give a comparison between fault tree analysis and the RGGG method with two example systems. The two example systems are the recirculation of in Korean standard nuclear power plants (KSNP) and the fault tree model developed by Rauzy

Fault tree synthesis for software design analysis of PLC based safety-critical systems

International Nuclear Information System (INIS)

Koo, S. R.; Cho, C. H.; Seong, P. H.

2006-01-01

As a software verification and validation should be performed for the development of PLC based safety-critical systems, a software safety analysis is also considered in line with entire software life cycle. In this paper, we propose a technique of software safety analysis in the design phase. Among various software hazard analysis techniques, fault tree analysis is most widely used for the safety analysis of nuclear power plant systems. Fault tree analysis also has the most intuitive notation and makes both qualitative and quantitative analyses possible. To analyze the design phase more effectively, we propose a technique of fault tree synthesis, along with a universal fault tree template for the architecture modules of nuclear software. Consequently, we can analyze the safety of software on the basis of fault tree synthesis. (authors)

Relating tree growth to rainfall in Bolivian rain forests: a test for six species using tree ring analysis.

Science.gov (United States)

Brienen, Roel J W; Zuidema, Pieter A

2005-11-01

Many tropical regions show one distinct dry season. Often, this seasonality induces cambial dormancy of trees, particularly if these belong to deciduous species. This will often lead to the formation of annual rings. The aim of this study was to determine whether tree species in the Bolivian Amazon region form annual rings and to study the influence of the total amount and seasonal distribution of rainfall on diameter growth. Ring widths were measured on stem discs of a total of 154 trees belonging to six rain forest species. By correlating ring width and monthly rainfall data we proved the annual character of the tree rings for four of our study species. For two other species the annual character was proved by counting rings on trees of known age and by radiocarbon dating. The results of the climate-growth analysis show a positive relationship between tree growth and rainfall in certain periods of the year, indicating that rainfall plays a major role in tree growth. Three species showed a strong relationship with rainfall at the beginning of the rainy season, while one species is most sensitive to the rainfall at the end of the previous growing season. These results clearly demonstrate that tree ring analysis can be successfully applied in the tropics and that it is a promising method for various research disciplines.

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Directory of Open Access Journals (Sweden)

Arachchige Surantha Ashan Salgadoe

2018-02-01

Full Text Available Phytophthora root rot (PRR infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of ‘Canny edge detection’ and ‘Otsu’s’ methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3 satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20–70% derived from RGB images were found to be significantly different for most disease rankings (p < 0.05 and correlated well (R2 = 0.89 with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI to be strongly correlated (R2 = 0.96 with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal.

Two Paradoxes in Linear Regression Analysis

Science.gov (United States)

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

System-level hazard analysis using the sequence-tree method

International Nuclear Information System (INIS)

Huang, H.-W.; Shih Chunkuan; Yih Swu; Chen, M.-H.

2008-01-01

A system-level PHA using the sequence-tree method is presented to perform safety-related digital I and C system SSA. The conventional PHA involves brainstorming among experts on various portions of the system to identify hazards through discussions. However, since the conventional PHA is not a systematic technique, the analysis results depend strongly on the experts' subjective opinions. The quality of analysis cannot be appropriately controlled. Therefore, this study presents a system-level sequence tree based PHA, which can clarify the relationship among the major digital I and C systems. This sequence-tree-based technique has two major phases. The first phase adopts a table to analyze each event in SAR Chapter 15 for a specific safety-related I and C system, such as RPS. The second phase adopts a sequence tree to recognize the I and C systems involved in the event, the working of the safety-related systems and how the backup systems can be activated to mitigate the consequence if the primary safety systems fail. The defense-in-depth echelons, namely the Control echelon, Reactor trip echelon, ESFAS echelon and Monitoring and indicator echelon, are arranged to build the sequence-tree structure. All the related I and C systems, including the digital systems and the analog back-up systems, are allocated in their specific echelons. This system-centric sequence-tree analysis not only systematically identifies preliminary hazards, but also vulnerabilities in a nuclear power plant. Hence, an effective simplified D3 evaluation can also be conducted

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.

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

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Science.gov (United States)

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Linear regression and sensitivity analysis in nuclear reactor design

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

2015-01-01

Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

Fault trees for decision making in systems analysis

International Nuclear Information System (INIS)

Lambert, H.E.

1975-01-01

The application of fault tree analysis (FTA) to system safety and reliability is presented within the framework of system safety analysis. The concepts and techniques involved in manual and automated fault tree construction are described and their differences noted. The theory of mathematical reliability pertinent to FTA is presented with emphasis on engineering applications. An outline of the quantitative reliability techniques of the Reactor Safety Study is given. Concepts of probabilistic importance are presented within the fault tree framework and applied to the areas of system design, diagnosis and simulation. The computer code IMPORTANCE ranks basic events and cut sets according to a sensitivity analysis. A useful feature of the IMPORTANCE code is that it can accept relative failure data as input. The output of the IMPORTANCE code can assist an analyst in finding weaknesses in system design and operation, suggest the most optimal course of system upgrade, and determine the optimal location of sensors within a system. A general simulation model of system failure in terms of fault tree logic is described. The model is intended for efficient diagnosis of the causes of system failure in the event of a system breakdown. It can also be used to assist an operator in making decisions under a time constraint regarding the future course of operations. The model is well suited for computer implementation. New results incorporated in the simulation model include an algorithm to generate repair checklists on the basis of fault tree logic and a one-step-ahead optimization procedure that minimizes the expected time to diagnose system failure. (80 figures, 20 tables)

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

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

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Multivariate analysis of flow cytometric data using decision trees.

Science.gov (United States)

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

Fault tree analysis of KNICS RPS software

International Nuclear Information System (INIS)

Park, Gee Yong; Kwon, Kee Choon; Koh, Kwang Yong; Jee, Eun Kyoung; Seong, Poong Hyun; Lee, Dae Hyung

2008-01-01

This paper describes the application of a software Fault Tree Analysis (FTA) as one of the analysis techniques for a Software Safety Analysis (SSA) at the design phase and its analysis results for the safety-critical software of a digital reactor protection system, which is called the KNICS RPS, being developed in the KNICS (Korea Nuclear Instrumentation and Control Systems) project. The software modules in the design description were represented by Function Blocks (FBs), and the software FTA was performed based on the well-defined fault tree templates for the FBs. The SSA, which is part of the verification and validation (V and V) activities, was activated at each phase of the software lifecycle for the KNICS RPS. At the design phase, the software HAZOP (Hazard and Operability) and the software FTA were employed in the SSA in such a way that the software HAZOP was performed first and then the software FTA was applied. The software FTA was applied to some critical modules selected from the software HAZOP analysis

Simulation Experiments in Practice: Statistical Design and Regression Analysis

OpenAIRE

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...

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.

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...

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

General Nature of Multicollinearity in Multiple Regression Analysis.

Science.gov (United States)

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery.

Science.gov (United States)

Montella, Alfonso; Aria, Massimo; D'Ambrosio, Antonio; Mauriello, Filomena

2012-11-01

Aim of the study was the analysis of powered two-wheeler (PTW) crashes in Italy in order to detect interdependence as well as dissimilarities among crash characteristics and provide insights for the development of safety improvement strategies focused on PTWs. At this aim, data mining techniques were used to analyze the data relative to the 254,575 crashes involving PTWs occurred in Italy in the period 2006-2008. Classification trees analysis and rules discovery were performed. Tree-based methods are non-linear and non-parametric data mining tools for supervised classification and regression problems. They do not require a priori probabilistic knowledge about the phenomena under studying and consider conditional interactions among input data. Rules discovery is the identification of sets of items (i.e., crash patterns) that occur together in a given event (i.e., a crash in our study) more often than they would if they were independent of each other. Thus, the method can detect interdependence among crash characteristics. Due to the large number of patterns considered, both methods suffer from an extreme risk of finding patterns that appear due to chance alone. To overcome this problem, in our study we randomly split the sample data in two data sets and used well-established statistical practices to evaluate the statistical significance of the results. Both the classification trees and the rules discovery were effective in providing meaningful insights about PTW crash characteristics and their interdependencies. Even though in several cases different crash characteristics were highlighted, the results of the two the analysis methods were never contradictory. Furthermore, most of the findings of this study were consistent with the results of previous studies which used different analytical techniques, such as probabilistic models of crash injury severity. Basing on the analysis results, engineering countermeasures and policy initiatives to reduce PTW injuries and

Utilization of fault tree analysis techniques in fire protection work

International Nuclear Information System (INIS)

Crass, E.R.

1986-01-01

This paper describes the development of a fault tree model for a typical pressurized water reactor (PWR), and the subsequent use of this model to perform a safe shutdown analysis and determine conformance with Section IIIG of 10 CFR 50, Appendix R. The paper describes the rationale for choosing this analytical tool, the development of the fault tree model, the analysis of the model using the PREP code, disposition of the results, and finally, application of the results to determine the need for plant modifications. It concludes with a review of the strengths and weaknesses of the use of Fault Tree Methodology for this application

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.

Tree height-diameter and yield functions for Gmelina arborea (roxb ...

African Journals Online (AJOL)

Data were analyzed using descriptive statistics and regression analysis. Linear, logarithmic, polynomial, power and exponential height-diameter and stem volume models were fitted to the dataset. The predictor was tree Dbh (cm). The developed models were assessed using coefficient of determination (R2) and root mean ...

TH-EF-BRC-03: Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Thomadsen, B. [University of Wisconsin (United States)

2016-06-15

This Hands-on Workshop will be focused on providing participants with experience with the principal tools of TG 100 and hence start to build both competence and confidence in the use of risk-based quality management techniques. The three principal tools forming the basis of TG 100’s risk analysis: Process mapping, Failure-Modes and Effects Analysis and fault-tree analysis will be introduced with a 5 minute refresher presentation and each presentation will be followed by a 30 minute small group exercise. An exercise on developing QM from the risk analysis follows. During the exercise periods, participants will apply the principles in 2 different clinical scenarios. At the conclusion of each exercise there will be ample time for participants to discuss with each other and the faculty their experience and any challenges encountered. Learning Objectives: To review the principles of Process Mapping, Failure Modes and Effects Analysis and Fault Tree Analysis. To gain familiarity with these three techniques in a small group setting. To share and discuss experiences with the three techniques with faculty and participants. Director, TreatSafely, LLC. Director, Center for the Assessment of Radiological Sciences. Occasional Consultant to the IAEA and Varian.

TH-EF-BRC-03: Fault Tree Analysis

International Nuclear Information System (INIS)

Thomadsen, B.

2016-01-01

This Hands-on Workshop will be focused on providing participants with experience with the principal tools of TG 100 and hence start to build both competence and confidence in the use of risk-based quality management techniques. The three principal tools forming the basis of TG 100’s risk analysis: Process mapping, Failure-Modes and Effects Analysis and fault-tree analysis will be introduced with a 5 minute refresher presentation and each presentation will be followed by a 30 minute small group exercise. An exercise on developing QM from the risk analysis follows. During the exercise periods, participants will apply the principles in 2 different clinical scenarios. At the conclusion of each exercise there will be ample time for participants to discuss with each other and the faculty their experience and any challenges encountered. Learning Objectives: To review the principles of Process Mapping, Failure Modes and Effects Analysis and Fault Tree Analysis. To gain familiarity with these three techniques in a small group setting. To share and discuss experiences with the three techniques with faculty and participants. Director, TreatSafely, LLC. Director, Center for the Assessment of Radiological Sciences. Occasional Consultant to the IAEA and Varian.

On logistic regression analysis of dichotomized responses.

Science.gov (United States)

Lu, Kaifeng

2017-01-01

We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Neutron activation analysis of absolutely-dated tree rings

International Nuclear Information System (INIS)

Uenlue, K.; Hauck, D.K.; Kuniholm, P.I.; Chiment, J.J.

2005-01-01

Gold concentration was determined for dendrochronologically-dated wood samples using neutron activation analysis (NAA) and correlation sought with known environmental changes, e.g., volcanic activities, during historic periods. Uptake of gold is sensitive to soil pH for many plants. Data presented are from a single, cross-dated tree that grew in Greece. Using NAA, gold was measured with parts-per-billion sensitivity in individual tree rings from 1411 to 1988 AD. (author)

NuFTA: A CASE Tool for Automatic Software Fault Tree Analysis

International Nuclear Information System (INIS)

Yun, Sang Hyun; Lee, Dong Ah; Yoo, Jun Beom

2010-01-01

Software fault tree analysis (SFTA) is widely used for analyzing software requiring high-reliability. In SFTA, experts predict failures of system through HA-ZOP (Hazard and Operability study) or FMEA (Failure Mode and Effects Analysis) and draw software fault trees about the failures. Quality and cost of the software fault tree, therefore, depend on knowledge and experience of the experts. This paper proposes a CASE tool NuFTA in order to assist experts of safety analysis. The NuFTA automatically generate software fault trees from NuSCR formal requirements specification. NuSCR is a formal specification language used for specifying software requirements of KNICS RPS (Reactor Protection System) in Korea. We used the SFTA templates proposed by in order to generate SFTA automatically. The NuFTA also generates logical formulae summarizing the failure's cause, and we have a plan to use the formulae usefully through formal verification techniques

Learning from examples - Generation and evaluation of decision trees for software resource analysis

Science.gov (United States)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

Fault tree analysis for urban flooding

NARCIS (Netherlands)

Ten Veldhuis, J.A.E.; Clemens, F.H.L.R.; Van Gelder, P.H.A.J.M.

2008-01-01

Traditional methods to evaluate flood risk mostly focus on storm events as the main cause of flooding. Fault tree analysis is a technique that is able to model all potential causes of flooding and to quantify both the overall probability of flooding and the contributions of all causes of flooding to

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

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

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.

Shedding light on tree growth : ring analysis of juvenile tropical trees

NARCIS (Netherlands)

Soliz Gamboa, C.C.

2010-01-01

In the understory of tropical forests light is believed to be the main limiting growth factor for the newly established trees. Trees growing in shade of the understory may experience periods of slow radial growth. It is expected that gaps created by tree or branch fall will provoke tree growth

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.

Elliptic Fourier analysis of crown shapes in Quercus petraea trees

Directory of Open Access Journals (Sweden)

Ovidiu Hâruţa

2011-02-01

Full Text Available Shape is a fundamental morphological descriptor, significant in taxonomic research as well as in ecomorphology, one method of estimation being from digitally processed images. In the present study, were analysed shapes of Q. petraea crowns, pertaining to five different stem diameter classes, from three similar stands. Based on measurements on terrestrial digital vertical photos, crown size analysis was performed and correlations between crown and stem variables were tested. Linear regression equations between crown volumes and dbh, and crown volumes and stem volumes were derived, explaining more than half of data variability. Employment of elliptic Fourier analysis (EFA, a powerful analysis tool, permitted the extraction of the mean shape from crowns, characterized by high morphological variability. The extracted, most important, coefficients were used to reconstruct the average shape of the crowns, using Inverse Fourier Transform. A mean shape of the crown, corresponding to stand conditions in which competition is added as influential shaping factor, aside genetic program of the species, is described for each stem diameter class. Crown regions with highest shape variability, from the perspective of stage developmentof the trees, were determined. Accordingly, the main crown shape characteristics are: crown elongation, mass center, asymmetry with regard to the main axis, lateral regions symmetrical and asymmetrical variations.

Elliptic Fourier analysis of crown shapes in Quercus petraea trees

Directory of Open Access Journals (Sweden)

Ovidiu Hâruţa

2011-06-01

Full Text Available Shape is a fundamental morphological descriptor, significant in taxonomic research as well as in ecomorphology, one method of estimation being from digitally processed images. In the present study, were analysed shapes of Q. petraea crowns, pertaining to five different stem diameter classes, from three similar stands. Based on measurements on terrestrial digital vertical photos, crown size analysis was performed and correlations between crown and stem variables were tested. Linear regression equations between crown volumes and dbh, and crown volumes and stem volumes were derived, explaining more than half of data variability. Employment of elliptic Fourier analysis (EFA, a powerful analysis tool, permitted the extraction of the mean shape from crowns, characterized by high morphological variability. The extracted, most important, coefficients were used to reconstruct the average shape of the crowns, using Inverse Fourier Transform. A mean shape of the crown, corresponding to stand conditions in which competition is added as influential shaping factor, aside genetic program of the species, is described for each stem diameter class. Crown regions with highest shape variability, from the perspective of stage development of the trees, were determined. Accordingly, the main crown shape characteristics are: crown elongation, centroid position, asymmetry with regard to the main axis, lateral regions symmetrical and asymmetrical variations. 

BEAST: Bayesian evolutionary analysis by sampling trees

Directory of Open Access Journals (Sweden)

Drummond Alexei J

2007-11-01

Full Text Available Abstract Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

Modular techniques for dynamic fault-tree analysis

Science.gov (United States)

Patterson-Hine, F. A.; Dugan, Joanne B.

1992-01-01

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

[Study on sensitivity of climatic factors on influenza A (H1N1) based on classification and regression tree and wavelet analysis].

Science.gov (United States)

Xiao, Hong; Lin, Xiao-ling; Dai, Xiang-yu; Gao, Li-dong; Chen, Bi-yun; Zhang, Xi-xing; Zhu, Pei-juan; Tian, Huai-yu

2012-05-01

To analyze the periodicity of pandemic influenza A (H1N1) in Changsha in year 2009 and its correlation with sensitive climatic factors. The information of 5439 cases of influenza A (H1N1) and synchronous meteorological data during the period between May 22th and December 31st in year 2009 (223 days in total) in Changsha city were collected. The classification and regression tree (CART) was employed to screen the sensitive climatic factors on influenza A (H1N1); meanwhile, cross wavelet transform and wavelet coherence analysis were applied to assess and compare the periodicity of the pandemic disease and its association with the time-lag phase features of the sensitive climatic factors. The results of CART indicated that the daily minimum temperature and daily absolute humidity were the sensitive climatic factors for the popularity of influenza A (H1N1) in Changsha. The peak of the incidence of influenza A (H1N1) was in the period between October and December (Median (M) = 44.00 cases per day), simultaneously the daily minimum temperature (M = 13°C) and daily absolute humidity (M = 6.69 g/m(3)) were relatively low. The results of wavelet analysis demonstrated that a period of 16 days was found in the epidemic threshold in Changsha, while the daily minimum temperature and daily absolute humidity were the relatively sensitive climatic factors. The number of daily reported patients was statistically relevant to the daily minimum temperature and daily absolute humidity. The frequency domain was mostly in the period of (16 ± 2) days. In the initial stage of the disease (from August 9th and September 8th), a 6-day lag was found between the incidence and the daily minimum temperature. In the peak period of the disease, the daily minimum temperature and daily absolute humidity were negatively relevant to the incidence of the disease. In the pandemic period, the incidence of influenza A (H1N1) showed periodic features; and the sensitive climatic factors did have a "driving

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

Science.gov (United States)

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Development of a User Interface for a Regression Analysis Software Tool

Science.gov (United States)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

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

Method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1972-01-01

Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

[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.

Dynamic Event Tree Analysis Through RAVEN

Energy Technology Data Exchange (ETDEWEB)

A. Alfonsi; C. Rabiti; D. Mandelli; J. Cogliati; R. A. Kinoshita; A. Naviglio

2013-09-01

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

International Nuclear Information System (INIS)

Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

1985-01-01

Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

Study on reliability analysis based on multilevel flow models and fault tree method

International Nuclear Information System (INIS)

Chen Qiang; Yang Ming

2014-01-01

Multilevel flow models (MFM) and fault tree method describe the system knowledge in different forms, so the two methods express an equivalent logic of the system reliability under the same boundary conditions and assumptions. Based on this and combined with the characteristics of MFM, a method mapping MFM to fault tree was put forward, thus providing a way to establish fault tree rapidly and realizing qualitative reliability analysis based on MFM. Taking the safety injection system of pressurized water reactor nuclear power plant as an example, its MFM was established and its reliability was analyzed qualitatively. The analysis result shows that the logic of mapping MFM to fault tree is correct. The MFM is easily understood, created and modified. Compared with the traditional fault tree analysis, the workload is greatly reduced and the modeling time is saved. (authors)

Multivariate analysis of flow cytometric data using decision trees

Directory of Open Access Journals (Sweden)

Svenja eSimon

2012-04-01

Full Text Available Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called 'induction of decision trees' in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees's quality, we used stratified 5-fold cross-validation and chose the 'best' tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

Science.gov (United States)

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

Science.gov (United States)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

2017-09-01

This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

Directory of Open Access Journals (Sweden)

Wen-Tsao Pan

2016-01-01

Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.

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

De novo analysis of electron impact mass spectra using fragmentation trees

International Nuclear Information System (INIS)

Hufsky, Franziska; Rempt, Martin; Rasche, Florian; Pohnert, Georg; Böcker, Sebastian

2012-01-01

Highlights: ► We present a method for de novo analysis of accurate mass EI mass spectra of small molecules. ► This method identifies the molecular ion and thus the molecular formula where the molecular ion is present in the spectrum. ► Fragmentation trees are constructed by automated signal extraction and evaluation. ► These trees explain relevant fragmentation reactions. ► This method will be very helpful in the automated analysis of unknown metabolites. - Abstract: The automated fragmentation analysis of high resolution EI mass spectra based on a fragmentation tree algorithm is introduced. Fragmentation trees are constructed from EI spectra by automated signal extraction and evaluation. These trees explain relevant fragmentation reactions and assign molecular formulas to fragments. The method enables the identification of the molecular ion and the molecular formula of a metabolite if the molecular ion is present in the spectrum. These identifications are independent of existing library knowledge and, thus, support assignment and structural elucidation of unknown compounds. The method works even if the molecular ion is of very low abundance or hidden under contaminants with higher masses. We apply the algorithm to a selection of 50 derivatized and underivatized metabolites and demonstrate that in 78% of cases the molecular ion can be correctly assigned. The automatically constructed fragmentation trees correspond very well to published mechanisms and allow the assignment of specific relevant fragments and fragmentation pathways even in the most complex EI-spectra in our dataset. This method will be very helpful in the automated analysis of metabolites that are not included in common libraries and it thus has the potential to support the explorative character of metabolomics studies.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

Science.gov (United States)

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Directory of Open Access Journals (Sweden)

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'.

A Novel Clinical Prediction Model for Prognosis in Malignant Pleural Mesothelioma Using Decision Tree Analysis.

Science.gov (United States)

Brims, Fraser J H; Meniawy, Tarek M; Duffus, Ian; de Fonseka, Duneesha; Segal, Amanda; Creaney, Jenette; Maskell, Nicholas; Lake, Richard A; de Klerk, Nick; Nowak, Anna K

2016-04-01

Malignant pleural mesothelioma (MPM) is a rare cancer with a heterogeneous prognosis. Prognostic models are not widely utilized clinically. Classification and regression tree (CART) analysis examines the interaction of multiple variables with a given outcome. Between 2005 and 2014, all cases with pathologically confirmed MPM had routinely available histological, clinical, and laboratory characteristics recorded. Classification and regression tree analysis was performed using 29 variables with 18-month survival as the dependent variable. Risk groups were refined according to survival and clinical characteristics. The model was then tested on an external international cohort. A total of 482 cases were included in the derivation cohort; the median survival was 12.6 months, and the median age was 69 years. The model defined four risk groups with clear survival differences (p loss. The group with the best survival at 18 months (86.7% alive, median survival 34.0 months, termed risk group 1) had no weight loss, a hemoglobin level greater than 153 g/L, and a serum albumin level greater than 43 g/L. The group with the worst survival (0% alive, median survival 7.5 months, termed risk group 4d) had weight loss, a performance score of 0 or 1, and sarcomatoid histological characteristics. The C-statistic for the model was 0.761, and the sensitivity was 94.5%. Validation on 174 external cases confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic 0.68). We have developed and validated a simple, clinically relevant model to reliably discriminate patients at high and lower risk of death using routinely available variables from the time of diagnosis in unselected populations of patients with MPM. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

ADTool: Security Analysis with Attack-Defense Trees

NARCIS (Netherlands)

Kordy, Barbara; Kordy, P.T.; Mauw, Sjouke; Schweitzer, Patrick; Joshi, Kaustubh; Siegle, Markus; Stoelinga, Mariëlle Ida Antoinette; d' Argenio, P.R.

ADTool is free, open source software assisting graphical modeling and quantitative analysis of security, using attack–defense trees. The main features of ADTool are easy creation, efficient editing, and automated bottom-up evaluation of security-relevant measures. The tool also supports the usage of

Research and analyze of physical health using multiple regression analysis

Directory of Open Access Journals (Sweden)

T. S. Kyi

2014-01-01

Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

An improved multiple linear regression and data analysis computer program package

Science.gov (United States)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Tree-space statistics and approximations for large-scale analysis of anatomical trees

DEFF Research Database (Denmark)

Feragen, Aasa; Owen, Megan; Petersen, Jens

2013-01-01

parametrize the relevant parts of tree-space well. Using the developed approximate statistics, we illustrate how the structure and geometry of airway trees vary across a population and show that airway trees with Chronic Obstructive Pulmonary Disease come from a different distribution in tree-space than...

Fault Tree Analysis with Temporal Gates and Model Checking Technique for Qualitative System Safety Analysis

International Nuclear Information System (INIS)

Koh, Kwang Yong; Seong, Poong Hyun

2010-01-01

Fault tree analysis (FTA) has suffered from several drawbacks such that it uses only static gates and hence can not capture dynamic behaviors of the complex system precisely, and it is in lack of rigorous semantics, and reasoning process which is to check whether basic events really cause top events is done manually and hence very labor-intensive and time-consuming for the complex systems while it has been one of the most widely used safety analysis technique in nuclear industry. Although several attempts have been made to overcome this problem, they can not still do absolute or actual time modeling because they adapt relative time concept and can capture only sequential behaviors of the system. 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

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

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

2013-01-01

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng

2013-11-05

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

Directory of Open Access Journals (Sweden)

Yoon Song Ha

2018-01-01

Full Text Available A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.

Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations

International Nuclear Information System (INIS)

Shalev, Dan M.; Tiran, Joseph

2007-01-01

Condition-based maintenance methods have changed systems reliability in general and individual systems in particular. Yet, this change does not affect system reliability analysis. System fault tree analysis (FTA) is performed during the design phase. It uses components failure rates derived from available sources as handbooks, etc. Condition-based fault tree analysis (CBFTA) starts with the known FTA. Condition monitoring (CM) methods applied to systems (e.g. vibration analysis, oil analysis, electric current analysis, bearing CM, electric motor CM, and so forth) are used to determine updated failure rate values of sensitive components. The CBFTA method accepts updated failure rates and applies them to the FTA. The CBFTA recalculates periodically the top event (TE) failure rate (λ TE ) thus determining the probability of system failure and the probability of successful system operation-i.e. the system's reliability. FTA is a tool for enhancing system reliability during the design stages. But, it has disadvantages, mainly it does not relate to a specific system undergoing maintenance. CBFTA is tool for updating reliability values of a specific system and for calculating the residual life according to the system's monitored conditions. Using CBFTA, the original FTA is ameliorated to a practical tool for use during the system's field life phase, not just during system design phase. This paper describes the CBFTA method and its advantages are demonstrated by an example

The effect of contaminated groundwater on tree growth: A tree-ring analysis

International Nuclear Information System (INIS)

LeBlanc, D.C.; Loehle, C.

1990-10-01

A study was conducted on the effect of contaminated groundwater seepage on tree growth downslope from F- and H-Area seepage basins of the Savannah River Site. Trees in wetlands along Four Mile Creek began to show localized stress and mortality in the late 1970s. Extreme winter temperatures and high rainfall were ruled out as potential causal factors of tree stress. Drought was shown to affect trees in both contaminated and uncontaminated zones, but trees in uncontaminated areas exhibit better recovery after drought than trees in contaminated areas. Pollution-mediated alteration of soil acidity and aluminum, sodium, and heavy metal concentrations likely acted to predispose trees to decline, with severe drought acting as the trigger for decline initiation and tree death. Thus, a moderate pollution loading, not sufficient to cause visible damage of itself, may create conditions in which sudden, severe decline could result from natural stresses. This mechanism of forest decline is common, and should be considered in evaluations of the impact of pollution on wetland forest systems. 28 refs., 4 figs., 6 tabs

application of multilinear regression analysis in modeling of soil

African Journals Online (AJOL)

Windows User

Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...

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.

Multiple regression analysis of Jominy hardenability data for boron treated steels

International Nuclear Information System (INIS)

Komenda, J.; Sandstroem, R.; Tukiainen, M.

1997-01-01

The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)

Preliminary Investigation into Tree Dryness in Hevea brasiliensis (Wild. Ex Adr. De Juss Muell. Arg. by Path Analysis of Tree Dryness and Latex Parameters

Directory of Open Access Journals (Sweden)

Omokhafe, KO.

2001-01-01

Full Text Available The correlative path analysis between tree dryness and four latex parameters was studied in eleven clones of Hevea brasiliensis. The latex parameters used for evaluation were initial volume, initial flow rate, final volume and plugging index with tree dryness as the dependent factor. Estimates of direct and indirect effects of each of the latex parameters on tree dryness were calculated through linear correlation and path analysis. The correlation coefficient between initial volume and initial flow rate was 1.00 and both characters had the same correlation coefficients with other latex parameters and tree dryness. This implies that initial volume and initial flow rate are alternatives for evaluation of correlation between latex parameters and tree dryness. There was significant correlation between initial volume, initial flow rate and tree dryness at V = -0.52 and high indirect effect of initial volume and initial flow rate through final volume. In addition, despite the low correlation coefficient of V = -0.20 between final volume and tree dryness, the direct effect of final volume on tree dryness was high at 0.812. Final volume is therefore an important factor in the incidence of tree dryness.

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...

Analysis of electrical tree propagation in XLPE power cable insulation

International Nuclear Information System (INIS)

Bao Minghui; Yin Xiaogen; He Junjia

2011-01-01

Electrical treeing is one of the major breakdown mechanisms for solid dielectrics subjected to high electrical stress. In this paper, the characteristics of electrical tree growth in XLPE samples have been investigated. XLPE samples are obtained from a commercial XLPE power cable, in which electrical trees have been grown from pin to plane in the frequency range of 4000-10,000 Hz, voltage range of 4-10 kV, and the distances between electrodes of 1 and 2 mm. Images of trees and their growing processes were taken by a CCD camera. The fractal dimensions of electric trees were obtained by using a simple box-counting technique. The results show that the tree growth rate and fractal dimension was bigger when the frequency or voltage was higher, or the distance between electrodes was smaller. Contrary to our expectation, it has been found that when the distance between electrodes changed from 1 to 2 mm, the required voltage of the similar electrical trees decreased only 1or 2 kV. In order to evaluate the difficulties of electrical tree propagation in different conditions, a simple energy threshold analysis method has been proposed. The threshold energy, which presents the minimum energy that a charge carrier in the well at the top of the tree should have to make the tree grow, has been computed considering the length of electrical tree, the fractal dimension, and the growth time. The computed results indicate that when one of the three parameters of voltage, frequency, and local electric field increase, the trends of energy threshold can be split into 3 regions.

Pan-Tropical Analysis of Climate Effects on Seasonal Tree Growth

Science.gov (United States)

Wagner, Fabien; Rossi, Vivien; Aubry-Kientz, Mélaine; Bonal, Damien; Dalitz, Helmut; Gliniars, Robert; Stahl, Clément; Trabucco, Antonio; Hérault, Bruno

2014-01-01

Climate models predict a range of changes in tropical forest regions, including increased average temperatures, decreased total precipitation, reduced soil moisture and alterations in seasonal climate variations. These changes are directly related to the increase in anthropogenic greenhouse gas concentrations, primarily CO2. Assessing seasonal forest growth responses to climate is of utmost importance because woody tissues, produced by photosynthesis from atmospheric CO2, water and light, constitute the main component of carbon sequestration in the forest ecosystem. In this paper, we combine intra-annual tree growth measurements from published tree growth data and the corresponding monthly climate data for 25 pan-tropical forest sites. This meta-analysis is designed to find the shared climate drivers of tree growth and their relative importance across pan-tropical forests in order to improve carbon uptake models in a global change context. Tree growth reveals significant intra-annual seasonality at seasonally dry sites or in wet tropical forests. Of the overall variation in tree growth, 28.7% was explained by the site effect, i.e. the tree growth average per site. The best predictive model included four climate variables: precipitation, solar radiation (estimated with extrasolar radiation reaching the atmosphere), temperature amplitude and relative soil water content. This model explained more than 50% of the tree growth variations across tropical forests. Precipitation and solar radiation are the main seasonal drivers of tree growth, causing 19.8% and 16.3% of the tree growth variations. Both have a significant positive association with tree growth. These findings suggest that forest productivity due to tropical tree growth will be reduced in the future if climate extremes, such as droughts, become more frequent. PMID:24670981

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

Science.gov (United States)

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

Plotting and analysis of fault trees in safety evaluation of nuclear power plants

International Nuclear Information System (INIS)

Wild, A.

1979-12-01

Fault tree analysis is a useful tool in determining the safety and reliability of nuclear power plants. The main strength of the fault tree method, its ability to detect cross-links between systems, can be used only if fault trees are constructed for complete nuclear generating stations. Such trees are large and have to be handled by computers. A system is described for handling fault trees using small computers such as the HP-1000 with disc drive, graphics terminal and x-y plotter

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.

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

Science.gov (United States)

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.

Understanding logistic regression analysis

OpenAIRE

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

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....

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.

Radiation densitometry in tree-ring analysis: a review and procedure manual

Energy Technology Data Exchange (ETDEWEB)

Parker, M.L.; Taylor, F.G.; Doyle, T.W.; Foster, B.E.; Cooper, C.; West, D.C.

1985-01-01

An x-ray densitometry of wood facility is being established by the Environmental Sciences Division, Oak Ridge Natioanl Laboratory (ORNL). The objective is to apply tree-ring data to determine whether or not there is a fertilizer effect on tree growth from increased atmospheric carbon dioxide since the beginning of the industrial era. Intra-ring width and density data, including ring-mass will be detemined from tree-ring samples collected from sites located throughout the United States and Canada. This report is designed as a guide to assist ORNL scientists in building the x-ray densitometry system. The history and development of x-ray densitometry in tree-ring research is examined and x-ray densitometry is compared with other techniques. Relative wood and tree characteristics are described as are environmental and genetic factors affecting tree growth responses. Methods in x-ray densitometry are examined in detail and the techniques used at four operating laboratories are described. Some ways that dendrochronology has been applied in dating, in wood quality, and environmental studies are presented, and a number of tree-ring studies in Canada are described. An annotated bibliography of radiation densitometry in tree-ring analysis and related subjects is included.

Radiation densitometry in tree-ring analysis: a review and procedure manual

International Nuclear Information System (INIS)

Parker, M.L.; Taylor, F.G.; Doyle, T.W.; Foster, B.E.; Cooper, C.; West, D.C.

1985-01-01

An x-ray densitometry of wood facility is being established by the Environmental Sciences Division, Oak Ridge Natioanl Laboratory (ORNL). The objective is to apply tree-ring data to determine whether or not there is a fertilizer effect on tree growth from increased atmospheric carbon dioxide since the beginning of the industrial era. Intra-ring width and density data, including ring-mass will be detemined from tree-ring samples collected from sites located throughout the United States and Canada. This report is designed as a guide to assist ORNL scientists in building the x-ray densitometry system. The history and development of x-ray densitometry in tree-ring research is examined and x-ray densitometry is compared with other techniques. Relative wood and tree characteristics are described as are environmental and genetic factors affecting tree growth responses. Methods in x-ray densitometry are examined in detail and the techniques used at four operating laboratories are described. Some ways that dendrochronology has been applied in dating, in wood quality, and environmental studies are presented, and a number of tree-ring studies in Canada are described. An annotated bibliography of radiation densitometry in tree-ring analysis and related subjects is included

PL-MOD: a computer code for modular fault tree analysis and evaluation

International Nuclear Information System (INIS)

Olmos, J.; Wolf, L.

1978-01-01

The computer code PL-MOD has been developed to implement the modular methodology to fault tree analysis. In the modular approach, fault tree structures are characterized by recursively relating the top tree event to all basic event inputs through a set of equations, each defining an independent modular event for the tree. The advantages of tree modularization lie in that it is a more compact representation than the minimal cut-set description and in that it is well suited for fault tree quantification because of its recursive form. In its present version, PL-MOD modularizes fault trees and evaluates top and intermediate event failure probabilities, as well as basic component and modular event importance measures, in a very efficient way. Thus, its execution time for the modularization and quantification of a PWR High Pressure Injection System reduced fault tree was 25 times faster than that necessary to generate its equivalent minimal cut-set description using the computer code MOCUS

FuncTree: Functional Analysis and Visualization for Large-Scale Omics Data.

Directory of Open Access Journals (Sweden)

Takeru Uchiyama

Full Text Available Exponential growth of high-throughput data and the increasing complexity of omics information have been making processing and interpreting biological data an extremely difficult and daunting task. Here we developed FuncTree (http://bioviz.tokyo/functree, a web-based application for analyzing and visualizing large-scale omics data, including but not limited to genomic, metagenomic, and transcriptomic data. FuncTree allows user to map their omics data onto the "Functional Tree map", a predefined circular dendrogram, which represents the hierarchical relationship of all known biological functions defined in the KEGG database. This novel visualization method allows user to overview the broad functionality of their data, thus allowing a more accurate and comprehensive understanding of the omics information. FuncTree provides extensive customization and calculation methods to not only allow user to directly map their omics data to identify the functionality of their data, but also to compute statistically enriched functions by comparing it to other predefined omics data. We have validated FuncTree's analysis and visualization capability by mapping pan-genomic data of three different types of bacterial genera, metagenomic data of the human gut, and transcriptomic data of two different types of human cell expression. All three mapping strongly confirms FuncTree's capability to analyze and visually represent key functional feature of the omics data. We believe that FuncTree's capability to conduct various functional calculations and visualizing the result into a holistic overview of biological function, would make it an integral analysis/visualization tool for extensive omics base research.

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.

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.

An investigation of the relations between fault tree analysis and cause consequence analysis with special reference to a photometry and conductimetry system

International Nuclear Information System (INIS)

Weber, G.

1980-02-01

For an automated photometry and conductimetry system, the relations between cause consequence analysis and fault tree analysis have been investigated. It has been shown how failure combinations of a cause consequence diagram and minimal cuts of a fault tree can be identified. This procedure allows a mutual control of fault tree analysis and cause consequence analysis. From a representation of all failure combinations of the system by means of a matrix we obtain a control of our analysis. Moreover, heuristic rules improving and simplifying the cause consequence analysis can be found. Necessary assumptions for the validity of these rules are discussed. Methodologically, the relation of a fault tree and a cause consequence diagram can be represented (under certain conditions) as a relation of a Boolean function and a binary decision tree. (orig.) 891 HP/orig. 892 MKO [de

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.

Repeated measures from FIA data facilitates analysis across spatial scales of tree growth responses to nitrogen deposition from individual trees to whole ecoregions

Science.gov (United States)

Charles H. (Hobie) Perry; Kevin J. Horn; R. Quinn Thomas; Linda H. Pardo; Erica A.H. Smithwick; Doug Baldwin; Gregory B. Lawrence; Scott W. Bailey; Sabine Braun; Christopher M. Clark; Mark Fenn; Annika Nordin; Jennifer N. Phelan; Paul G. Schaberg; Sam St. Clair; Richard Warby; Shaun Watmough; Steven S. Perakis

2015-01-01

The abundance of temporally and spatially consistent Forest Inventory and Analysis data facilitates hierarchical/multilevel analysis to investigate factors affecting tree growth, scaling from plot-level to continental scales. Herein we use FIA tree and soil inventories in conjunction with various spatial climate and soils data to estimate species-specific responses of...

The value of decision tree analysis in planning anaesthetic care in obstetrics.

Science.gov (United States)

Bamber, J H; Evans, S A

2016-08-01

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Poisson Regression Analysis of Illness and Injury Surveillance Data

Energy Technology Data Exchange (ETDEWEB)

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra

Reliability Analysis of Operation for Cableways by FTA (Fault Tree Analysis Method

Directory of Open Access Journals (Sweden)

Sergej Težak

2010-05-01

Full Text Available This paper examines the reliability of the operation of cableway systems in Slovenia, which has major impact on the quality of service in the mountain tourism, mainly in wintertime. Different types of cableway installations in Slovenia were captured in a sample and fault tree analysis (FTA was made on the basis of the obtained data. The paper presents the results of the analysis. With these results it is possible to determine the probability of faults of different types of cableways, which types of faults have the greatest impact on the termination of operation, which components of cableways fail most, what is the impact of age of cableways on the occurrence of the faults. Finally, an attempt was made to find if occurrence of faults on individual cableway installation has also impact on traffic on this cableway due to reduced quality of service. KEYWORDS: cableways, aerial ropeways, chairlifts, ski-tows, quality, faults, fault tree analysis, reliability, service quality, winter tourism, mountain tourist centre

α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

International Nuclear Information System (INIS)

Purba, Julwan Hendry; Sony Tjahyani, D.T.; Widodo, Surip; Tjahjono, Hendro

2017-01-01

Highlights: •FPFTA deals with epistemic uncertainty using fuzzy probability. •Criticality analysis is important for reliability improvement. •An α-cut method based importance measure is proposed for criticality analysis in FPFTA. •The α-cut method based importance measure utilises α-cut multiplication, α-cut subtraction, and area defuzzification technique. •Benchmarking confirm that the proposed method is feasible for criticality analysis in FPFTA. -- Abstract: Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results

Automated fault tree analysis: the GRAFTER system

International Nuclear Information System (INIS)

Sancaktar, S.; Sharp, D.R.

1985-01-01

An inherent part of probabilistic risk assessment (PRA) is the construction and analysis of detailed fault trees. For this purpose, a fault tree computer graphics code named GRAFTER has been developed. The code system centers around the GRAFTER code. This code is used interactively to construct, store, update and print fault trees of small or large sizes. The SIMON code is used to provide data for the basic event probabilities. ENCODE is used to process the GRAFTER files to prepare input for the WAMCUT code. WAMCUT is used to quantify the top event probability and to identify the cutsets. This code system has been extensively used in various PRA projects. It has resulted in reduced manpower costs, increased QA capability, ease of documentation and it has simplified sensitivity analyses. Because of its automated nature, it is also suitable for LIVING PRA Studies which require updating and modifications during the lifetime of the plant. Brief descriptions and capabilities of the GRAFTER, SIMON and ENCODE codes are provided; an application of the GRAFTER system is outlined; and conclusions and comments on the code system are given

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

Science.gov (United States)

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

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."

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.)

Understanding logistic regression analysis.

Science.gov (United States)

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

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.

Decision tree modeling using R.

Science.gov (United States)

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.

TreeNetViz: revealing patterns of networks over tree structures.

Science.gov (United States)

Gou, Liang; Zhang, Xiaolong Luke

2011-12-01

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

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.

Geospatial relationships of tree species damage caused by Hurricane Katrina in south Mississippi

Science.gov (United States)

Mark W. Garrigues; Zhaofei Fan; David L. Evans; Scott D. Roberts; William H. Cooke III

2012-01-01

Hurricane Katrina generated substantial impacts on the forests and biological resources of the affected area in Mississippi. This study seeks to use classification tree analysis (CTA) to determine which variables are significant in predicting hurricane damage (shear or windthrow) in the Southeast Mississippi Institute for Forest Inventory District. Logistic regressions...

Fault tree analysis for vital area identification

International Nuclear Information System (INIS)

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

1978-01-01

The use of fault tree analysis techniques to systematically identify (1) the sabotage events which can lead to release of significant quantities of radioactive materials, (2) the areas of the nuclear power plant in which the sabotage events can be accomplished, and (3) the areas of the plant which must be protected to assure that release does not occur are discussed

Event tree analysis using artificial intelligence techniques

International Nuclear Information System (INIS)

Dixon, B.W.; Hinton, M.F.

1985-01-01

Artificial Intelligence (AI) techniques used in Expert Systems and Object Oriented Programming are discussed as they apply to Event Tree Analysis. A SeQUence IMPortance calculator, SQUIMP, is presented to demonstrate the implementation of these techniques. Benefits of using AI methods include ease of programming, efficiency of execution, and flexibility of application. The importance of an appropriate user interface is stressed. 5 figs

Statistical evaluation of fuel yield and morphological variates for some promising energy plantation tree species in western Rajasthan

Energy Technology Data Exchange (ETDEWEB)

Kalla, J.C.

1977-01-01

Stepwise regression analysis suggested that tree height and collar diameter were, in general, the morphological parameters that most reliably predicted fuel yield in Acacia nilotica, A. tortilis, Albizzia lebbek, Azadirachta indica and Prosopis juliflora.

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...

Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.

Science.gov (United States)

He, Jichao; Wanik, David W; Hartman, Brian M; Anagnostou, Emmanouil N; Astitha, Marina; Frediani, Maria E B

2017-03-01

This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources. © 2016 Society for Risk Analysis.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

Science.gov (United States)

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

TU-AB-BRD-03: Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Dunscombe, P. [University of Calgary (Canada)

2015-06-15

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to

TU-AB-BRD-03: Fault Tree Analysis

International Nuclear Information System (INIS)

Dunscombe, P.

2015-01-01

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to

Holocene tree-line variability in the Kauner Valley, Central Eastern Alps, indicated by dendrochronological analysis of living trees and subfossil logs

NARCIS (Netherlands)

Nicolussi, Kurt; Kaufmann, Matthias; Patzelt, Gernot; van der Plicht, Johannes; Thurner, Andrea

2005-01-01

The altitude of the Alpine tree-line has often been used as proxy for the climatic conditions in the Holocene epoch. The usual approach for establishing a record for this proxy is the analysis of pollen and macro remains. We analysed living trees and subfossil logs from the timberline ecotone in the

Management of Industrial Performance Indicators: Regression Analysis and Simulation

Directory of Open Access Journals (Sweden)

Walter Roberto Hernandez Vergara

2017-11-01

Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.

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

Science.gov (United States)

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.

Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

Science.gov (United States)

Hecht, Jeffrey B.

The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

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.)

Fuzzy Uncertainty Evaluation for Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)

2015-05-15

This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.

Vulnerability Analysis of Urban Drainage Systems: Tree vs. Loop Networks

Directory of Open Access Journals (Sweden)

Chi Zhang

2017-03-01

Full Text Available Vulnerability analysis of urban drainage networks plays an important role in urban flood management. This study analyzes and compares the vulnerability of tree and loop systems under various rainfall events to structural failure represented by pipe blockage. Different pipe blockage scenarios, in which one of the pipes in an urban drainage network is assumed to be blocked individually, are constructed and their impacts on the network are simulated under different storm events. Furthermore, a vulnerability index is defined to measure the vulnerability of the drainage systems before and after the implementation of adaptation measures. The results obtained indicate that the tree systems have a relatively larger proportion of critical hydraulic pipes than the loop systems, thus the vulnerability of tree systems is substantially greater than that of the loop systems. Furthermore, the vulnerability index of tree systems is reduced after they are converted into a loop system with the implementation of adaptation measures. This paper provides an insight into the differences in the vulnerability of tree and loop systems, and provides more evidence for development of adaptation measures (e.g., tanks to reduce urban flooding.

Fault Tree Analysis for an Inspection Robot in a Nuclear Power Plant

Science.gov (United States)

Ferguson, Thomas A.; Lu, Lixuan

2017-09-01

The life extension of current nuclear reactors has led to an increasing demand on inspection and maintenance of critical reactor components that are too expensive to replace. To reduce the exposure dosage to workers, robotics have become an attractive alternative as a preventative safety tool in nuclear power plants. It is crucial to understand the reliability of these robots in order to increase the veracity and confidence of their results. This study presents the Fault Tree (FT) analysis to a coolant outlet piper snake-arm inspection robot in a nuclear power plant. Fault trees were constructed for a qualitative analysis to determine the reliability of the robot. Insight on the applicability of fault tree methods for inspection robotics in the nuclear industry is gained through this investigation.

Ion Uptake Determination of Dendrochronologically-Dated Trees Using Neutron Activation Analysis

Energy Technology Data Exchange (ETDEWEB)

Kenan Unlu; P.I. Kuniholm; D.K.H. Schwarz; N.O. Cetiner; J.J. Chiment

2009-03-30

Uptake of metal ions by plan roots is a function of the type and concentration of metal in the soil, the nutrient biochemistry of the plant, and the immediate environment of the root. Uptake of gold (Au) is known to be sensitive to soil pH for many species. Soil acidification due to acid precipitation following volcanic eruptions can dramatically increase Au uptake by trees. Identification of high Au content in tree rings in dendrochronologically-dated, overlapping sequences of trees allows the identification of temporally-conscribed, volcanically-influenced periods of environmental change. Ion uptake, specifically determination of trace amounts of gold, was performed for dendrochronologically-dated tree samples utilizing Neutron Activation Analysis (NAA) technique. The concentration of gold was correlated with known enviironmental changes, e.g. volcanic activities, during historic periods.

Predicting Dropouts of University Freshmen: A Logit Regression Analysis.

Science.gov (United States)

Lam, Y. L. Jack

1984-01-01

Stepwise discriminant analysis coupled with logit regression analysis of freshmen data from Brandon University (Manitoba) indicated that six tested variables drawn from research on university dropouts were useful in predicting attrition: student status, residence, financial sources, distance from home town, goal fulfillment, and satisfaction with…

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

Tracing footprints of environmental events in tree ring chemistry using neutron activation analysis

Science.gov (United States)

Sahin, Dagistan

The aim of this study is to identify environmental effects on tree-ring chemistry. It is known that industrial pollution, volcanic eruptions, dust storms, acid rain and similar events can cause substantial changes in soil chemistry. Establishing whether a particular group of trees is sensitive to these changes in soil environment and registers them in the elemental chemistry of contemporary growth rings is the over-riding goal of any Dendrochemistry research. In this study, elemental concentrations were measured in tree-ring samples of absolutely dated eleven modern forest trees, grown in the Mediterranean region, Turkey, collected and dated by the Malcolm and Carolyn Wiener Laboratory for Aegean and Near Eastern Dendrochronology laboratory at Cornell University. Correlations between measured elemental concentrations in the tree-ring samples were analyzed using statistical tests to answer two questions. Does the current concentration of a particular element depend on any other element within the tree? And, are there any elements showing correlated abnormal concentration changes across the majority of the trees? Based on the detailed analysis results, the low mobility of sodium and bromine, positive correlations between calcium, zinc and manganese, positive correlations between trace elements lanthanum, samarium, antimony, and gold within tree-rings were recognized. Moreover, zinc, lanthanum, samarium and bromine showed strong, positive correlations among the trees and were identified as possible environmental signature elements. New Dendrochemistry information found in this study would be also useful in explaining tree physiology and elemental chemistry in Pinus nigra species grown in Turkey. Elemental concentrations in tree-ring samples were measured using Neutron Activation Analysis (NAA) at the Pennsylvania State University Radiation Science and Engineering Center (RSEC). Through this study, advanced methodologies for methodological, computational and

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.

Static terrestrial laser scanning of juvenile understory trees for field phenotyping

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Wang, Huanhuan; Lin, Yi

2014-11-01

This study was to attempt the cutting-edge 3D remote sensing technique of static terrestrial laser scanning (TLS) for parametric 3D reconstruction of juvenile understory trees. The data for test was collected with a Leica HDS6100 TLS system in a single-scan way. The geometrical structures of juvenile understory trees are extracted by model fitting. Cones are used to model trunks and branches. Principal component analysis (PCA) is adopted to calculate their major axes. Coordinate transformation and orthogonal projection are used to estimate the parameters of the cones. Then, AutoCAD is utilized to simulate the morphological characteristics of the understory trees, and to add secondary branches and leaves in a random way. Comparison of the reference values and the estimated values gives the regression equation and shows that the proposed algorithm of extracting parameters is credible. The results have basically verified the applicability of TLS for field phenotyping of juvenile understory trees.

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

Quality of life in breast cancer patients--a quantile regression analysis.

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Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Instrumental neutron activation analysis of tree rings for dendrochemical studies

International Nuclear Information System (INIS)

Schaumloffel, J.C.; Filby, R.H.

1996-01-01

Instrumental neutron activation analysis (INAA) was employed to determine zinc, cadmium and potassium concentrations in the growth rings of ponderosa pine (Pinus ponderosa Dougl.) trees growing along the shores of Lake Roosevelt in Washington State, U.S.A. where mineral processing activities have resulted in high burdens of zinc and cadmium in the lake sediments. The tree growing along the contaminated waterway display elevated concentrations of zinc in its growth rings relative to a tree growing along an uncontaminated tributary of Lake Roosevelt. Cadmium concentrations in the growth rings from both sites are similar from 1988 to 1993. Water quality data indicate an increased concentration of cadmium in the lake from 1984 to 1988. The increased concentrations of cadmium in the lake water were reflected in apparent increases in concentrations of cadmium in individual rings of the tree sampled at the contaminated site. This suggests that translocation of cadmium in the sapwood of heartwood-forming species does not occur in the short term, and thus may not be a limiting factor in using trees as environmental monitors for cadmium. In addition, five-year tree ring segments were analyzed and subsequently reanalyzed as individual single-year ring segments. The analytical data obtained for the pooled individual rings are essentially the same as for the five-year segments, demonstrating the utility of NAA for dendrochemical studies. (author). 24 refs., 5 figs., 2 tabs

Fault tree analysis with multistate components

International Nuclear Information System (INIS)

Caldarola, L.

1979-02-01

A general analytical theory has been developed which allows one to calculate the occurence probability of the top event of a fault tree with multistate (more than states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of Boolean algebra is required. This is called 'Boolean algebra with restrictions on varibales' and its basic rules are the same as those of the traditional Boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. Important features of the method are the identification of the complete base and of the smallest irredundant base of a Boolean function which does not necessarily need to be coherent. It is shown that the identification of the complete base of a Boolean function requires the application of some algorithms which are not used in today's computer programmes for fault tree analysis. The problem of statistical dependence among primary components is discussed. The paper includes a small demonstrative example to illustrate the method. The example includes also statistical dependent components. (orig.) [de

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

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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

Classification and Regression Tree Analysis of Clinical Patterns that Predict Survival in 127 Chinese Patients with Advanced Non-small Cell Lung Cancer Treated by Gefitinib Who Failed to Previous Chemotherapy

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Ziping WANG

2011-09-01

Full Text Available Background and objective It has been proven that gefitinib produces only 10%-20% tumor regression in heavily pretreated, unselected non-small cell lung cancer (NSCLC patients as the second- and third-line setting. Asian, female, nonsmokers and adenocarcinoma are favorable factors; however, it is difficult to find a patient satisfying all the above clinical characteristics. The aim of this study is to identify novel predicting factors, and to explore the interactions between clinical variables and their impact on the survival of Chinese patients with advanced NSCLC who were heavily treated with gefitinib in the second- or third-line setting. Methods The clinical and follow-up data of 127 advanced NSCLC patients referred to the Cancer Hospital & Institute, Chinese Academy of Medical Sciences from March 2005 to March 2010 were analyzed. Multivariate analysis of progression-free survival (PFS was performed using recursive partitioning, which is referred to as the classification and regression tree (CART analysis. Results The median PFS of 127 eligible consecutive advanced NSCLC patients was 8.0 months (95%CI: 5.8-10.2. CART was performed with an initial split on first-line chemotherapy outcomes and a second split on patients’ age. Three terminal subgroups were formed. The median PFS of the three subsets ranged from 1.0 month (95%CI: 0.8-1.2 for those with progressive disease outcome after the first-line chemotherapy subgroup, 10 months (95%CI: 7.0-13.0 in patients with a partial response or stable disease in first-line chemotherapy and age <70, and 22.0 months for patients obtaining a partial response or stable disease in first-line chemotherapy at age 70-81 (95%CI: 3.8-40.1. Conclusion Partial response, stable disease in first-line chemotherapy and age ≥ 70 are closely correlated with long-term survival treated by gefitinib as a second- or third-line setting in advanced NSCLC. CART can be used to identify previously unappreciated patient

MulRF: a software package for phylogenetic analysis using multi-copy gene trees.

Science.gov (United States)

Chaudhary, Ruchi; Fernández-Baca, David; Burleigh, John Gordon

2015-02-01

MulRF is a platform-independent software package for phylogenetic analysis using multi-copy gene trees. It seeks the species tree that minimizes the Robinson-Foulds (RF) distance to the input trees using a generalization of the RF distance to multi-labeled trees. The underlying generic tree distance measure and fast running time make MulRF useful for inferring phylogenies from large collections of gene trees, in which multiple evolutionary processes as well as phylogenetic error may contribute to gene tree discord. MulRF implements several features for customizing the species tree search and assessing the results, and it provides a user-friendly graphical user interface (GUI) with tree visualization. The species tree search is implemented in C++ and the GUI in Java Swing. MulRF's executable as well as sample datasets and manual are available at http://genome.cs.iastate.edu/CBL/MulRF/, and the source code is available at https://github.com/ruchiherself/MulRFRepo. ruchic@ufl.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

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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.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Directory of Open Access Journals (Sweden)

Santana Isabel

2011-08-01

Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

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.

FTA, Fault Tree Analysis for Minimal Cut Sets, Graphics for CALCOMP

International Nuclear Information System (INIS)

Van Slyke, W.J.; Griffing, D.E.; Diven, J.

1978-01-01

1 - Description of problem or function: The FTA (Fault Tree Analysis) system was designed to predict probabilities of the modes of failure for complex systems and to graphically present the structure of systems. There are three programs in the system. Program ALLCUTS performs the calculations. Program KILMER constructs a CalComp plot file of the system fault tree. Program BRANCH builds a cross-reference list of the system fault tree. 2 - Method of solution: ALLCUTS employs a top-down set expansion algorithm to find fault tree cut-sets and then optionally calculates their probability using a currently accepted cut-set quantification method. The methodology is adapted from that in WASH-1400 (draft), August 1974. 3 - Restrictions on the complexity of the problem: Maxima of: 175 basic events, 425 rate events. ALLCUTS may be expanded to solve larger problems depending on available core memory

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

Science.gov (United States)

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

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

Science.gov (United States)

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.

Regression analysis of radiological parameters in nuclear power plants

International Nuclear Information System (INIS)

Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.

2003-01-01

Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)

TREE STEM AND CANOPY BIOMASS ESTIMATES FROM TERRESTRIAL LASER SCANNING DATA

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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.

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.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

Science.gov (United States)

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Seismic Margin Assessment for Research Reactor using Fragility based Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Kwag, Shinyoung; Oh, Jinho; Lee, Jong-Min; Ryu, Jeong-Soo [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

The research reactor has been often subjected to external hazards during the design lifetime. Especially, a seismic event can be one of significant threats to the failure of structure system of the research reactor. This failure is possibly extended to the direct core damage of the reactor. For this purpose, the fault tree for structural system failure leading to the core damage under an earthquake accident is developed. The failure probabilities of basic events are evaluated as fragility curves of log-normal distributions. Finally, the plant-level seismic margin is investigated by the fault tree analysis combining with fragility data and the critical path is identified. The plant-level probabilistic seismic margin assessment using the fragility based fault tree analysis was performed for quantifying the safety of research reactor to a seismic hazard. For this, the fault tree for structural system failure leading to the core damage of the reactor under a seismic accident was developed. The failure probabilities of basic events were evaluated as fragility curves of log-normal distributions.

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

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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

Regression Analysis: Instructional Resource for Cost/Managerial Accounting

Science.gov (United States)

Stout, David E.

2015-01-01

This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

Retrospective evaluation of tritium fallout by tree-ring analysis

International Nuclear Information System (INIS)

Kozak, K.; Biro, T.; Golder, F.; Rank, D.; Rajner, V.; Staudner, F.

1993-01-01

Tritium analyses of tree-ring cellulose were made to test its suitability for retrospective evaluation of a local tritium fallout. Several spruce trees were taken from an Austrian alpine area where tritium contamination of May 1974 precipitation had been detected. Wood from the annual growth rings of 1973, 1974 and 1975 was separated and the cellulose extracted. After isotopic equilibration with dead water, cellulose was combusted to yield water, whose tritium concentration was measured by liquid scintillation counting. Rigorous statistical treatment proved the significance of the increased tritium concentration caused by the tritium anomaly, which occurred during the growing season. The long-term trends of local atmospheric tritium, including the 1974 peak, were also well reflected by analysis of a 24-year ring sequence from a single tree in the contaminated area. The tritium data gained by the given method can be used at present qualitatively and a better understanding of the possible sources of contamination is required in order that the quantitative criteria be satisfied. (Author)

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

Modeling and Testing Landslide Hazard Using Decision Tree

Directory of Open Access Journals (Sweden)

Mutasem Sh. Alkhasawneh

2014-01-01

Full Text Available This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID, Exhaustive CHAID, Classification and Regression Tree (CRT, and Quick-Unbiased-Efficient Statistical Tree (QUEST. Twenty-one factors were extracted using digital elevation models (DEMs and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0% compared to CHAID (81.9%, CRT (75.6%, and QUEST (74.0% model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

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.

Robust Mediation Analysis Based on Median Regression

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2014-01-01

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

A comparative study of multiple regression analysis and back ...

Indian Academy of Sciences (India)

Abhijit Sarkar

artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding ... Keywords. Submerged arc welding (SAW); multi-regression analysis (MRA); artificial neural network ..... Degree of freedom.

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

TREE SELECTING AND TREE RING MEASURING IN DENDROCHRONOLOGICAL INVESTIGATIONS

Directory of Open Access Journals (Sweden)

Sefa Akbulut

2004-04-01

Full Text Available Dendrochronology is a method of dating which makes use of the annual nature of tree growth. Dendrochronology may be divided into a number of subfields, each of which covers one or more aspects of the use of tree ring data: dendroclimatology, dendrogeomorphology, dendrohydrology, dendroecology, dendroarchaelogy, and dendrogylaciology. Basic of all form the analysis of the tree rings. The wood or tree rings can aid to dating past events about climatology, ecology, geology, hydrology. Dendrochronological studies are conducted either on increment cores or on discs. It may be seen abnormalities on tree rings during the measurement like that false rings, missing rings, reaction wood. Like that situation, increment cores must be extracted from four different sides of each tree and be studied as more as on tree.

Arenal-type pyroclastic flows: A probabilistic event tree risk analysis

Science.gov (United States)

Meloy, Anthony F.

2006-09-01

A quantitative hazard-specific scenario-modelling risk analysis is performed at Arenal volcano, Costa Rica for the newly recognised Arenal-type pyroclastic flow (ATPF) phenomenon using an event tree framework. These flows are generated by the sudden depressurisation and fragmentation of an active basaltic andesite lava pool as a result of a partial collapse of the crater wall. The deposits of this type of flow include angular blocks and juvenile clasts, which are rarely found in other types of pyroclastic flow. An event tree analysis (ETA) is a useful tool and framework in which to analyse and graphically present the probabilities of the occurrence of many possible events in a complex system. Four event trees are created in the analysis, three of which are extended to investigate the varying individual risk faced by three generic representatives of the surrounding community: a resident, a worker, and a tourist. The raw numerical risk estimates determined by the ETA are converted into a set of linguistic expressions (i.e. VERY HIGH, HIGH, MODERATE etc.) using an established risk classification scale. Three individually tailored semi-quantitative risk maps are then created from a set of risk conversion tables to show how the risk varies for each individual in different areas around the volcano. In some cases, by relocating from the north to the south, the level of risk can be reduced by up to three classes. While the individual risk maps may be broadly applicable, and therefore of interest to the general community, the risk maps and associated probability values generated in the ETA are intended to be used by trained professionals and government agencies to evaluate the risk and effectively manage the long-term development of infrastructure and habitation. With the addition of fresh monitoring data, the combination of both long- and short-term event trees would provide a comprehensive and consistent method of risk analysis (both during and pre-crisis), and as such

Power system reliability analysis using fault trees

International Nuclear Information System (INIS)

Volkanovski, A.; Cepin, M.; Mavko, B.

2006-01-01

The power system reliability analysis method is developed from the aspect of reliable delivery of electrical energy to customers. The method is developed based on the fault tree analysis, which is widely applied in the Probabilistic Safety Assessment (PSA). The method is adapted for the power system reliability analysis. The method is developed in a way that only the basic reliability parameters of the analysed power system are necessary as an input for the calculation of reliability indices of the system. The modeling and analysis was performed on an example power system consisting of eight substations. The results include the level of reliability of current power system configuration, the combinations of component failures resulting in a failed power delivery to loads, and the importance factors for components and subsystems. (author)

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

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

Science.gov (United States)

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

Post Fire Safe Shutdown Analysis Using a Fault Tree Logic Model

International Nuclear Information System (INIS)

Yim, Hyun Tae; Park, Jun Hyun

2005-01-01

Every nuclear power plant should have its own fire hazard analysis including the fire safe shutdown analysis. A safe shutdown (SSD) analysis is performed to demonstrate the capability of the plant to safely shut down for a fire in any given area. The basic assumption is that there will be fire damage to all cables and equipment located within a common fire area. When evaluating the SSD capabilities of the plant, based on a review of the systems, equipment and cables within each fire area, it should be determined which shutdown paths are either unaffected or least impacted by a postulated fire within the fire area. Instead of seeking a success path for safe shutdown given all cables and equipment damaged by a fire, there can be an alternative approach to determine the SSD capability: fault tree analysis. This paper introduces the methodology for fire SSD analysis using a fault tree logic model

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

Science.gov (United States)

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.

Exploratory regression analysis: a tool for selecting models and determining predictor importance.

Science.gov (United States)

Braun, Michael T; Oswald, Frederick L

2011-06-01

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

Regression analysis for LED color detection of visual-MIMO system

Science.gov (United States)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

Science.gov (United States)

Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

2017-12-01

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

Application of Fault Tree Analysis for Estimating Temperature Alarm Circuit Reliability

International Nuclear Information System (INIS)

El-Shanshoury, A.I.; El-Shanshoury, G.I.

2011-01-01

Fault Tree Analysis (FTA) is one of the most widely-used methods in system reliability analysis. It is a graphical technique that provides a systematic description of the combinations of possible occurrences in a system, which can result in an undesirable outcome. The presented paper deals with the application of FTA method in analyzing temperature alarm circuit. The criticality failure of this circuit comes from failing to alarm when temperature exceeds a certain limit. In order for a circuit to be safe, a detailed analysis of the faults causing circuit failure is performed by configuring fault tree diagram (qualitative analysis). Calculations of circuit quantitative reliability parameters such as Failure Rate (FR) and Mean Time between Failures (MTBF) are also done by using Relex 2009 computer program. Benefits of FTA are assessing system reliability or safety during operation, improving understanding of the system, and identifying root causes of equipment failures

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Sensitivity Analysis for Assessing Effects of Tree Population Dynamics on Soil Bioturbation

Science.gov (United States)

Martin, Y. E.; Johnson, E. A.

2012-12-01

demonstrate that wildfire return intervals, recruitment rates and mortality rates are all critical in determining patterns in the number of live trees over time. Further field research is required in a variety of forest settings to better define and understand recruitment and mortality rates as required in tree population dynamics models. Other factors that are important in determining root throw occurrence are topple rates of standing dead trees (consisting of trees that die during competition/thinning or that are killed by the wildfire), and whether or not the standing dead tree snaps at the bole or upheaves a root plate as it topples; once again, additional research is required in different forest settings to better understand this model parameter. When the factors outlined above are integrated together in our tree population dynamics model and sensitivity analysis is conducted, distinct temporal patterns and pulses of root throw and associated soil bioturbation emerge as the model cycles through generations of forest.

Post-fire tree establishment patterns at the alpine treeline ecotone: Mount Rainier National Park, Washington, USA

Science.gov (United States)

Kirk M. Stueve; Dawna L. Cerney; Regina M. Rochefort; Laurie L. Kurth

2009-01-01

We performed classification analysis of 1970 satellite imagery and 2003 aerial photography to delineate establishment. Local site conditions were calculated from a LIDAR-based DEM, ancillary climate data, and 1970 tree locations in a GIS. We used logistic regression on a spatially weighted landscape matrix to rank variables.

Methodology for reliability allocation based on fault tree analysis and dualistic contrast

Institute of Scientific and Technical Information of China (English)

TONG Lili; CAO Xuewu

2008-01-01

Reliability allocation is a difficult multi-objective optimization problem.This paper presents a methodology for reliability allocation that can be applied to determine the reliability characteristics of reactor systems or subsystems.The dualistic contrast,known as one of the most powerful tools for optimization problems,is applied to the reliability allocation model of a typical system in this article.And the fault tree analysis,deemed to be one of the effective methods of reliability analysis,is also adopted.Thus a failure rate allocation model based on the fault tree analysis and dualistic contrast is achieved.An application on the emergency diesel generator in the nuclear power plant is given to illustrate the proposed method.

SALP-PC, a computer program for fault tree analysis on personal computers

International Nuclear Information System (INIS)

Contini, S.; Poucet, A.

1987-01-01

The paper presents the main characteristics of the SALP-PC computer code for fault tree analysis. The program has been developed in Fortran 77 on an Olivetti M24 personal computer (IBM compatible) in order to reach a high degree of portability. It is composed of six processors implementing the different phases of the analysis procedure. This particular structure presents some advantages like, for instance, the restart facility and the possibility to develop an event tree analysis code. The set of allowed logical operators, i.e. AND, OR, NOT, K/N, XOR, INH, together with the possibility to define boundary conditions, make the SALP-PC code a powerful tool for risk assessment. (orig.)

Fault tree analysis of loss of cooling to a HALW storage tank

International Nuclear Information System (INIS)

Nomura, Yasushi

1992-01-01

Results of a scenario identification, a fault tree construction and an analysis for a loss of cooling accident in a High Activity Liquid Waste (HALW) tank of a typical model of reprocessing facility, is rendered together with considerations of the system reliability improvement by changing the model design. Model plant data, basic failure frequency data and a fault tree analysis named FTL have been introduced from NUKEM GmbH, Germany. They are throughly reviewed and reevaluated at JAERI, and improved to apply to Japanese facilities. A general systematic method for constructing fault trees is used to avoid missing scenarios, thus all of the 10 conceivable accident scenarios for 'HALW storage tank without cooling, HALW boiling' are identified, and a total failure frequency are calculated to be in the 90 % confidence interval of (1.1 ∼ 5.8) x 10 -6 /yr for the German model plant. (author)

Record of the Solar Activity and of Other Geophysical Phenomenons in Tree Ring

Science.gov (United States)

Rigozo, Nivaor Rodolfo

1999-01-01

Tree ring studies are usually used to determine or verify climatic factors which prevail in a given place or region and may cause tree ring width variations. Few studies are dedicated to the geophysical phenomena which may underlie these tree ring width variations. In order to look for periodicities which may be associated to the solar activity and/or to other geophysical phenomena which may influence tree ring growth, a new interactive image analysis method to measure tree ring width was developed and is presented here. This method makes use of a computer and a high resolution flatbed scanner; a program was also developed in Interactive Data Language (IDL 5.0) to study ring digitized images and transform them into time series. The main advantage of this method is the tree ring image interactive analysis without needing complex and high cost instrumentation. Thirty-nine samples were collected: 12 from Concordia - S. C., 9 from Canela - R. S., 14 from Sao Francisco de Paula - R. S., one from Nova Petropolis - R. S., 2 from Sao Martinho da Serra - R. S. e one from Chile. Fit functions are applied to ring width time series to obtain the best long time range trend (growth rate of every tree) curves and are eliminated through a standardization process that gives the tree ring index time series from which is performed spectral analysis by maximum entropy method and iterative regression. The results obtained show periodicities close to 11 yr, 22 yr Hale solar cycles and 5.5 yr for all sampling locations 52 yr and Gleissberg cycles for Concordia - S. C. and Chile samples. El Nino events were also observed with periods around 4 e 7 yr.

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

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

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...

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

Science.gov (United States)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

Science.gov (United States)

Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-04-01

To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

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.

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.

Non-stationary hydrologic frequency analysis using B-spline quantile regression

Science.gov (United States)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

SALP-3: A computer program for fault-tree analysis. Description and how-to-use. (Sensitivity analysis by list processing)

International Nuclear Information System (INIS)

Contini, S.; Astolfi, M.; Muysenberg, C.L. van den; Volta, G.

1979-01-01

The main characteristics and the how-to-use of the computer program SALP-3 for the analysis of coherent systems are described. The program is writen in PL/1 for the IBM/370-165. A syntactic analysis is made for the imput (fault-tree and data) and appropriate messages are supplied, should and error take place. The significant minimal cut sets (MCS) are searched by the use of algorithms based on the direct manipulation of the tree. The MCS, of whichever order, are supplied in output in order of importance with reference to a given probability threshold. The computer program SALP-3 represents only the intermediate results of a project whose objective is the implementation of a computer program for the analysis of both coherent and non-coherent structure functions, and, finally, for the automatic event tree analysis. The last part of the report illustrates the developments regarding the improvement in progress

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

A Rigorous, Compositional, and Extensible Framework for Dynamic Fault Tree Analysis

NARCIS (Netherlands)

Boudali, H.; Sandhu, R.; Crouzen, Pepijn; Stoelinga, Mariëlle Ida Antoinette

Fault trees (FT) are among the most prominent formalisms for reliability analysis of technical systems. Dynamic FTs extend FTs with support for expressing dynamic dependencies among components. The standard analysis vehicle for DFTs is state-based, and treats the model as a CTMC, a continuous-time

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

Science.gov (United States)

Kenah, Eben; Britton, Tom; Halloran, M. Elizabeth; Longini, Ira M.

2016-01-01

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. PMID:27070316

Selective Tree-ring Models: A Novel Method for Reconstructing Streamflow Using Tree Rings

Science.gov (United States)

Foard, M. B.; Nelson, A. S.; Harley, G. L.

2017-12-01

Surface water is among the most instrumental and vulnerable resources in the Northwest United States (NW). Recent observations show that overall water quantity is declining in streams across the region, while extreme flooding events occur more frequently. Historical streamflow models inform probabilities of extreme flow events (flood or drought) by describing frequency and duration of past events. There are numerous examples of tree-rings being utilized to reconstruct streamflow in the NW. These models confirm that tree-rings are highly accurate at predicting streamflow, however there are many nuances that limit their applicability through time and space. For example, most models predict streamflow from hydrologically altered rivers (e.g. dammed, channelized) which may hinder our ability to predict natural prehistoric flow. They also have a tendency to over/under-predict extreme flow events. Moreover, they often neglect to capture the changing relationships between tree-growth and streamflow over time and space. To address these limitations, we utilized national tree-ring and streamflow archives to investigate the relationships between the growth of multiple coniferous species and free-flowing streams across the NW using novel species-and site-specific streamflow models - a term we coined"selective tree-ring models." Correlation function analysis and regression modeling were used to evaluate the strengths and directions of the flow-growth relationships. Species with significant relationships in the same direction were identified as strong candidates for selective models. Temporal and spatial patterns of these relationships were examined using running correlations and inverse distance weighting interpolation, respectively. Our early results indicate that (1) species adapted to extreme climates (e.g. hot-dry, cold-wet) exhibit the most consistent relationships across space, (2) these relationships weaken in locations with mild climatic variability, and (3) some

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...

On macroeconomic values investigation using fuzzy linear regression analysis

Directory of Open Access Journals (Sweden)

Richard Pospíšil

2017-06-01

Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

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

Science.gov (United States)

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.

Structural system reliability calculation using a probabilistic fault tree analysis method

Science.gov (United States)

Torng, T. Y.; Wu, Y.-T.; Millwater, H. R.

1992-01-01

The development of a new probabilistic fault tree analysis (PFTA) method for calculating structural system reliability is summarized. The proposed PFTA procedure includes: developing a fault tree to represent the complex structural system, constructing an approximation function for each bottom event, determining a dominant sampling sequence for all bottom events, and calculating the system reliability using an adaptive importance sampling method. PFTA is suitable for complicated structural problems that require computer-intensive computer calculations. A computer program has been developed to implement the PFTA.

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.

Smoothed analysis of binary search trees

NARCIS (Netherlands)

Manthey, Bodo; Reischuk, Rüdiger

2007-01-01

Binary search trees are one of the most fundamental data structures. While the height of such a tree may be linear in the worst case, the average height with respect to the uniform distribution is only logarithmic. The exact value is one of the best studied problems in average-case complexity. We

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

Science.gov (United States)

Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H

2017-10-25

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

Science.gov (United States)

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

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 ...

Regression analysis understanding and building business and economic models using Excel

CERN Document Server

Wilson, J Holton

2012-01-01

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe

Prognostic classification index in Iranian colorectal cancer patients: Survival tree analysis

Directory of Open Access Journals (Sweden)

Amal Saki Malehi

2016-01-01

Full Text Available Aims: The aim of this study was to determine the prognostic index for separating homogenous subgroups in colorectal cancer (CRC patients based on clinicopathological characteristics using survival tree analysis. Methods: The current study was conducted at the Research Center of Gastroenterology and Liver Disease, Shahid Beheshti Medical University in Tehran, between January 2004 and January 2009. A total of 739 patients who already have been diagnosed with CRC based on pathologic report were enrolled. The data included demographic and clinical-pathological characteristic of patients. Tree-structured survival analysis based on a recursive partitioning algorithm was implemented to evaluate prognostic factors. The probability curves were calculated according to the Kaplan-Meier method, and the hazard ratio was estimated as an interest effect size. Result: There were 526 males (71.2% of these patients. The mean survival time (from diagnosis time was 42.46± (3.4. Survival tree identified three variables as main prognostic factors and based on their four prognostic subgroups was constructed. The log-rank test showed good separation of survival curves. Patients with Stage I-IIIA and treated with surgery as the first treatment showed low risk (median = 34 months whereas patients with stage IIIB, IV, and more than 68 years have the worse survival outcome (median = 9.5 months. Conclusion: Constructing the prognostic classification index via survival tree can aid the researchers to assess interaction between clinical variables and determining the cumulative effect of these variables on survival outcome.

Equations relating compacted and uncompacted live crown ratio for common tree species in the South

Science.gov (United States)

KaDonna C. Randolph

2010-01-01

Species-specific equations to predict uncompacted crown ratio (UNCR) from compacted live crown ratio (CCR), tree length, and stem diameter were developed for 24 species and 12 genera in the southern United States. Using data from the US Forest Service Forest Inventory and Analysis program, nonlinear regression was used to model UNCR with a logistic function. Model...

Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer

International Nuclear Information System (INIS)

Sarrach, D.; Strohner, P.

1986-01-01

The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated

Economic Cost-Analysis of the Impact of Container Size on Transplanted Tree Value

Directory of Open Access Journals (Sweden)

Lauren M. Garcia Chance

2017-04-01

Full Text Available The benefits and costs of varying container sizes have yet to be fully evaluated to determine which container size affords the most advantageous opportunity for consumers. To determine value of the tree following transplant, clonal replicates of Vitex agnus-castus L. [Chaste Tree], Acer rubrum L. var. drummondii (Hook. & Arn. ex Nutt. Sarg. [Drummond Red Maple], and Taxodium distichum (L. Rich. [Baldcypress] were grown under common conditions in each of five container sizes 3.5, 11.7, 23.3, 97.8 or 175.0 L, respectively (#1, 3, 7, 25 or 45. In June 2013, six trees of each container size and species were transplanted to a sandy clay loam field in College Station, Texas. To determine the increase in value over a two-year post-transplant period, height and caliper measurements were taken at the end of nursery production and again at the end of the second growing season in the field, October 2014. Utilizing industry standards, initial costs of materials and labor were then compared with the size of trees after two years. Replacement cost analysis after two growing seasons indicated a greater increase in value for 11.7 and 23.3 L trees compared to losses in value for some 175.0 L trees. In comparison with trees from larger containers, trees from smaller size containers experienced shorter establishment times and increased growth rates, thus creating a quicker return on investment for trees transplanted from the smaller container sizes.

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.

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2010-01-01

The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

Science.gov (United States)

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Treating experimental data of inverse kinetic method by unitary linear regression analysis

International Nuclear Information System (INIS)

Zhao Yusen; Chen Xiaoliang

2009-01-01

The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

Regression analysis of informative current status data with the additive hazards model.

Science.gov (United States)

Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Climate Response of Tree Radial Growth at Different Timescales in the Qinling Mountains.

Directory of Open Access Journals (Sweden)

Changfeng Sun

Full Text Available The analysis of the tree radial growth response to climate is crucial for dendroclimatological research. However, the response relationships between tree-ring indices and climatic factors at different timescales are not yet clear. In this study, the tree-ring width of Huashan pine (Pinus armandii from Huashan in the Qinling Mountains, north-central China, was used to explore the response differences of tree growth to climatic factors at daily, pentad (5 days, dekad (10 days and monthly timescales. Correlation function and linear regression analysis were applied in this paper. The tree-ring width showed a more sensitive response to daily and pentad climatic factors. With the timescale decreasing, the absolute value of the maximum correlation coefficient between the tree-ring data and precipitation increases as well as temperature (mean, minimum and maximum temperature. Compared to the other three timescales, pentad was more suitable for analysing the response of tree growth to climate. Relative to the monthly climate data, the association between the tree-ring data and the pentad climate data was more remarkable and accurate, and the reconstruction function based on the pentad climate was also more reliable and stable. We found that the major climatic factor limiting Huashan pine growth was the precipitation of pentads 20-35 (from April 6 to June 24 rather than the well-known April-June precipitation. The pentad was also proved to be a better timescale for analysing the climate and tree growth in the western and eastern Qinling Mountains. The formation of the earlywood density of Chinese pine (Pinus tabulaeformis from Shimenshan in western Qinling was mainly affected by the maximum temperature of pentads 28-32 (from May 16 to June 9. The maximum temperature of pentads 28-33 (from May 16 to June 14 was the major factor affecting the ring width of Chinese pine from Shirenshan in eastern Qinling.

Stem analysis program (GOAP for evaluating of increment and growth data at individual tree

Directory of Open Access Journals (Sweden)

Gafura Aylak Özdemir

2016-07-01

Full Text Available Stem analysis is a method evaluating in a detailed way data of increment and growth of individual tree at the past periods and widely used in various forestry disciplines. Untreated data of stem analysis consist of annual ring count and measurement procedures performed on cross sections taken from individual tree by section method. The evaluation of obtained this untreated data takes quite some time. Thus, a computer software was developed in this study to quickly and efficiently perform stem analysis. This computer software developed to evaluate untreated data of stem analysis as numerical and graphical was programmed as macro by utilizing Visual Basic for Application feature of MS Excel 2013 program currently the most widely used. In developed this computer software, growth height model is formed from two different approaches, individual tree volume depending on section method, cross-sectional area, increments of diameter, height and volume, volume increment percent and stem form factor at breast height are calculated depending on desired period lengths. This calculated values are given as table. Development of diameter, height, volume, increments of these variables, volume increment percent and stem form factor at breast height according to periodic age are given as chart. Stem model showing development of diameter, height and shape of individual tree in the past periods also can be taken from computer software as chart.

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

MULGRES: a computer program for stepwise multiple regression analysis

Science.gov (United States)

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Real-time regression analysis with deep convolutional neural networks

OpenAIRE

Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle

2018-01-01

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

Hydrochemical analysis of groundwater using a tree-based model

Science.gov (United States)

Litaor, M. Iggy; Brielmann, H.; Reichmann, O.; Shenker, M.

2010-06-01

SummaryHydrochemical indices are commonly used to ascertain aquifer characteristics, salinity problems, anthropogenic inputs and resource management, among others. This study was conducted to test the applicability of a binary decision tree model to aquifer evaluation using hydrochemical indices as input. The main advantage of the tree-based model compared to other commonly used statistical procedures such as cluster and factor analyses is the ability to classify groundwater samples with assigned probability and the reduction of a large data set into a few significant variables without creating new factors. We tested the model using data sets collected from headwater springs of the Jordan River, Israel. The model evaluation consisted of several levels of complexity, from simple separation between the calcium-magnesium-bicarbonate water type of karstic aquifers to the more challenging separation of calcium-sodium-bicarbonate water type flowing through perched and regional basaltic aquifers. In all cases, the model assigned measures for goodness of fit in the form of misclassification errors and singled out the most significant variable in the analysis. The model proceeded through a sequence of partitions providing insight into different possible pathways and changing lithology. The model results were extremely useful in constraining the interpretation of geological heterogeneity and constructing a conceptual flow model for a given aquifer. The tree model clearly identified the hydrochemical indices that were excluded from the analysis, thus providing information that can lead to a decrease in the number of routinely analyzed variables and a significant reduction in laboratory cost.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

Science.gov (United States)

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P valuelinear regression P value). The statistical power of CAT test decreased, while the result of linear regression analysis remained the same when population size was reduced by 100 times and AMI incidence rate remained unchanged. The two statistical methods have their advantages and disadvantages. It is necessary to choose statistical method according the fitting degree of data, or comprehensively analyze the results of two methods.

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.

The integration of expert-defined importance factors to enrich Bayesian Fault Tree Analysis

International Nuclear Information System (INIS)

Darwish, Molham; Almouahed, Shaban; Lamotte, Florent de

2017-01-01

This paper proposes an analysis of a hybrid Bayesian-Importance model for system designers to improve the quality of services related to Active Assisted Living Systems. The proposed model is based on two factors: failure probability measure of different service components and, an expert defined degree of importance that each component holds for the success of the corresponding service. The proposed approach advocates the integration of expert-defined importance factors to enrich the Bayesian Fault Tree Analysis (FTA) approach. The evaluation of the proposed approach is conducted using the Fault Tree Analysis formalism where the undesired state of a system is analyzed using Boolean logic mechanisms to combine a series of lower-level events.

Terahertz pulse imaging for tree-ring analysis: a preliminary study for dendrochronology applications

International Nuclear Information System (INIS)

Jackson, J B; Mourou, M; Whitaker, J F; Labaune, J; Mourou, G A; Duling, I N III; Williamson, S L; Lavier, C; Menu, M

2009-01-01

Time-domain terahertz reflection imaging is presented as a novel method of measuring otherwise inaccessible tree rings in wooden cultural heritage for the purpose of tree-ring crossdating. Axial and lateral two-dimensional terahertz images of obscured ring patterns are statistically validated with respect to their corresponding optical photographs via adapted dendrochronological methods. Results are compared to similar analysis of x-ray images of a wood specimen

Toward a theory of statistical tree-shape analysis

DEFF Research Database (Denmark)

Feragen, Aasa; Lo, Pechin Chien Pau; de Bruijne, Marleen

2013-01-01

In order to develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-shapes and study metrics on the shape space. This shape space has singularities, which correspond to topological transitions in the represented trees. We study two closely relat...

The evolution of GDP in USA using cyclic regression analysis

OpenAIRE

Catalin Angelo IOAN; Gina IOAN

2013-01-01

Based on the four major types of economic cycles (Kondratieff, Juglar, Kitchin, Kuznet), the paper aims to determine their actual length (for the U.S. economy) using cyclic regressions based on Fourier analysis.

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.

Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools

NARCIS (Netherlands)

Ruijters, Enno Jozef Johannes; Stoelinga, Mariëlle Ida Antoinette

2015-01-01

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software

Fault Tree Analysis: A survey of the state-of-the-art in modeling, analysis and tools

NARCIS (Netherlands)

Ruijters, Enno Jozef Johannes; Stoelinga, Mariëlle Ida Antoinette

2014-01-01

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software

Human Factors Reliability Analysis for Assuring Nuclear Safety Using Fuzzy Fault Tree

International Nuclear Information System (INIS)

Eisawy, E.A.-F. I.; Sallam, H.

2016-01-01

In order to ensure effective prevention of harmful events, the risk assessment process cannot ignore the role of humans in the dynamics of accidental events and thus the seriousness of the consequences that may derive from them. Human reliability analysis (HRA) involves the use of qualitative and quantitative methods to assess the human contribution to risk. HRA techniques have been developed in order to provide human error probability values associated with operators’ tasks to be included within the broader context of system risk assessment, and are aimed at reducing the probability of accidental events. Fault tree analysis (FTA) is a graphical model that displays the various combinations of equipment failures and human errors that can result in the main system failure of interest. FTA is a risk analysis technique to assess likelihood (in a probabilistic context) of an event. The objective data available to estimate the likelihood is often missing, and even if available, is subject to incompleteness and imprecision or vagueness. Without addressing incompleteness and imprecision in the available data, FTA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. To solve this problem, qualitative justification in the context of failure possibilities can be used as alternative for quantitative justification. In this paper, we introduce the approach of fuzzy reliability as solution for fault tree analysis drawbacks. A new fuzzy fault tree method is proposed for the analysis of human reliability based on fuzzy sets and fuzzy operations t-norms, co-norms, defuzzification, and fuzzy failure probability. (author)

Quantile regression for the statistical analysis of immunological data with many non-detects.

Science.gov (United States)

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Optimal choice of basis functions in the linear regression analysis

International Nuclear Information System (INIS)

Khotinskij, A.M.

1988-01-01

Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis.

Science.gov (United States)

De Mil, Tom; Vannoppen, Astrid; Beeckman, Hans; Van Acker, Joris; Van den Bulcke, Jan

2016-06-01

Disentangling tree growth requires more than ring width data only. Densitometry is considered a valuable proxy, yet laborious wood sample preparation and lack of dedicated software limit the widespread use of density profiling for tree ring analysis. An X-ray computed tomography-based toolchain of tree increment cores is presented, which results in profile data sets suitable for visual exploration as well as density-based pattern matching. Two temperate (Quercus petraea, Fagus sylvatica) and one tropical species (Terminalia superba) were used for density profiling using an X-ray computed tomography facility with custom-made sample holders and dedicated processing software. Density-based pattern matching is developed and able to detect anomalies in ring series that can be corrected via interactive software. A digital workflow allows generation of structure-corrected profiles of large sets of cores in a short time span that provide sufficient intra-annual density information for tree ring analysis. Furthermore, visual exploration of such data sets is of high value. The dated profiles can be used for high-resolution chronologies and also offer opportunities for fast screening of lesser studied tropical tree species. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2015-01-01

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

RELOSS, Reliability of Safety System by Fault Tree Analysis

International Nuclear Information System (INIS)

Allan, R.N.; Rondiris, I.L.; Adraktas, A.

1981-01-01

1 - Description of problem or function: Program RELOSS is used in the reliability/safety assessment of any complex system with predetermined operational logic in qualitative and (if required) quantitative terms. The program calculates the possible system outcomes following an abnormal operating condition and the probability of occurrence, if required. Furthermore, the program deduces the minimal cut or tie sets of the system outcomes and identifies the potential common mode failures. 4. Method of solution: The reliability analysis performed by the program is based on the event tree methodology. Using this methodology, the program develops the event tree of a system or a module of that system and relates each path of this tree to its qualitative and/or quantitative impact on specified system or module outcomes. If the system being analysed is subdivided into modules the program assesses each module in turn as described previously and then combines the module information to obtain results for the overall system. Having developed the event tree of a module or a system, the program identifies which paths lead or do not lead to various outcomes depending on whether the cut or the tie sets of the outcomes are required and deduces the corresponding sets. Furthermore the program identifies for a specific system outcome, the potential common mode failures and the cut or tie sets containing potential dependent failures of some components. 5. Restrictions on the complexity of the problem: The present dimensions of the program are as follows. They can however be easily modified: Maximum number of modules (equivalent components): 25; Maximum number of components in a module: 15; Maximum number of levels of parentheses in a logical statement: 10 Maximum number of system outcomes: 3; Maximum number of module outcomes: 2; Maximum number of points in time for which quantitative analysis is required: 5; Maximum order of any cut or tie set: 10; Maximum order of a cut or tie of any

Exploring predictors of scientific performance with decision tree analysis: The case of research excellence in early career mathematics

Energy Technology Data Exchange (ETDEWEB)

Lindahl, J.

2016-07-01

The purpose of this study was (1) to introduce the exploratory method of decision tree analysis as a complementary alternative to current confirmatory methods used in scientometric prediction studies of research performance; and (2) as an illustrative case, to explore predictors of future research excellence at the individual level among 493 early career mathematicians in the sub-field of number theory between 1999 and 2010. A conceptual introduction to decision tree analysis is provided including an overview of the main steps of the tree-building algorithm and the statistical method of cross-validation used to evaluate the performance of decision tree models. A decision tree analysis of 493 mathematicians was conducted to find useful predictors and important relationships between variables in the context of predicting research excellence. The results suggest that the number of prestige journal publications and a topically diverse output are important predictors of future research excellence. Researchers with no prestige journal publications are very unlikely to produce excellent research. Limitations of decision three analysis are discussed. (Author)

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

Science.gov (United States)

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Directory of Open Access Journals (Sweden)

Chang Jeong-Ho

2006-06-01

Full Text Available Abstract Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway

City of Berkeley, California Municipal Tree Resource Analysis

Science.gov (United States)

S.E. Maco; E.G. McPherson; J.R. Simpson; P.J. Peper; Q. Xiao

2005-01-01

Vibrant, renowned for its livability and cultural wealth, the city of Berkeley maintains trees as an integral component of the urban infrastructure. Research indicates that healthy trees can mitigate impacts associated with the built environment by reducing stormwater runoff, energy consumption, and air pollutants. Put simply, trees improve urban life, making Berkeley...

A method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data

DEFF Research Database (Denmark)

Mousavi, Seyed Nourollah

Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...

Systems analysis programs for hands-on integrated reliability evaluations (SAPHIRE) Version 5.0. Fault tree, event tree, and piping ampersand instrumentation diagram (FEP) editors reference manual: Volume 7

International Nuclear Information System (INIS)

McKay, M.K.; Skinner, N.L.; Wood, S.T.

1994-07-01

The Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) refers to a set of several microcomputer programs that were developed to create and analyze probabilistic risk assessments (PRAs), primarily for nuclear power plants. The Fault Tree, Event Tree, and Piping and Instrumentation Diagram (FEP) editors allow the user to graphically build and edit fault trees, and event trees, and piping and instrumentation diagrams (P and IDs). The software is designed to enable the independent use of the graphical-based editors found in the Integrated Reliability and Risk Assessment System (IRRAS). FEP is comprised of three separate editors (Fault Tree, Event Tree, and Piping and Instrumentation Diagram) and a utility module. This reference manual provides a screen-by-screen guide of the entire FEP System

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

Vasak, F.; Epstein, N.

1996-01-01

A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

Repeated measurements of blood lactate concentration as a prognostic marker in horses with acute colitis evaluated with classification and regression trees (CART) and random forest analysis

DEFF Research Database (Denmark)

Petersen, Mette Bisgaard; Tolver, Anders; Husted, Louise

2016-01-01

-off value of 7 mmol/L had a sensitivity of 0.66 and a specificity of 0.92 in predicting survival. In independent test data, the sensitivity was 0.69 and the specificity was 0.76. At the observed survival rate (38%), the optimal decision tree identified horses as non-survivors when the Lac at admission...... admitted with acute colitis (trees, as well as random...

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

Science.gov (United States)

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

Obesity and the decision tree: predictors of sustained weight loss after bariatric surgery.

Science.gov (United States)

Lee, Yi-Chih; Lee, Wei-Jei; Lin, Yang-Chu; Liew, Phui-Ly; Lee, Chia Ko; Lin, Steven C H; Lee, Tian-Shyung

2009-01-01

Bariatric surgery is the only long-lasting effective treatment to reduce body weight in morbid obesity. Previous literature in using data mining techniques to predict weight loss in obese patients who have undergone bariatric surgery is limited. This study used initial evaluations before bariatric surgery and data mining techniques to predict weight outcomes in morbidly obese patients seeking surgical treatment. 251 morbidly obese patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) with complete clinical data at baseline and at two years were enrolled for analysis. Decision Tree, Logistic Regression and Discriminant analysis technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. Two hundred fifty-one patients consisting of 68 men and 183 women was studied; with mean age 33 years. Mean +/- SD weight loss at 2 year was 74.5 +/- 16.4 kg. During two years of follow up, two-hundred and five (81.7%) patients had successful weight reduction while 46 (18.3%) were failed to reduce body weight. Operation methods, alanine transaminase (ALT), aspartate transaminase (AST), white blood cell counts (WBC), insulin and hemoglobin A1c (HbA1c) levels were the predictive factors for successful weight reduction. Decision tree model was a better classification models than traditional logistic regression and discriminant analysis in view of predictive accuracies.