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Publication DateApplied Optics, ISSN 1559-128X, 2015, Volume 54, Issue 4, pp. 802 - 815
Atmospheric parameters strongly affect the performance of free-space optical communication (FSOC) systems when the optical wave is propagating through the...
PATH | OPTICS | SPACE OPTICAL COMMUNICATIONS | IMAGES | Linear models (Statistics) | Usage | Atmospheric turbulence | Linear regression models | Analysis | Weather | Climatology | Free-space optical communication | Atmospheric models | Mathematical models | Climate models | Strength
PATH | OPTICS | SPACE OPTICAL COMMUNICATIONS | IMAGES | Linear models (Statistics) | Usage | Atmospheric turbulence | Linear regression models | Analysis | Weather | Climatology | Free-space optical communication | Atmospheric models | Mathematical models | Climate models | Strength
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Loading RightsStatistics in Medicine, ISSN 0277-6715, 03/2019, Volume 38, Issue 7, pp. 1262 - 1275
In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a...
minimum sample size | multivariable prediction model | R-squared | continuous outcome | linear regression | Statistics and Probability | Epidemiology | R‐squared | MEDICINE, RESEARCH & EXPERIMENTAL | LINEAR-REGRESSION | CONFIDENCE-INTERVALS | MEDICAL INFORMATICS | STATISTICS & PROBABILITY | ACCURACY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | STATISTICAL POWER | SHRINKAGE | MATHEMATICAL & COMPUTATIONAL BIOLOGY | LIKELIHOOD
minimum sample size | multivariable prediction model | R-squared | continuous outcome | linear regression | Statistics and Probability | Epidemiology | R‐squared | MEDICINE, RESEARCH & EXPERIMENTAL | LINEAR-REGRESSION | CONFIDENCE-INTERVALS | MEDICAL INFORMATICS | STATISTICS & PROBABILITY | ACCURACY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | STATISTICAL POWER | SHRINKAGE | MATHEMATICAL & COMPUTATIONAL BIOLOGY | LIKELIHOOD
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Loading RightsIEEE Transactions on Software Engineering, ISSN 0098-5589, 05/2011, Volume 37, Issue 3, pp. 356 - 370
BACKGROUND - Predicting defect-prone software components is an economically important activity and so has received a good deal of attention. However, making...
Training | Buildings | Software defect prediction | Training data | Predictive models | Prediction algorithms | Software | Data models | scheme evaluation | software defect-proneness prediction | machine learning | Software defect-proneness prediction | Scheme evaluation | Machine learning | INSPECTION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | CAPTURE-RECAPTURE MODELS | STATIC CODE ATTRIBUTES | SELECTION | ENGINEERING, ELECTRICAL & ELECTRONIC | Technology application | Electronic data processing | Usage | Innovations | Linear models (Statistics) | Computer programming services | Linear regression models | Simulation methods | Studies | Artificial intelligence | Analysis | Predictions | Defects | Software engineering | Economics | Learning | Construction | Data sets | Mathematical models | Computer programs
Training | Buildings | Software defect prediction | Training data | Predictive models | Prediction algorithms | Software | Data models | scheme evaluation | software defect-proneness prediction | machine learning | Software defect-proneness prediction | Scheme evaluation | Machine learning | INSPECTION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | CAPTURE-RECAPTURE MODELS | STATIC CODE ATTRIBUTES | SELECTION | ENGINEERING, ELECTRICAL & ELECTRONIC | Technology application | Electronic data processing | Usage | Innovations | Linear models (Statistics) | Computer programming services | Linear regression models | Simulation methods | Studies | Artificial intelligence | Analysis | Predictions | Defects | Software engineering | Economics | Learning | Construction | Data sets | Mathematical models | Computer programs
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Loading RightsJournal of the American Statistical Association, ISSN 0162-1459, 03/2006, Volume 101, Issue 473, pp. 119 - 137
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory...
Regression | Survival analysis | Gene expression | Microarray | Datasets | Theory and Methods | Linear regression | Genes | Least squares | Eigenvalues | Coordinate systems | Eigenvectors | Regression analysis | Modeling | SURVIVAL | GENE-EXPRESSION DATA | SUFFICIENT DIMENSION REDUCTION | microarray | survival analysis | regression | LASSO | DECOMPOSITION | STATISTICS & PROBABILITY | SELECTION | CANCER | gene expression | Case studies | Principal components analysis | Research | Analysis | Human genetics
Regression | Survival analysis | Gene expression | Microarray | Datasets | Theory and Methods | Linear regression | Genes | Least squares | Eigenvalues | Coordinate systems | Eigenvectors | Regression analysis | Modeling | SURVIVAL | GENE-EXPRESSION DATA | SUFFICIENT DIMENSION REDUCTION | microarray | survival analysis | regression | LASSO | DECOMPOSITION | STATISTICS & PROBABILITY | SELECTION | CANCER | gene expression | Case studies | Principal components analysis | Research | Analysis | Human genetics
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Loading RightsChemistry – A European Journal, ISSN 0947-6539, 10/2018, Volume 24, Issue 59, pp. 15781 - 15785
A new electrochemical iodine(III)‐mediated cyclisation reaction for the synthesis of 4‐(2,2,2‐trifluoroethoxy)isochroman‐1‐ones is presented. Based on this...
iodoarenes | electrochemistry | hypercoordinate iodine | multivariate linear regression | iodine(III) | REACTION PERFORMANCE | OXIDATION | CATALYSIS | FLUORINATION | CHEMISTRY, MULTIDISCIPLINARY | PRODUCTS | ROBUSTNESS SCREEN | CHEMISTRY | HYPERVALENT IODINE REAGENTS | Electrochemistry | Electrochemical reactions | Analysis | Regression analysis | Chemical synthesis | Design of experiments | Substrates | Iodine
iodoarenes | electrochemistry | hypercoordinate iodine | multivariate linear regression | iodine(III) | REACTION PERFORMANCE | OXIDATION | CATALYSIS | FLUORINATION | CHEMISTRY, MULTIDISCIPLINARY | PRODUCTS | ROBUSTNESS SCREEN | CHEMISTRY | HYPERVALENT IODINE REAGENTS | Electrochemistry | Electrochemical reactions | Analysis | Regression analysis | Chemical synthesis | Design of experiments | Substrates | Iodine
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Loading RightsThe Annals of Statistics, ISSN 0090-5364, 10/2006, Volume 34, Issue 5, pp. 2159 - 2179
There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case...
Density estimation | Error rates | Data analysis | Nonparametric Statistical Analysis | Linear regression | Eigenvalues | Mathematical functions | Regression analysis | Data smoothing | Estimators | Consistent estimators | Minimax | Intercept | Eigenvector | Eigenfunction | Eigenvalue | Principal components analysis | Slope | Dimension reduction | Covariance | Smoothing | Optimal convergence rate | Bootstrap | Spectral decomposition | Rate of convergence | Functional data analysis | spectral decomposition | LOGISTIC-REGRESSION | eigenfunction | rate of convergence | functional data analysis | STATISTICS & PROBABILITY | covariance | optimal convergence rate | bootstrap | slope | dimension reduction | CURVES | principal components analysis | eigenvector | MODELS | intercept | PRINCIPAL-COMPONENTS-ANALYSIS | smoothing | eigenvalue | minimax | 62J05 | 62G20
Density estimation | Error rates | Data analysis | Nonparametric Statistical Analysis | Linear regression | Eigenvalues | Mathematical functions | Regression analysis | Data smoothing | Estimators | Consistent estimators | Minimax | Intercept | Eigenvector | Eigenfunction | Eigenvalue | Principal components analysis | Slope | Dimension reduction | Covariance | Smoothing | Optimal convergence rate | Bootstrap | Spectral decomposition | Rate of convergence | Functional data analysis | spectral decomposition | LOGISTIC-REGRESSION | eigenfunction | rate of convergence | functional data analysis | STATISTICS & PROBABILITY | covariance | optimal convergence rate | bootstrap | slope | dimension reduction | CURVES | principal components analysis | eigenvector | MODELS | intercept | PRINCIPAL-COMPONENTS-ANALYSIS | smoothing | eigenvalue | minimax | 62J05 | 62G20
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Loading RightsStatistical Science, ISSN 0883-4237, 11/2007, Volume 22, Issue 4, pp. 477 - 505
We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including...
Componentwise operations | Degrees of freedom | Linear regression | Least squares | Machine learning | Data smoothing | Binomials | Linear models | Modeling | Estimators | Survival analysis | Software | Generalized additive models | Generalized linear models | Gradient boosting | Variable selection | generalized linear models | software | STATISTICAL VIEW | STATISTICS & PROBABILITY | LINEAR-MODELS | MONOTONIC REGRESSION | GENE-EXPRESSION DATA | CONSISTENCY | GRADIENT DESCENT | gradient boosting | generalized additive models | survival analysis | ADDITIVE LOGISTIC-REGRESSION | LASSO | TUMOR CLASSIFICATION | variable selection | Statistics - Methodology
Componentwise operations | Degrees of freedom | Linear regression | Least squares | Machine learning | Data smoothing | Binomials | Linear models | Modeling | Estimators | Survival analysis | Software | Generalized additive models | Generalized linear models | Gradient boosting | Variable selection | generalized linear models | software | STATISTICAL VIEW | STATISTICS & PROBABILITY | LINEAR-MODELS | MONOTONIC REGRESSION | GENE-EXPRESSION DATA | CONSISTENCY | GRADIENT DESCENT | gradient boosting | generalized additive models | survival analysis | ADDITIVE LOGISTIC-REGRESSION | LASSO | TUMOR CLASSIFICATION | variable selection | Statistics - Methodology
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Loading RightsRenewable and Sustainable Energy Reviews, ISSN 1364-0321, 07/2015, Volume 47, pp. 332 - 343
The considerable amount of energy consumption associated to the residential sector justifies and supports energy consumption modeling efforts. Among the three...
Energy consumption | Linear regression | Energy regression models | Residential buildings | GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY | CONDITIONAL DEMAND | ENERGY & FUELS | PERFORMANCE | SECTOR | ELECTRICITY CONSUMPTION | MODELS | FEEDBACK | BUILDINGS | Analysis | Regression analysis | Energy use | Radiation | Information management | Mechanical engineering
Energy consumption | Linear regression | Energy regression models | Residential buildings | GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY | CONDITIONAL DEMAND | ENERGY & FUELS | PERFORMANCE | SECTOR | ELECTRICITY CONSUMPTION | MODELS | FEEDBACK | BUILDINGS | Analysis | Regression analysis | Energy use | Radiation | Information management | Mechanical engineering
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Loading RightsJournal of the American Statistical Association, ISSN 0162-1459, 09/2004, Volume 99, Issue 467, pp. 619 - 632
Having constructed a data-based estimation rule, perhaps a logistic regression or a classification tree, the statistician would like to know its performance as...
Rao-Blackwellization | Degrees of freedom | Parametric bootstrap | SURE | Nonparametric estimates | Statistical variance | Penalty function | Covariance | Theory and Methods | Linear regression | Polynomials | Modeling | Parametric models | Estimation methods | Optimism | REGRESSION | nonparametric estimates | BOOTSTRAP | degrees of freedom | C-p | parametric bootstrap | STATISTICS & PROBABILITY | Error analysis (Mathematics) | Methods | Analysis of variance
Rao-Blackwellization | Degrees of freedom | Parametric bootstrap | SURE | Nonparametric estimates | Statistical variance | Penalty function | Covariance | Theory and Methods | Linear regression | Polynomials | Modeling | Parametric models | Estimation methods | Optimism | REGRESSION | nonparametric estimates | BOOTSTRAP | degrees of freedom | C-p | parametric bootstrap | STATISTICS & PROBABILITY | Error analysis (Mathematics) | Methods | Analysis of variance
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Loading Rights1982, 2nd ed. --, ISBN 0030417600, x, 822
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A Systematic Literature Review on Fault Prediction Performance in Software Engineering
IEEE Transactions on Software Engineering, ISSN 0098-5589, 11/2012, Volume 38, Issue 6, pp. 1276 - 1304
Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software....
Software testing | Fault diagnosis | Analytical models | Systematics | software fault prediction | Predictive models | Data models | Context modeling | Systematic literature review | QUALITY | EMPIRICAL VALIDATION | STATIC CODE ATTRIBUTES | RELIABILITY | IDENTIFICATION | QUANTITATIVE-ANALYSIS | DESIGN METRICS | ENGINEERING, ELECTRICAL & ELECTRONIC | COMPUTER SCIENCE, SOFTWARE ENGINEERING | DEFECT-PREDICTION | MODELS | PRONE CLASSES | Usage | Analysis | Linear models (Statistics) | Fault location (Engineering) | Linear regression models | Mathematical optimization | Methods | Software engineering
Software testing | Fault diagnosis | Analytical models | Systematics | software fault prediction | Predictive models | Data models | Context modeling | Systematic literature review | QUALITY | EMPIRICAL VALIDATION | STATIC CODE ATTRIBUTES | RELIABILITY | IDENTIFICATION | QUANTITATIVE-ANALYSIS | DESIGN METRICS | ENGINEERING, ELECTRICAL & ELECTRONIC | COMPUTER SCIENCE, SOFTWARE ENGINEERING | DEFECT-PREDICTION | MODELS | PRONE CLASSES | Usage | Analysis | Linear models (Statistics) | Fault location (Engineering) | Linear regression models | Mathematical optimization | Methods | Software engineering
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Loading RightsEnergy, ISSN 0360-5442, 2010, Volume 35, Issue 2, pp. 512 - 517
In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model...
Energy consumption | Artificial neural networks | Prediction | Linear regression method | Installed capacity | Multilayer perceptron | Support vector machine method | Gross domestic product | SUPPORT VECTOR MACHINES | LOAD | ENERGY & FUELS | ALGORITHM | ELECTRICITY CONSUMPTION | MODEL | DEMAND | THERMODYNAMICS | TEMPERATURE
Energy consumption | Artificial neural networks | Prediction | Linear regression method | Installed capacity | Multilayer perceptron | Support vector machine method | Gross domestic product | SUPPORT VECTOR MACHINES | LOAD | ENERGY & FUELS | ALGORITHM | ELECTRICITY CONSUMPTION | MODEL | DEMAND | THERMODYNAMICS | TEMPERATURE
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Loading RightsJournal of Hazardous Materials, ISSN 0304-3894, 08/2014, Volume 278, pp. 320 - 329
A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the...
Ionic liquids | Support vector machine (SVM) | QSAR | Multiple linear regression (MLR) | Toxicity | SUPPORT VECTOR MACHINES | PHYSICAL-PROPERTIES | MELTING-POINTS | PHYSICOCHEMICAL PROPERTIES | ENVIRONMENTAL SCIENCES | VIBRIO-FISCHERI | NEURAL-NETWORKS | ENGINEERING, ENVIRONMENTAL | QSPR CORRELATION | STRUCTURE-PROPERTY RELATIONSHIP | DAPHNIA-MAGNA | CELL-LINE | Models, Theoretical | Animals | Ionic Liquids - toxicity | Cell Line, Tumor | Rats | Linear Models | Support Vector Machine | Ionic Liquids - chemistry | Quantitative Structure-Activity Relationship | Databases, Factual | Analysis | Methods | Databases | Index Medicus | Support vector machines | Algorithms | Regression | Atomic properties | Mathematical models
Ionic liquids | Support vector machine (SVM) | QSAR | Multiple linear regression (MLR) | Toxicity | SUPPORT VECTOR MACHINES | PHYSICAL-PROPERTIES | MELTING-POINTS | PHYSICOCHEMICAL PROPERTIES | ENVIRONMENTAL SCIENCES | VIBRIO-FISCHERI | NEURAL-NETWORKS | ENGINEERING, ENVIRONMENTAL | QSPR CORRELATION | STRUCTURE-PROPERTY RELATIONSHIP | DAPHNIA-MAGNA | CELL-LINE | Models, Theoretical | Animals | Ionic Liquids - toxicity | Cell Line, Tumor | Rats | Linear Models | Support Vector Machine | Ionic Liquids - chemistry | Quantitative Structure-Activity Relationship | Databases, Factual | Analysis | Methods | Databases | Index Medicus | Support vector machines | Algorithms | Regression | Atomic properties | Mathematical models
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Loading Rights1977, ISBN 047101656X, xiii, 188
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New Zealand Journal of Forestry Science, ISSN 0048-0134, 12/2018, Volume 48, Issue 1, pp. 1 - 17
In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and...
Life Sciences | Machine learning algorithms | Forestry | Artificial neural networks | Random forest | Multiple linear regression | Support vector machine | Forest inventory | REGRESSION | FOREST INVENTORY DATA | REMOTE-SENSING DATA | PLANTATION FOREST | RESOLUTION | ABOVEGROUND BIOMASS | ARTIFICIAL NEURAL-NETWORK | TIME-SERIES | MACHINE | FORESTRY | AIRBORNE LIDAR | Spatial analysis (Statistics) | Eucalyptus | Forest surveys | Forest mapping | Forests | Supply chains | Spatial discrimination | Forest management | Remote sensing | Forest productivity | Dimensional analysis | Decision trees | Productivity | Wood | Decision making | Plantations | Spectral bands | Landsat | Data processing | Learning theory | Regression analysis | Kriging | Satellite imagery | Support vector machines | Learning algorithms | Satellites | Neural networks | Computer applications | Machine learning | Software | Landsat satellites
Life Sciences | Machine learning algorithms | Forestry | Artificial neural networks | Random forest | Multiple linear regression | Support vector machine | Forest inventory | REGRESSION | FOREST INVENTORY DATA | REMOTE-SENSING DATA | PLANTATION FOREST | RESOLUTION | ABOVEGROUND BIOMASS | ARTIFICIAL NEURAL-NETWORK | TIME-SERIES | MACHINE | FORESTRY | AIRBORNE LIDAR | Spatial analysis (Statistics) | Eucalyptus | Forest surveys | Forest mapping | Forests | Supply chains | Spatial discrimination | Forest management | Remote sensing | Forest productivity | Dimensional analysis | Decision trees | Productivity | Wood | Decision making | Plantations | Spectral bands | Landsat | Data processing | Learning theory | Regression analysis | Kriging | Satellite imagery | Support vector machines | Learning algorithms | Satellites | Neural networks | Computer applications | Machine learning | Software | Landsat satellites
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Loading RightsEnvironmental Toxicology and Chemistry, ISSN 0730-7268, 02/2015, Volume 34, Issue 2, pp. 235 - 246
The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total...
Soil | Metal/metalloid | Mid‐infrared spectroscopy | Anions | Solid‐solution partitioning coefficients (Kd) | Partial least squares regression | Solid-solution partitioning coefficients (K | Mid-infrared spectroscopy | BORON | Solid-solution partitioning coefficients (K-d) | Metal | ADSORPTION | ENVIRONMENTAL SCIENCES | metalloid | METALS | AVAILABILITY | CALCIUM | AGRICULTURAL SOILS | TOXICOLOGY | ALFALFA | RANGE | Boric Acids - analysis | Geography | Models, Theoretical | Europe | Linear Models | Calibration | Soil Pollutants - analysis | Spectrophotometry, Infrared - methods | Solutions | Soil - chemistry | Anions - analysis | Least-Squares Analysis | Agriculture | Hydrogen-Ion Concentration | Soils | Infrared spectroscopy | Boric acid | Index Medicus | Partitioning | Regression | Boric acids | Mathematical models | Drift | Reflectance
Soil | Metal/metalloid | Mid‐infrared spectroscopy | Anions | Solid‐solution partitioning coefficients (Kd) | Partial least squares regression | Solid-solution partitioning coefficients (K | Mid-infrared spectroscopy | BORON | Solid-solution partitioning coefficients (K-d) | Metal | ADSORPTION | ENVIRONMENTAL SCIENCES | metalloid | METALS | AVAILABILITY | CALCIUM | AGRICULTURAL SOILS | TOXICOLOGY | ALFALFA | RANGE | Boric Acids - analysis | Geography | Models, Theoretical | Europe | Linear Models | Calibration | Soil Pollutants - analysis | Spectrophotometry, Infrared - methods | Solutions | Soil - chemistry | Anions - analysis | Least-Squares Analysis | Agriculture | Hydrogen-Ion Concentration | Soils | Infrared spectroscopy | Boric acid | Index Medicus | Partitioning | Regression | Boric acids | Mathematical models | Drift | Reflectance
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