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Publication Date2006, 1. Aufl., ISBN 0470014962, x, 366
Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition...
Plan d'expérience | Multilevel models (Statistics) | Experimental design | Analyse de régression | Regression analysis | Analyse de variance | Analysis of variance | Modèles multiniveaux (Statistique)
Plan d'expérience | Multilevel models (Statistics) | Experimental design | Analyse de régression | Regression analysis | Analyse de variance | Analysis of variance | Modèles multiniveaux (Statistique)
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2014, Second edition., ISBN 9781119945499, xiii, 487 pages
Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is...
Multilevel models (Statistics) | Regression analysis | Experimental design | Analysis of variance
Multilevel models (Statistics) | Regression analysis | Experimental design | Analysis of variance
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2007, Analytical methods for social research, ISBN 0521867061, xxii, 625
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2007, Methodology in the social sciences, ISBN 159385191X, xxvii, 355
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Biodiversity and Conservation, ISSN 0960-3115, 5/2000, Volume 9, Issue 5, pp. 655 - 671
In many large-scale conservation or ecological problems where experiments are intractable or unethical, regression methods are used to attempt to gauge the...
Life Sciences | hierarchical partitioning | inference | criteria | model selection | multiple regression | Evolutionary Biology | Tree Biology | Plant Sciences | model artefacts | Inference | Criteria | Model selection | Model artefacts | Hierarchical partitioning | Multiple regression | LINEAR-REGRESSION | LANDSCAPE | MULTIPLE-REGRESSION | COASTAL SOUTHERN CALIFORNIA | ABUNDANCE | SUBSET REGRESSION | ENVIRONMENTAL SCIENCES | HABITAT | SPECIES RICHNESS | ECOLOGY | VARIABLES | SELECTION | BIODIVERSITY CONSERVATION | Geography | Modelling (-General-) | Regression Analysis | Modelling (-Specific Names-II) | Ecology | Modelling (- Specific Names-I) | Modelling (-Specific Names-I) | Studies | Regression analysis
Life Sciences | hierarchical partitioning | inference | criteria | model selection | multiple regression | Evolutionary Biology | Tree Biology | Plant Sciences | model artefacts | Inference | Criteria | Model selection | Model artefacts | Hierarchical partitioning | Multiple regression | LINEAR-REGRESSION | LANDSCAPE | MULTIPLE-REGRESSION | COASTAL SOUTHERN CALIFORNIA | ABUNDANCE | SUBSET REGRESSION | ENVIRONMENTAL SCIENCES | HABITAT | SPECIES RICHNESS | ECOLOGY | VARIABLES | SELECTION | BIODIVERSITY CONSERVATION | Geography | Modelling (-General-) | Regression Analysis | Modelling (-Specific Names-II) | Ecology | Modelling (- Specific Names-I) | Modelling (-Specific Names-I) | Studies | Regression analysis
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Loading RightsJournal of Multivariate Analysis, ISSN 0047-259X, 2004, Volume 91, Issue 1, pp. 74 - 89
The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a...
Penalty methods | L-statistics | Random effects | Robust estimation | Shrinkage | Quantile regression | Hierarchical models | random effects | robust estimation | ESTIMATORS | LASSO | quantile regression | hierarchical models | STATISTICS & PROBABILITY | penalty methods | LINEAR-MODEL | shrinkage | Quantile regression Penalty methods Shrinkage L-statistics Random effects Robust estimation Hierarchical models
Penalty methods | L-statistics | Random effects | Robust estimation | Shrinkage | Quantile regression | Hierarchical models | random effects | robust estimation | ESTIMATORS | LASSO | quantile regression | hierarchical models | STATISTICS & PROBABILITY | penalty methods | LINEAR-MODEL | shrinkage | Quantile regression Penalty methods Shrinkage L-statistics Random effects Robust estimation Hierarchical models
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Loading RightsStatistics and Computing, ISSN 0960-3174, 3/2014, Volume 24, Issue 2, pp. 223 - 238
Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear...
Statistics and Computing/Statistics Programs | Multiplicative random effects | P-splines | Artificial Intelligence (incl. Robotics) | MCMC | Statistical Theory and Methods | Bayesian hierarchical models | Gaussian random fields | Statistics | Probability and Statistics in Computer Science | Markov random fields | BINARY | STATISTICS & PROBABILITY | MODELS | SPLINES | COMPUTER SCIENCE, THEORY & METHODS | SELECTION | BAYESIAN-INFERENCE | Markov processes | Monte Carlo method | Models | Analysis | Heterogeneity | Additives | Hierarchies | Computer simulation | Multilevel | Regression | Nonlinearity | Mathematical models
Statistics and Computing/Statistics Programs | Multiplicative random effects | P-splines | Artificial Intelligence (incl. Robotics) | MCMC | Statistical Theory and Methods | Bayesian hierarchical models | Gaussian random fields | Statistics | Probability and Statistics in Computer Science | Markov random fields | BINARY | STATISTICS & PROBABILITY | MODELS | SPLINES | COMPUTER SCIENCE, THEORY & METHODS | SELECTION | BAYESIAN-INFERENCE | Markov processes | Monte Carlo method | Models | Analysis | Heterogeneity | Additives | Hierarchies | Computer simulation | Multilevel | Regression | Nonlinearity | Mathematical models
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Loading RightsMathematical Biosciences, ISSN 0025-5564, 09/2018, Volume 303, pp. 75 - 82
Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance....
Adaptive lasso | Gibbs sampler | Hierarchical model | Bayesian inference | Linear regression | ELASTIC NET | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | REGULARIZATION | VARIABLE SELECTION | ORACLE PROPERTIES | Analysis | Algorithms | Information management
Adaptive lasso | Gibbs sampler | Hierarchical model | Bayesian inference | Linear regression | ELASTIC NET | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | REGULARIZATION | VARIABLE SELECTION | ORACLE PROPERTIES | Analysis | Algorithms | Information management
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Loading RightsBayesian Analysis, ISSN 1936-0975, 2010, Volume 5, Issue 2, pp. 369 - 412
Penalized regression methods for simultaneous variables election and coefficient estimation, especially those based on the lasso of Tibshirani (1996), have...
Geometric ergodicity | Gibbs Sampling | Hierarchical models | Variable selection | Variable Selection | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | ORACLE PROPERTIES | Hierarchical Models | DISTRIBUTIONS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | BOOTSTRAP | MODEL SELECTION | ESTIMATORS | Geometric Ergodicity | GIBBS
Geometric ergodicity | Gibbs Sampling | Hierarchical models | Variable selection | Variable Selection | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | ORACLE PROPERTIES | Hierarchical Models | DISTRIBUTIONS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | BOOTSTRAP | MODEL SELECTION | ESTIMATORS | Geometric Ergodicity | GIBBS
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Loading RightsJournal of Statistical Software, ISSN 1548-7660, 2006, Volume 17, Issue 1, pp. 1 - 27
Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different...
Relative importance | Relaimpo | Variance decomposition | Hier.part | Hierarchical partitioning | Linear model | hier.part | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | hierarchical partitioning | relaimpo | R-2 | PREDICTORS | MULTIPLE-REGRESSION | STATISTICS & PROBABILITY | linear model | relative importance | variance decomposition
Relative importance | Relaimpo | Variance decomposition | Hier.part | Hierarchical partitioning | Linear model | hier.part | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | hierarchical partitioning | relaimpo | R-2 | PREDICTORS | MULTIPLE-REGRESSION | STATISTICS & PROBABILITY | linear model | relative importance | variance decomposition
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Loading RightsStatistics in Medicine, ISSN 0277-6715, 09/2017, Volume 36, Issue 20, pp. 3257 - 3277
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common....
multilevel analysis | clustered data | hierarchical models | logistic regression | multilevel models | MORTALITY | MEDICINE, RESEARCH & EXPERIMENTAL | BRIEF CONCEPTUAL TUTORIAL | LINEAR MIXED MODELS | CONTEXTUAL PHENOMENA | MEDICAL INFORMATICS | STATISTICS & PROBABILITY | SOCIAL EPIDEMIOLOGY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | EXPLAINED VARIATION | MATHEMATICAL & COMPUTATIONAL BIOLOGY | VARIANCE | INDIVIDUAL HEALTH | CLUSTER | Biostatistics | Data Interpretation, Statistical | Myocardial Infarction - mortality | Analysis of Variance | Humans | Logistic Models | Odds Ratio | Cluster Analysis | Tutorial in Biostatistics | Hälsovetenskap | Medical and Health Sciences | Medicin och hälsovetenskap | Public Health, Global Health, Social Medicine and Epidemiology | Mathematics | Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi | Journal Article | Naturvetenskap | Natural Sciences | Matematik | Sannolikhetsteori och statistik | Probability Theory and Statistics | Health Sciences
multilevel analysis | clustered data | hierarchical models | logistic regression | multilevel models | MORTALITY | MEDICINE, RESEARCH & EXPERIMENTAL | BRIEF CONCEPTUAL TUTORIAL | LINEAR MIXED MODELS | CONTEXTUAL PHENOMENA | MEDICAL INFORMATICS | STATISTICS & PROBABILITY | SOCIAL EPIDEMIOLOGY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | EXPLAINED VARIATION | MATHEMATICAL & COMPUTATIONAL BIOLOGY | VARIANCE | INDIVIDUAL HEALTH | CLUSTER | Biostatistics | Data Interpretation, Statistical | Myocardial Infarction - mortality | Analysis of Variance | Humans | Logistic Models | Odds Ratio | Cluster Analysis | Tutorial in Biostatistics | Hälsovetenskap | Medical and Health Sciences | Medicin och hälsovetenskap | Public Health, Global Health, Social Medicine and Epidemiology | Mathematics | Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi | Journal Article | Naturvetenskap | Natural Sciences | Matematik | Sannolikhetsteori och statistik | Probability Theory and Statistics | Health Sciences
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Loading RightsJournal of Biomechanics, ISSN 0021-9290, 2016, Volume 49, Issue 9, pp. 1649 - 1657
Abstract Recent advances in technology have allowed for the measurement of dynamic processes (re-alignment, crimp, deformation, sliding), but only a limited...
Physical Medicine and Rehabilitation | Hierarchical | Regression | Mechanics | Supraspinatus tendon | MATRIX | FIBRILLOGENESIS IN-SITU | COLLAGEN CROSS-LINKING | ENGINEERING, BIOMEDICAL | MECHANICAL-PROPERTIES | PATELLAR TENDON | SHEAR | BIOPHYSICS | FIBER RE-ALIGNMENT | FIBRIL SEGMENTS | STRESS | AGE | Regression Analysis | Biomechanical Phenomena | Animals | Models, Biological | Collagen - physiology | Tendons - physiology | Rotator Cuff - anatomy & histology | Rotator Cuff - physiology | Collagen - genetics | Tendons - anatomy & histology | Mice, Knockout | Collagen | Analysis | Models | Mechanical properties | Medical colleges | Mediation | Studies | Biomechanics | Viscoelasticity | Load | Microscopy | Morphology | Extracellular matrix | Software | Deformation | Dynamic mechanical properties | Dynamics | Insertion | Sliding | Mathematical models | Tendons | mechanics | supraspinatus tendon | regression | hierarchical
Physical Medicine and Rehabilitation | Hierarchical | Regression | Mechanics | Supraspinatus tendon | MATRIX | FIBRILLOGENESIS IN-SITU | COLLAGEN CROSS-LINKING | ENGINEERING, BIOMEDICAL | MECHANICAL-PROPERTIES | PATELLAR TENDON | SHEAR | BIOPHYSICS | FIBER RE-ALIGNMENT | FIBRIL SEGMENTS | STRESS | AGE | Regression Analysis | Biomechanical Phenomena | Animals | Models, Biological | Collagen - physiology | Tendons - physiology | Rotator Cuff - anatomy & histology | Rotator Cuff - physiology | Collagen - genetics | Tendons - anatomy & histology | Mice, Knockout | Collagen | Analysis | Models | Mechanical properties | Medical colleges | Mediation | Studies | Biomechanics | Viscoelasticity | Load | Microscopy | Morphology | Extracellular matrix | Software | Deformation | Dynamic mechanical properties | Dynamics | Insertion | Sliding | Mathematical models | Tendons | mechanics | supraspinatus tendon | regression | hierarchical
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Full Text
boral – Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in r
Methods in Ecology and Evolution, ISSN 2041-210X, 06/2016, Volume 7, Issue 6, pp. 744 - 750
Summary Model‐based methods have emerged as a powerful approach for analysing multivariate abundance data in community ecology. Key applications include...
generalized linear models | community composition | hierarchical models | species interaction | latent variable model | Bayesian inference | MODELS | ENVIRONMENT | ECOLOGY | TRAITS | Bayesian analysis | Monte Carlo simulation | Markov analysis | Monte Carlo method | Correlation | Computer simulation | Ordination | Boral | Markov chains | Data processing | Abundance | Regression analysis | Indicator species | Multivariate analysis | Generalized linear models | Statistical models | Ecological monitoring | Modelling | Mathematical models
generalized linear models | community composition | hierarchical models | species interaction | latent variable model | Bayesian inference | MODELS | ENVIRONMENT | ECOLOGY | TRAITS | Bayesian analysis | Monte Carlo simulation | Markov analysis | Monte Carlo method | Correlation | Computer simulation | Ordination | Boral | Markov chains | Data processing | Abundance | Regression analysis | Indicator species | Multivariate analysis | Generalized linear models | Statistical models | Ecological monitoring | Modelling | Mathematical models
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Loading RightsExpert Systems With Applications, ISSN 0957-4174, 2008, Volume 34, Issue 1, pp. 366 - 374
In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective...
Logistic regression | Neural networks | Hierarchical cluster analysis | ROC curve | Decision tree | Coronary artery disease | Multidimensional scaling | coronary artery disease | hierarchical cluster analysis | RISK-FACTORS | neural networks | ATHEROSCLEROSIS | CURVES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | decision tree | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MEN | multidimensional scaling | logistic regression | Medicine, Experimental | Medical research | Coronary heart disease | Analysis
Logistic regression | Neural networks | Hierarchical cluster analysis | ROC curve | Decision tree | Coronary artery disease | Multidimensional scaling | coronary artery disease | hierarchical cluster analysis | RISK-FACTORS | neural networks | ATHEROSCLEROSIS | CURVES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | decision tree | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MEN | multidimensional scaling | logistic regression | Medicine, Experimental | Medical research | Coronary heart disease | Analysis
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Loading RightsMALARIA JOURNAL, ISSN 1475-2875, 01/2019, Volume 18, Issue 1, pp. 4 - 16
BackgroundEmerging resistance to anti-malarial drugs has led malaria researchers to investigate what covariates (parasite and host factors) are associated with...
Plasmodium falciparum | INFECTIOUS DISEASES | Bayesian methods | PLASMODIUM-FALCIPARUM | Hierarchical linear models | Clearance rate | PARASITOLOGY | TROPICAL MEDICINE | Vector-borne diseases | Human diseases | Statistical analysis | Malaria | Methodology | Mortality | Probability theory | Markov chains | Parasites | Markov analysis | Drug resistance | Drug development | Computer programs | Regressions | Studies | Profiles | Modelling | Bayesian analysis | Public health | Framework
Plasmodium falciparum | INFECTIOUS DISEASES | Bayesian methods | PLASMODIUM-FALCIPARUM | Hierarchical linear models | Clearance rate | PARASITOLOGY | TROPICAL MEDICINE | Vector-borne diseases | Human diseases | Statistical analysis | Malaria | Methodology | Mortality | Probability theory | Markov chains | Parasites | Markov analysis | Drug resistance | Drug development | Computer programs | Regressions | Studies | Profiles | Modelling | Bayesian analysis | Public health | Framework
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