2012 - Fellow of the American Association for the Advancement of Science (AAAS)
2007 - Fellow of the American Statistical Association (ASA)
Marc G. Genton mainly focuses on Statistics, Applied mathematics, Econometrics, Covariance and Covariance function. His Statistics study often links to related topics such as Selection. His research integrates issues of Skewness, Estimator and Monte Carlo method in his study of Applied mathematics.
His studies in Econometrics integrate themes in fields like Maximum likelihood, Probabilistic logic, Spatial analysis and Electricity. His Covariance study incorporates themes from Statistical hypothesis testing, Covariance matrix and Likelihood-ratio test. His study in Covariance function is interdisciplinary in nature, drawing from both F-test, Geostatistics, Mathematical optimization and Random field.
His primary areas of study are Statistics, Applied mathematics, Estimator, Multivariate statistics and Covariance. His research combines Econometrics and Statistics. His Applied mathematics research includes elements of Skewness, Distribution and Joint probability distribution.
His Estimator research incorporates elements of Variogram, Mathematical optimization and Robustness. As part of the same scientific family, Marc G. Genton usually focuses on Multivariate statistics, concentrating on Artificial intelligence and intersecting with Machine learning. His work carried out in the field of Covariance brings together such families of science as Algorithm, Covariance matrix and Random field.
Marc G. Genton focuses on Multivariate statistics, Algorithm, Covariance, Statistics and Wind power. His biological study spans a wide range of topics, including Pattern recognition, Spatial dependence and Bayesian inference. The concepts of his Algorithm study are interwoven with issues in Maximum likelihood, Diagonal and Autoregressive model.
His work on Covariance function as part of general Covariance study is frequently connected to Space time, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The study incorporates disciplines such as Series and Robustness in addition to Statistics. His Multivariate normal distribution research is multidisciplinary, incorporating perspectives in Monte Carlo method and Applied mathematics.
His primary areas of investigation include Algorithm, Multivariate statistics, Autoregressive model, Wind power and Covariance. His research in Algorithm intersects with topics in Representation, Hierarchical database model, Conditional probability distribution and T-model. The study of Statistics and Machine learning are components of his Multivariate statistics research.
His Autoregressive model study integrates concerns from other disciplines, such as Robust statistics, Estimator, Skewness, Principal component analysis and Least squares. Marc G. Genton combines subjects such as Generator and Environmental economics with his study of Wind power. Marc G. Genton has included themes like Function, Applied mathematics and Random field in his Covariance study.
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Covariance Tapering for Interpolation of Large Spatial Datasets
Reinhard Furrer;Marc G Genton;Douglas Nychka.
Journal of Computational and Graphical Statistics (2006)
Classes of kernels for machine learning: a statistics perspective
Marc G. Genton.
international conference on artificial intelligence and statistics (2002)
Skew-Elliptical Distributions and Their Applications : A Journey Beyond Normality
Marc. G. Genton.
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry
Tilmann Gneiting;Marc G. Genton;Peter Guttorp.
On fundamental skew distributions
Reinaldo B. Arellano-Valle;Marc G. Genton.
Journal of Multivariate Analysis (2005)
Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method
Tilmann Gneiting;Kristin Larson;Kenneth Westrick;Marc G Genton.
Journal of the American Statistical Association (2004)
Highly Robust Variogram Estimation
Marc G. Genton.
Mathematical Geosciences (1998)
Robust Likelihood Methods Based on the Skew-t and Related Distributions
Adelchi Azzalini;Marc G. Genton;Marc G. Genton.
International Statistical Review (2008)
Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch
Le Xie;Yingzhong Gu;Xinxin Zhu;Marc G. Genton.
power and energy society general meeting (2014)
A unified view on skewed distributions arising from selections
Reinaldo Boris Arellano-Valle;Márcia D. Branco;Marc G. Genton.
Canadian Journal of Statistics-revue Canadienne De Statistique (2006)
Profile was last updated on December 6th, 2021.
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