László Györfi focuses on Statistics, Nonparametric statistics, Applied mathematics, Econometrics and Estimator. László Györfi has researched Statistics in several fields, including Zero and Distribution. His Econometrics study integrates concerns from other disciplines, such as Ergodic theory and Stochastic process.
His Strong consistency study incorporates themes from Cross-validation, Regression analysis, Nonparametric regression, Generalized least squares and Non-linear least squares. His research in Entropy intersects with topics in Parametric statistics, Artificial neural network, Probabilistic logic, Totally bounded space and Feature extraction. His studies deal with areas such as Bayes' theorem and Artificial intelligence as well as Parametric statistics.
His primary scientific interests are in Statistics, Applied mathematics, Combinatorics, Nonparametric statistics and Ergodic theory. His is doing research in Nonparametric regression, Regression analysis, Consistency, Strong consistency and Regression function, both of which are found in Statistics. László Györfi has included themes like Polynomial regression, Kernel density estimation, Bounded function, Asymptotic distribution and Exponential function in his Applied mathematics study.
His Kernel density estimation research incorporates elements of Density estimation, Multivariate kernel density estimation and Kernel. Many of his research projects under Combinatorics are closely connected to St petersburg with St petersburg, tying the diverse disciplines of science together. His Nonparametric statistics study combines topics in areas such as Machine learning, Sequence prediction and Differential entropy.
László Györfi spends much of his time researching Combinatorics, Limit, Applied mathematics, Statistics and Limit distribution. His work deals with themes such as Distribution, k-nearest neighbors algorithm, Probability measure, Rate of convergence and Upper and lower bounds, which intersect with Combinatorics. The various areas that László Györfi examines in his k-nearest neighbors algorithm study include Smoothness, Probability of error and Classification rule.
László Györfi interconnects Value, Central limit theorem, Delta method and Kernel in the investigation of issues within Applied mathematics. His Value research includes themes of Ergodic theory, Ergodic process and Conditional expectation. All of his Statistics and Polynomial regression, Nonparametric regression, Strong consistency, Nonparametric statistics and Mean integrated squared error investigations are sub-components of the entire Statistics study.
His primary areas of study are Combinatorics, Limit, Applied mathematics, Rate of convergence and St petersburg. His study focuses on the intersection of Combinatorics and fields such as Probability measure with connections in the field of Absolute continuity and Poisson limit theorem. His Limit research is multidisciplinary, incorporating elements of Representation and Pure mathematics.
His Rate of convergence research incorporates elements of Probability of error, Stochastic geometry and Partition. The Probability of error study combines topics in areas such as Classification rule and k-nearest neighbors algorithm. His Kernel study is concerned with the field of Statistics as a whole.
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A Probabilistic Theory of Pattern Recognition
Luc Devroye;László Györfi;Gábor Lugosi.
A Distribution-Free Theory of Nonparametric Regression
Nonparametric density estimation : the L view
Luc Devroye;László Györfi.
Journal of the American Statistical Association (1987)
Nonparametric entropy estimation. An overview
J. Beirlant;EJ Dudewicz;László Györfi;István Dénes.
International Journal of Mathematical and Statistical Sciences (1997)
On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates
Luc Devroye;Laszlo Gyorfi;Adam Krzyzak;Gabor Lugosi.
Annals of Statistics (1994)
Constructions of binary constant-weight cyclic codes and cyclically permutable codes
Nguyen Q. A;L. Gyorfi;J.L. Massey.
IEEE Transactions on Information Theory (1992)
Distribution estimation consistent in total variation and in two types of information divergence
A.R. Barron;L. Gyorfi;E.C. van der Meulen.
IEEE Transactions on Information Theory (1992)
Consistent Nonparametric Tests of Independence
Arthur Gretton;Arthur Gretton;László Györfi.
Journal of Machine Learning Research (2010)
NONPARAMETRIC KERNEL-BASED SEQUENTIAL INVESTMENT STRATEGIES
László Györfi;Gábor Lugosi;Frederic Udina.
Mathematical Finance (2006)
Density-free convergence properties of various estimators of entropy
László Györfi;Edward C. van der Meulen.
Computational Statistics & Data Analysis (1987)
Profile was last updated on December 6th, 2021.
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