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NOVA techniques, shrinkage methods, GLS, GLM models. • Complexity theory, combinatorial optimization methods • Optimization methods: Linear (LP), Quadratic (QP), Convex and Semi-Definite programming (SDP) • Strong general mathematical foundations in multivariate complex analysis, functional analysis,
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegrati
NOVA techniques, shrinkage methods, GLS, GLM models. • Complexity theory, combinatorial optimization methods • Optimization methods: Linear (LP), Quadratic (QP), Convex and Semi-Definite programming (SDP) • Strong general mathematical foundations in multivariate complex analysis, functional analysis, d
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Igarkov Model • Kernel Methods, Support Vector Machines • Cointegrat
NOVA techniques, shrinkage methods, GLS, GUM models. • Complexity theory, combinatorial optimization methods • Optimization methods: Linear (LP), Quadratic (QP), Convex and Semi-Definite programming (SDP) • Strong general mathematical foundations in multivariate complex analysis, functional analysis,
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegrati
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