B. Ninness

Latest

• Least-squares estimation of a class of frequency functions: A finite sample variance expression
• Accurate quantification of variance error
• Exact Quantification of variance error
• Finite sample input design for linearly parameterized models subject to frequency domain constraints
• On explicit characterisation of reproducing kernels with applications in estimation theory
• On frequency Domain Accuracy of Closed Loop Estimates
• The Analysis of Variance Error Part I: Fundamental Principles
• The Analysis of Variance Error Part II: Accurate Quantification
• On maximum likelihood identification of errors-in-variables models
• Analysis of the Variability of Joint Input-Output Estimation Methods
• Numerical Conditioning
• On the Frequency Domain Accuracy of Closed Loop Estimates
• Variance Error, Reproducing Kernels and Orhonormal Bases
• An exact finite sample variance expression for a class of frequency function estimates
• The Effect of Regularisation on Variance Error
• Variance Error Quantifications that are Exact for Finite Model Order
• Variance Error Quantifications that are Exact for Finite Model Order
• Model Structure and Numerical Properties of Normal Equations
• Improved and quantified accuracy for linear spectral estimates
• The fundamental role of general orthonormal bases in system identification
• Asymptotic Variance Expressions for Output Error Model Structures
• Estimation Variance is not Model Structure Independent
• Model Structure and Numerical Properties of Normal Equations
• Signal Spectra and Conditioning when using Orthonormal Parametrisation
• Generalised Fourier and Toeplitz results for rational orthonormal bases
• Fast non-iterative estimation of hidden Markov models
• Generalized Fourier and Toeplitz results for rational orthonormal bases.
• Identification in closed loop: Asymptotic high order variance for restricted complexity models
• Modelling of Random Processes using Orthonormal Bases