Håkan Hjalmarsson
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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
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