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- Juan Merlo, Basile Chaix, +5 authors K Larsen
- Journal of epidemiology and community health
- 2006

STUDY OBJECTIVE
In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case in a didactic rather than a mathematical way.
DESIGN AND… (More)

- Tianshi Chen, Henrik Ohlsson, Lennart Ljung
- Automatica
- 2012

Intrigued by some recent results on impulse response estimation by kernel and nonparametric techniques, we revisit the old problem of transfer function estimation from input-output measurements. We formulate a classical regularization approach, focused on finite impulse response (FIR) models, and find that regularization is necessary to cope with the high… (More)

- Henrik Ohlsson, Lennart Ljung, Stephen P. Boyd
- Automatica
- 2010

Segmentation of time-varying systems and signals into models whose parameters are piecewise constant in time is an important and well studied problem. It is here formulated as a least-squares problem with sumof-norms regularization over the state parameter jumps, a generalization of `1-regularization. A nice property of the suggested formulation is that it… (More)

- Henrik Ohlsson, Yonina C. Eldar
- 2014 IEEE International Conference on Acoustics…
- 2014

The phase retrieval problem has a long history and is an important problem in many areas of optics. Theoretical understanding of phase retrieval is still limited and fundamental questions such as uniqueness and stability of the recovered solution are not yet fully understood. This paper provides several additions to the theoretical understanding of sparse… (More)

- Tianshi Chen, Thomas B. Schön, Henrik Ohlsson, Lennart Ljung
- IEEE Transactions on Signal Processing
- 2011

In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested subproblems and then handles the two nested subproblems using PFs. The DPF has the advantage over the regular PF that the DPF can increase the level of… (More)

- Henrik Ohlsson, Allen Y. Yang, Roy Dong, S. Shankar Sastry
- NIPS
- 2012

While compressive sensing (CS) has been one of the most vibrant research fields in the past few years, most development only applies to linear models. This limits its application in many areas where CS could make a difference. This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used… (More)

- H. Ohlsson, U. Lindblad, +5 authors J. Merlo
- European Journal of Clinical Pharmacology
- 2005

The aim was to investigate the role that municipalities and out-patient health care centres (HCCs) have in understanding adherence to official guidelines on statin prescribing. Our hypothesis was that after guideline publication, adherence to recommended statin prescription would increase and variance among HCCs and municipalities would decrease. Since… (More)

- J Merlo, H Ohlsson, K F Lynch, B Chaix, S V Subramanian
- Journal of epidemiology and community health
- 2009

BACKGROUND
Social epidemiology investigates both individuals and their collectives. Although the limits that define the individual bodies are very apparent, the collective body's geographical or cultural limits (eg "neighbourhood") are more difficult to discern. Also, epidemiologists normally investigate causation as changes in group means. However, many… (More)

Given a linear system in a real or complex domain, linear regression aims to recover the model parameters from a set of observations. Recent studies in compressive sensing have successfully shown that under certain conditions, a linear program, namely, `1-minimization, guarantees recovery of sparse parameter signals even when the system is underdetermined.… (More)

We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to… (More)