Aigars Lācis, Natālija Ezīte, Jānis Šavlovskis, Indulis Kukulis, Roberts Rumba, Eva Strīķe, Edgars Zellāns, Inguna Ļūļaka and Dainis Krieviņš
Aboyans, V., Criqui, M. H., Abraham, P., Allison, M. A., Creager, M. A., Diehm, C., Fowkes F. G., Hiatt, W. R., Jönsson, B., Lacroix, P., Marin, B., McDermott, M. M., Norgren, L., Pande, R. L., Preux, P. M., Stoffers, H. E., Treat-Jacobson, D. (2012). Measurement and interpretation of the ankle-brachial index: A scientific statement from the American Heart Association. Circulation , 126 , 2890–2909.
Aboyans, V., Ho, E., Denenberg, J. O., Ho, L. A., Natarajan, L., Criqui, M. H. (2008). The association between elevated ankle systolic
 BABA, Y.-NISHIMARU, H.-HYAKUTAKE, H.: Confidence regions of parameters in a nonlinear repeated measurement model with mixed effects, Hiroshima Math. J. 37 (2007), 111-117.
 DAVIDIAN, D.-GILTINAN, D. M.: Nonlinear Models for Repeated Measurement Data. Chapman & Hall/CRC, London, 1995.
 DAVIS, C. S.: Statistical Methods for the Analysis of Repeated Measurements. Springer, New York, 2002.
 HSU, J. C.: Multiple Comparisons: Theory and Methods. Chapman & Hall, London
with application to sampled network data, Ann. Appl. Stat. 4 (2010), 78-93.
 SONG, H. H.-QIU, L.-ZHANG, Y.: Netquest: A flexible framework for largescale network measurement, in: ACM SIGMETRICS ’06 (R. A. Marie, P. B. Key and E. Smirni, eds.), St. Malo, France, 2006, SIGMETRICS Perform. Eval. Rev., Vol. 34, ACM New York, NY, USA, 2006, pp. 121-132.
 STURM, J. F.: Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones, Optim. Methods Softw. 11-12 (1999), 625-653.
 TITTERINGTON, D. M
Alexandru M. Morega, Cristina Săvăstru and Mihaela Morega
. Physiol Heart Circ. Physiol 280, pp. 1519-1527, 2001.
 Avolio, A.P., Butlin, M., Walsh, A., "Arterial blood pressure measurement and pulse wave analysis - their role in enhancing cardiovascular assessment," Physiological Measurement, 31, R1-R47, 2010.
 Millasseau S.C., Patel S.J., Redwood S.R., Ritter J.M., Chowienczyk P.J., "Pressure wave reflection assessed from the peripheral pulse: is a transfer function necessary," Hypertension, 41, pp. 1016-1020, 2003.
 da Fonseca L.J.S., Mota-Gomes M.A., Rabelo L
The paper deals with homogenization of nonlinear differential operators with monotone behaviour. We consider a situation, when the coefficients of the operator are not known exactly, but in certain bounds only due to errors caused by measurements. We use the deterministic approach to the problem- -worst scenario method introduced by I. Hlaváček.
In this article we analyze the admissibility and the exact observability of a body-beam system when the output is taken in a point of attachment from the beam to the body. The single output case chosen here is the practical measurement of the strength, its velocity or its moment. We prove the exact observability for the moment and the admissibility for the other cases. These results are obtained by the spectral properties of rotating body beam system operator and Ingham's inequalities.
The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity. The CS theory shows that any signal/image can be undersampled at a rate dependent on its intrinsic complexity. Moreover, in such undersampling regimes, the signal can be recovered by sparsity promoting convex regularization like ℓ1- or total variation (TV-) minimization. Precise relations between many low complexity measures and the sufficient number of random measurements are known for many sparsity promoting norms. However, a precise estimate of the undersampling rate for the TV seminorm is still lacking. We address this issue by: a) providing dual certificates testing uniqueness of a given cosparse signal with bounded signal values, b) approximating the undersampling rates via the statistical dimension of the TV descent cone and c) showing empirically that the provided rates also hold for tomographic measurements.
We consider an inverse problem arising from an time-dependent drift-diffusion model in semiconductor devices, which is formulated in terms of a system of parabolic equations for the electron and hole densities and the Poisson equation for the electric potential. This inverse problem aims to identify the doping profile from the final overdetermination data of the electric potential. By using the Schauder’s fixed point theorem in suitable Sobolev space, the existence of this inverse problem are obtained. Moreover by means of Gronwall inequality, we prove the uniqueness of this inverse problem for small measurement time. For this nonlinear inverse problem, our theoretical results guarantee the solvability for the proposed physical model.
Most recorded data of continuous distributions are rounded to the nearest decimal place due to the precision of the recording mechanism. This rounding entails errors in estimation and measurement. In this study, we consider parameter estimation of time series models based on rounded data. The adjusted maximum likelihood estimates in [Stam, A.-Cogger, K. O.: Rounding errors in autoregressive processes, Internat. J. Forecast. 9 (1993), 487-508] are derived theoretically for the first order moving average MA(1) model. Simulations are performed to compare the efficiencies of the adjusted maximum likelihood estimators with other estimators.
 HAN, A. K.: Non-parametric analysis of a generalized regression model, J. Econom. 35 (1987), 303-316.
 FANG, H.-B.-FANG, K.-T.: The meta-elliptical distributions with given marginals, J. Multivariate Anal. 82 (2002), 1-16.
 FULLER, W. A.: Measurement Error Models. John Wiley & Sons, New York, 1987.
 GIBBONS, J. D.: Nonparametric Statistical Inference. McGraw-Hill Book Comp., New York, 1971.
 JAECKEL, L. A.: Estimating regression coefficients by