Paul de Bruin

Philips (Netherlands)

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Philip Hans Franses

Erasmus University Rotterdam

Raymond Adema

Dick van Dijk

Erasmus University Rotterdam

Pavel A. Belov

ITMO University

Alena Shchelokova

ITMO University

Matthias J.P. van Osch

Leiden University Medical Center

Kirsten Koolstra

Philips (Netherlands)

Andrew Webb

Leiden University Medical Center

Efthymios Kallos

Metamaterial Technologies (Canada)

Irena Zivkovic

Eindhoven University of Technology

Cooperative Institutions

Leiden University Medical Center

Carle Foundation Hospital

ITMO University

Centrale Marseille

Centre National de la Recherche Scientifique

Aix-Marseille Université

Universidad Nacional de Colombia

Sergio Arboleda University

Leiden University

Delft University of Technology

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Experimental investigation of a metasurface resonator for in vivo imaging at 1.5 T

Journal of Magnetic Resonance (2017)

Alena Shchelokova Alexey Slobozhanyuk Paul de Bruin Irena Zivkovic Efthymios Kallos

In this work, we experimentally demonstrate an increase in the local transmit efficiency of a 1.5 T MRI scanner by using a metasurface formed by an array of brass wires embedded in a high permittivity low loss medium. Placement of such a structure inside the scanner results in strong coupling of the radiofrequency field produced by the body coil with the lowest frequency electromagnetic eigenmode of the metasurface. This leads to spatial redistribution of the near fields with enhancement of the local magnetic field and an increase in the transmit efficiency per square root maximum specific absorption rate in the region-of-interest. We have investigated this structure in vivo and achieved a factor of 3.3 enhancement in the local radiofrequency transmit efficiency.

Specific absorption rate

Radiofrequency coil

10.1016/j.jmr.2017.11.013

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Citations (42)

Forecasting poer-tranfromed time series data

Journal of Applied Statistics (1999)

Paul de Bruin Philip Hans Franses

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Forecasting power-transformed time series data

Journal of Applied Statistics (1999)

Paul de Bruin Philip Hans Franses

When there is an interest in forecasting the growth rates as well as the levels of a single macro-economic time series, a practitioner faces the question of whether a forecasting model should be constructed for growth rates, for levels, or for both. In this paper, we investigate this issue for 10 US (un-)employment series, where we evaluate the forecasts from a non-linear time series model for power-transformed data. Our main finding is that models for growth rates (levels) do not automatically result in the most accurate forecasts of growth rates (levels).

Macro

Predictive power

10.1080/02664769922043

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EU Financial Transaction Tax (FTT)

Raymond Adema Paul de Bruin

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Seasonal smooth transition autoregression

Econometric Institute Research Papers (2000)

Philip Hans Franses Paul de Bruin Dick van Dijk

textabstractIn this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We discuss its representation, estimation of the parameters and inference. This seasonal STAR (SEASTAR) model is examined for its practical usefulness by applying it to 18 quarterly industrial production series. The data are tested for smooth-transition nonlinearity and for time-varying seasonality. We find that the model fits the data well for 14 of the 18 series. We also consider out-of-sample forecasting where we compare forecasts from the SEASTAR models with forecasts from nested models. It turns out that the SEASTAR model sometimes outperforms the other models, particularly for large horizons. Finally, we compare the SEASTAR models with STAR models for the 14 corresponding seasonally adjusted series, and we find that the estimated business cycle chronologies can be markedly different.

Seasonality

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On data transformations and evidence of nonlinearity

Computational Statistics & Data Analysis (2002)

Philip Hans Franses Paul de Bruin

10.1016/s0167-9473(02)00054-3

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Seasonal Adjustment and the Business Cycle in Unemployment

Studies in Nonlinear Dynamics and Econometrics (2000)

Philip Hans Franses Paul de Bruin

Several recent studies show that seasonal variation and cyclical variation in unemployment are correlated.A common nding is that seasonality tends to di er across the business cycle stages of recessions and expansions.Since seasonal adjustment methods assume that the two sources of variation can somehow beseparated, the present study examines the impact of seasonal adjustment on the analysis of cyclical patterns.Seasonally adjusted quarterly unemployment data for 5 G-7 countries are modeled by a S m o o t h T ransition Autoregression STAR] while the corresponding unadjusted data are modeled by a so-called Seasonal STAR SEASTAR].A comparison of the implied estimated peaks and troughs shows that there is substantial agreement on the business cycle chronologies, albeit that for seasonally adjusted data recessionary periods tend to last longer.

10.1162/10811820052485997

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Citations (3)

EU Financial Transaction Tax (FTT): Recente ontwikkelingen en extraterritoriale werking

Raymond Adema Paul de Bruin

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On data transformations and evidence of nonlinearity

RePEc: Research Papers in Economics (1998)

Paul de Bruin Philip Hans Franses

In this paper we examine the interaction between data transformation and the empirical evidence obtained when testing for (non-)linearity. For this purpose we examine nonlinear features in 64 monthly and 53 quarterly US macroeconomic variables for a range of Box-Cox data transformations. Our general finding is that evidence of nonlinearity is not independent of the data transformation. Results of simulation experiments substantiate this finding.

Linearity

Data transformation

Empirical evidence

Power transform

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Citations (1)

Seasonal smooth transition autoregression

RePEc: Research Papers in Economics (2000)

Philip Hans Franses Paul de Bruin Dick van Dijk

In this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We discuss its representation, estimation of the parameters and inference. This seasonal STAR (SEASTAR) model is examined for its practical usefulness by applying it to 18 quarterly industrial production series. The data are tested for smooth-transition nonlinearity and for time-varying seasonality. We find that the model fits the data well for 14 of the 18 series. We also consider out-of-sample forecasting where we compare forecasts from the SEASTAR models with forecasts from nested models. It turns out that the SEASTAR model sometimes outperforms the other models, particularly for large horizons. Finally, we compare the SEASTAR models with STAR models for the 14 corresponding seasonally adjusted series, and we find that the estimated business cycle chronologies can be markedly different.

Seasonality

Representation

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