Associate professor Delft University of Technology Electrical Engineering, Mathematics and Computer Science Delft Institute of Applied Mathematics Van Mourik Broekmanweg 6 2628 XE Delft, Phone: 015-2784517 Room: E-1.180 E-mail: f(dot)h(dot)vandermeulen(at)tudelft(dot)nl |

Together with Shota Gugushvili, Peter Spreij (University of Amsterdam) and Moritz Schauer I have considered various problems of nonparametric function estimation, where it is assumed that the function is piecewise constant, but adjacent bins are coupled such that the values of these have positive dependence. This appears to work well in a wide range of settings and we may expand this work to other settings or possibly work on stronger theoretical validation of this approach.

- More generally, I am interested in Bayesian computational methods such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC). On a somewhat more theoretical level I am interested in proving posterior contraction rates for Bayesian procedures (joint work with Geurt Jongbloed and Lixue Pang, in particular on censoring/shape restricted nonparametric problems). Finally an important aspect of my research consists of direct collaboration with researchers in fields outside mathematics. Examples include sports engineering (analysis of multilevel longitudinal data), climate projections and maritime engineering (fatigue calculations of maritime structures).

Some keywords: * statistical inference for stochastic processes
(diffusions, Lévy processes); Bayesian computation; Bayesian
asymptotics; dynamical systems; longitudinal data.
*

If
we share research interests, feel free to send me an email to
discuss possibilities for collaboration.

G. Jongbloed, F.H. van der Meulen and L.
Pang (2019) *Nonparametric Bayesian estimation of a
concave distribution** **function with mixed
interval censored data** *

F.H. van der Meulen and M.R. Schauer, M.R. (2017)

On the contrary, we believe the examples we chose should be favourable to residual bridge constructs and that's why we took these. So no, no cherry picking. The main point is that guided proposals (as Moritz and I have defined these) are not computationally excessive.

This has become much more apparent with the paper on "Continuous-discrete smoothing of diffusions".

R.B. Hageman, F.H. van der Meulen, A. Rouhan and M.L. Kaminski (2018)

G. Jongbloed, F.H. van
der Meulen and L. Pang (2018) *B**ayesian estimation of
a dec**reasing density* arXiv submitted

[corresponding code
is on zenodo https://zenodo.org/record/1215901#.Wtg3N9NuZTY]

S. Gugushvili, E. Mariucci and F.H. van der Meulen (2019)

S. Gugushvili, F.H. van der Meulen, M.R. Schauer and P. Spreij (2019)

S. Gugushvili, F.H. van der Meulen, M.R. Schauer and P. Spreij (2019)

Di Bucchianico, L. Iapichino, N. Litvak, F.H. van der Meulen and R. Wehrens (2018)

S. Gugushvili, F.H.
van der Meulen, M.R. Schauer and P. Spreij (2018) *Nonparametric
Bayesian volatility learning under microstructure noise, *arXiv,* *Entropy
(special issue on MaxEnt 2018).

S. Gugushvili, F.H.
van der Meulen, M.R. Schauer and P. Spreij (2018) *Nonparametric
Bayesian volatility estimation* arXiv, MATRIX
Annals, Editors: David R. Wood, Jan de Gier, Cheryl E. Praeger,
Terence Tao. MATRIX Book Series, Vol 2, Springer, to appear.

F.H. van der Meulen
and M.R. Schauer (2017) *Bayesian
estimation of incompletely observed diffusions, *Stochastics
**90**(5), 641-662.

F.H. van der Meulen,
M.R. Schauer, J. van Waaij (2017) *Adaptive
nonparametric drift estimation for diffusion processes using
Faber-Schauder expansions*, Statistical
Inference for Stochastic Processes **21**(3), 603-628.

F.H. van der Meulen and M.R. Schauer, M.R. (2017) *Bayesian
estimation of discretely observed multi-dimensional
diffusion processes using guided proposals*,
Electronic Journal of Statistics **11**(1), 2358--2396.

M.R. Schauer, F.H. van der Meulen and J.H. van Zanten
(2017) *Guided
proposals for simulating multi-dimensional diffusion bridges*,
Bernoulli **23**(4A), 2917--2950

Gugushvili, S., Van
der Meulen, F.H. and Spreij, P.J. (2016) *A
non-parametric Bayesian approach to decompounding from high
frequency data*. Statistical Inference for Stochastic
Processes.

Hartman, K., Wittich,
A. Cai, J.J., Van der Meulen, F.H. and Azevedo, J.M.N. (2016) *
Estimating the age of Rissos dolphins (Grampus griseus) based
on skin appearance*. Journal of Mammology **97**(2),
490--502.

Litvak, N. and Van der
Meulen, F.H. (2015) *Networks & Big Data*. Nieuw
Archief voor Wiskunde **5**(2), 138--139.

Van der Meulen, F.H., Luca, S. Overal, G., Di Bucchianico, A. and Jongbloed, G. (2014)

Van der Meulen, F.H., Schauer, M.R. and Van Zanten. J.H. (2014)

Van der Meulen, F.H. van Hageman, R. (2013)

Van der Meulen, F.H. and Van Zanten, J.H. (2013)

Wauben, L.S.G.L., Van Grevenstein, W.M.U., Goossens, R.H.M., Van der Meulen, F.H. and Lange, J.F. (2011)

Jongbloed, G. and Van der Meulen (2011)

Van der Meulen, F.H., Vermaat, M.B. and Willems, P. (2010) Case Study: An application of Logistic Regression in a Six Sigma project in Healthcare. Quality Engineering 23, 113-124.

Van der Meulen, F.H., De Koning, H. and De Mast, J. (2009)

Jongbloed,
G. and Van der Meulen, F.H. (2009) *Estimating
a concave distribution function from data corrupted with
additive noise.* Annals of Statistics **37**(2),
782-815.

Vermaat,
M.B., Van der Meulen, F.H. and Does, R.J.M.M. (2008)*
Asymptotic
Behaviour of the Variance of the EWMA Statistic for
Autoregressive Processes* Statistics and Probability
Letters **78**(12), 1673-1682

Van
der Meulen, F.H., Van der Vaart, A.W. and Van Zanten, J.H.
(2006) *Convergence
rates of posterior distributions for Brownian semimartingale
models *Bernoulli **12**(5),
863-888

Jongbloed,
G. and Van der Meulen, F.H. (2006) *Parametric
estimation for subordinators and induced OU-processes* Scandinavian Journal of Statistics **33**(4),
825-847

Ramaker,
H.JJ., Van Sprang, E.N.M., Westerhuis, J.A., Gorden, S.P., Van
der Meulen, F.H., Smilde, A.K. (2006) *Performance
assessment and improvement of control charts for statistical
batch process monitoring *Statistica
Neerlandica **60**(3), 339-360

Phd-thesis Statistical estimation for Levy driven OU-processes and Brownian semimartingales (2005), Vrije Universiteit Amsterdam

1995-2001:
TU Delft, applied mathematics

2001-2005: PhD student at Vrije Universiteit
Amsterdam

2005-2007: Consultant/researcher at the Institute
for Business and Industrial Statistics at the University
of Amsterdam (IBIS UvA)

2007-2017: Assistant professor at TU Delft

2018-now: Associate professor at TU Delft

2012-now: Scientific advisor for company
ProjectsOne