9,316 research outputs found
The use of S&T indicators in science policy: Dutch experiences and theoretical perspectives from policy analysis
The relation between bibliometrics and science policy remains underdeveloped. Relevance of new methods to produce indicators is easily claimed, but often without real insight in the policy processes. Drawing on experiences with the use of S&T indicators in science policy in the Netherlands and on principal-agent theory, I develop an analytical perspective which enbles to assess the role of S&T indicators in science policy. It is argue that the use of S&T indicators can only be understood well if one takes the socio-political context with its specific dynamics and rationalities into account
Consistent nonparametric Bayesian inference for discretely observed scalar diffusions
We study Bayes procedures for the problem of nonparametric drift estimation
for one-dimensional, ergodic diffusion models from discrete-time, low-frequency
data. We give conditions for posterior consistency and verify these conditions
for concrete priors, including priors based on wavelet expansions.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ385 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals
Estimation of parameters of a diffusion based on discrete time observations
poses a difficult problem due to the lack of a closed form expression for the
likelihood. From a Bayesian computational perspective it can be casted as a
missing data problem where the diffusion bridges in between discrete-time
observations are missing. The computational problem can then be dealt with
using a Markov-chain Monte-Carlo method known as data-augmentation. If unknown
parameters appear in the diffusion coefficient, direct implementation of
data-augmentation results in a Markov chain that is reducible. Furthermore,
data-augmentation requires efficient sampling of diffusion bridges, which can
be difficult, especially in the multidimensional case.
We present a general framework to deal with with these problems that does not
rely on discretisation. The construction generalises previous approaches and
sheds light on the assumptions necessary to make these approaches work. We
define a random-walk type Metropolis-Hastings sampler for updating diffusion
bridges. Our methods are illustrated using guided proposals for sampling
diffusion bridges. These are Markov processes obtained by adding a guiding term
to the drift of the diffusion. We give general guidelines on the construction
of these proposals and introduce a time change and scaling of the guided
proposal that reduces discretisation error. Numerical examples demonstrate the
performance of our methods
Perinatal mortality in the Netherlands. Backgrounds of a worsening international ranking
Perinatal mortality rates have dropped sharply in the past few decades, in the Netherlands as well as in all other European countries. However, as the decrease has generally slowed down since the 1980s, the Netherlands has lost its prominent position in the international ranking of countries with favourable perinatal mortality rates. This lower ranking is not only the result of the dialectics of progress, but also the consequence of a relatively restrained use of antenatal diagnostics. In addition, the Netherlands is among the European countries scoring highest on a number of important risk factors. This article examines the effect on perinatal mortality rates of known risk factors, in particular the presence of non-western foreigners, multiple births and older mothers. With respect to the latter factor, it is concluded that children of older mothers run a significantly higher risk of foetal mortality, whereas babies of young mothers (including women in their early twenties) run a higher risk of infant mortality. For babies of non-western mothers, infant mortality rates are higher, although there are substantial differences between ethnic backgrounds. First week mortality is most unfavourable for Surinamese and Antillean/Aruban children, and post-neonatal mortality is highest among Turkish and Moroccan babies. The fact that relatively many non-western foreigners from countries with relatively high risks of perinatal mortality have settled in the Netherlands, is one of the reasons for the fall in the international ranking. Lastly, the increase in the number of multiple births has been stronger in the Netherlands than in most other countries. The higher incidence of assisted reproduction explains most of this increase.ethnicity, foetal mortality, infant and child mortality, mortality, multiple births, neonatal mortality, perinatal mortality, Peristat, risk factors
A non-parametric Bayesian approach to decompounding from high frequency data
Given a sample from a discretely observed compound Poisson process, we
consider non-parametric estimation of the density of its jump sizes, as
well as of its intensity We take a Bayesian approach to the
problem and specify the prior on as the Dirichlet location mixture of
normal densities. An independent prior for is assumed to be
compactly supported and to possess a positive density with respect to the
Lebesgue measure. We show that under suitable assumptions the posterior
contracts around the pair at essentially (up to a logarithmic
factor) the -rate, where is the number of observations and
is the mesh size at which the process is sampled. The emphasis is on
high frequency data, , but the obtained results are also valid for
fixed . In either case we assume that . Our
main result implies existence of Bayesian point estimates converging (in the
frequentist sense, in probability) to at the same rate.
We also discuss a practical implementation of our approach. The computational
problem is dealt with by inclusion of auxiliary variables and we develop a
Markov Chain Monte Carlo algorithm that samples from the joint distribution of
the unknown parameters in the mixture density and the introduced auxiliary
variables. Numerical examples illustrate the feasibility of this approach
Simulation of elliptic and hypo-elliptic conditional diffusions
Suppose is a multidimensional diffusion process. Assume that at time zero
the state of is fully observed, but at time only linear combinations
of its components are observed. That is, one only observes the vector
for a given matrix . In this paper we show how samples from the conditioned
process can be generated. The main contribution of this paper is to prove that
guided proposals, introduced in Schauer et al. (2017), can be used in a unified
way for both uniformly and hypo-elliptic diffusions, also when is not the
identity matrix. This is illustrated by excellent performance in two
challenging cases: a partially observed twice integrated diffusion with
multiple wells and the partially observed FitzHugh-Nagumo model
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