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Managing Well Integrity using Reliability Based Models
Imperial Users onl
Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models
Structured additive regression provides a general framework for complex
Gaussian and non-Gaussian regression models, with predictors comprising
arbitrary combinations of nonlinear functions and surfaces, spatial effects,
varying coefficients, random effects and further regression terms. The large
flexibility of structured additive regression makes function selection a
challenging and important task, aiming at (1) selecting the relevant
covariates, (2) choosing an appropriate and parsimonious representation of the
impact of covariates on the predictor and (3) determining the required
interactions. We propose a spike-and-slab prior structure for function
selection that allows to include or exclude single coefficients as well as
blocks of coefficients representing specific model terms. A novel
multiplicative parameter expansion is required to obtain good mixing and
convergence properties in a Markov chain Monte Carlo simulation approach and is
shown to induce desirable shrinkage properties. In simulation studies and with
(real) benchmark classification data, we investigate sensitivity to
hyperparameter settings and compare performance to competitors. The flexibility
and applicability of our approach are demonstrated in an additive piecewise
exponential model with time-varying effects for right-censored survival times
of intensive care patients with sepsis. Geoadditive and additive mixed logit
model applications are discussed in an extensive appendix
Systematic Physics Constrained Parameter Estimation of Stochastic Differential Equations
A systematic Bayesian framework is developed for physics constrained
parameter inference ofstochastic differential equations (SDE) from partial
observations. The physical constraints arederived for stochastic climate models
but are applicable for many fluid systems. A condition isderived for global
stability of stochastic climate models based on energy conservation.
Stochasticclimate models are globally stable when a quadratic form, which is
related to the cubic nonlinearoperator, is negative definite. A new algorithm
for the efficient sampling of such negative definite matrices is developed and
also for imputing unobserved data which improve the accuracy of theparameter
estimates. The performance of this framework is evaluated on two conceptual
climatemodels
Procedure to Approximately Estimate the Uncertainty of Material Ratio Parameters due to Inhomogeneity of Surface Roughness
Roughness parameters that characterize contacting surfaces with regard to
friction and wear are commonly stated without uncertainties, or with an
uncertainty only taking into account a very limited amount of aspects such as
repeatability of reproducibility (homogeneity) of the specimen. This makes it
difficult to discriminate between different values of single roughness
parameters.
Therefore uncertainty assessment methods are required that take all relevant
aspects into account. In the literature this is scarcely performed and examples
specific for parameters used in friction and wear are not yet given.
We propose a procedure to derive the uncertainty from a single profile
employing a statistical method that is based on the statistical moments of the
amplitude distribution and the autocorrelation length of the profile. To show
the possibilities and the limitations of this method we compare the uncertainty
derived from a single profile with that derived from a high statistics
experiment.Comment: submitted to Meas. Sci. Technol., 12 figure
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
Large trades in a financial market are usually split into smaller parts and
traded incrementally over extended periods of time. We address these large
trades as hidden orders. In order to identify and characterize hidden orders we
fit hidden Markov models to the time series of the sign of the tick by tick
inventory variation of market members of the Spanish Stock Exchange. Our
methodology probabilistically detects trading sequences, which are
characterized by a net majority of buy or sell transactions. We interpret these
patches of sequential buying or selling transactions as proxies of the traded
hidden orders. We find that the time, volume and number of transactions size
distributions of these patches are fat tailed. Long patches are characterized
by a high fraction of market orders and a low participation rate, while short
patches have a large fraction of limit orders and a high participation rate. We
observe the existence of a buy-sell asymmetry in the number, average length,
average fraction of market orders and average participation rate of the
detected patches. The detected asymmetry is clearly depending on the local
market trend. We also compare the hidden Markov models patches with those
obtained with the segmentation method used in Vaglica {\it et al.} (2008) and
we conclude that the former ones can be interpreted as a partition of the
latter ones.Comment: 26 pages, 12 figure
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