Parametric modeling of photometric signals

This paper studies a new model for photometric signals under high flux assumption. Photometric signals are modeled by Gaussian autoregressive processes having the same mean and variance denoted Constraint Gaussian Autoregressive Processes (CGARP's). The estimation of the CGARP parameters is discussed. The Cramér Rao lower bounds for these parameters are studied and compared to the estimator mean square errors. The CGARP is intended to model the signal received by a satellite designed for extrasolar planets detection. A transit of a planet in front of a star results in an abrupt change in the mean and variance of the CGARP. The Neyman–Pearson detector for this changepoint detection problem is derived when the abrupt change parameters are known. Closed form expressions for the Receiver Operating Characteristics (ROC) are provided. The Neyman–Pearson detector combined with the maximum likelihood estimator for CGARP parameters allows to study the generalized likelihood ratio detector. ROC curves are then determined using computer simulations

Non-parametric Bayesian modeling of complex networks

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature

NON-PARAMETRIC AND SEMI-PARAMETRIC TECHNIQUES FOR MODELING AND SIMULATING CORRELATED, NON-NORMAL PRICE AND YIELD DISTRIBUTIONS: APPLICATIONS TO RISK ANALYSIS IN KANSAS AGRICULTURE

Parametric, non-parametric, and semi-parametric approaches are commonly used for modeling correlated distributions. Semi-parametric and non-parametric approaches are used to examine the risk situation for Kansas agriculture. Results from the model indicate that 2000 will be another difficult year for Kansas farmers, although crop income will increase slightly from 1999. However, unless another supplemental infusion of government payments occurs, crop income is expected to be the lowest since 1992.correlated distributions, non-parametric modeling, semi-parametric modeling, Kansas agriculture, Research Methods/ Statistical Methods,

Parametrization of translational surfaces

The algebraic translational surface is a typical modeling surface in computer aided design and architecture industry. In this paper, we give a necessary and sufficient condition for that algebraic surface having a standard parametric representation and our proof is constructive. If the given algebraic surface is translational, then we can compute a standard parametric representation for the surface

Fully parameterized macromodeling of S-parameter data by interpolation of numerator & denominator

A robust approach for parametric macromodeling of tabulated frequency responses is presented. An existing technique is modified in such a way that interpolation is performed at the numerator and denominator level, rather than the transfer function level. This enhancement ensures that the poles of the parametric macromodel are fully parameterized. It strengthens the modeling capabilities and improves the model compactness

GDP nowcasting with ragged-edge data: a semi-parametric modeling

This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone.euro area GDP • real-time nowcasting • forecasting • non-parametric methods