Statistics for Spatial Data.

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

(1963). Statistical Inference for Markov Processes. Technometrics: Vol. 5, No. 3, pp. 413-415.

Statistical Inference for Markov Processes

Tomographic imaging has become a mandatory tool for the diagnosis of a majority of diseases in clinical routine. Since each method has its pros and cons, a variety of them is regularly used in clinics to satisfy all application needs. Magnetic particle imaging (MPI) is a relatively new tomographic imaging technique that images magnetic nanoparticles with a high spatiotemporal resolution in a quantitative way, and in turn is highly suited for vascular and targeted imaging. MPI was introduced in 2005 and now enters the preclinical research phase, where medical researchers get access to this new technology and exploit its potential under physiological conditions. Within this paper, we review the development of MPI since its introduction in 2005. Besides an in-depth description of the basic principles, we provide detailed discussions on imaging sequences, reconstruction algorithms, scanner instrumentation and potential medical applications.

https://iopscience.iop.org/article/10.1088/1361-6560/aa6c99/pdf

Magnetic particle imaging: from proof of principle to preclinical applications

Superparamagnetic iron oxides as MPI tracers: A primer and review of early applications.

The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures.,To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively.,Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique.,This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates).

Interest rates calibration with a CIR model

The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The new approach is applied to different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g., simulating regime switching, volatility clustering, skewed tails) as well as the new ones added by the current market environment characterized by low to negative interest rates.

Forecasting interest rates through Vasicek and CIR models: A partitioning approach

Higher-order Cahn–Hilliard equations with dynamic boundary conditions

A large sampling of Apulian red-figured pottery fragments (fifth to fourth century BC) coming from Taranto (Italy) was analyzed by a multi-technique approach. The ceramic bodies’ elemental composition has been obtained by inductively coupled plasma mass spectrometry (ICP-MS), the mineralogical composition of pastes by polarized-light optical and electron microscopies (OM and SEM-EDS), and X-ray powder diffraction (PXRD). The results obtained from the statistical treatment of compositional data, combined with those driven from mineralogical composition of pastes, allow to formulate hypotheses about the provenance of the objects and the manufacturing tradition of the workshops, starting to make it possible to understand the relationships among ceramic technology, artistic expression, and workshop practice in the samples analyzed.

Rosa Maria Mininni

Papers

Interest rates calibration with a CIR model

Forecasting interest rates through Vasicek and CIR models: A partitioning approach

Higher-order Cahn–Hilliard equations with dynamic boundary conditions

An archaeometric approach to gain knowledge on technology and provenance of Apulian red-figured pottery from Taranto

Stochastic modelling and statistics of polymer crystallization process