Robust Forecasting of Non-Stationary Time Series

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.Heteroscedasticity;Non-parametric regression;Prediction;Outliers;Robustness

An ab initio study of the C3(+) cation using multireference methods

The energy difference between the linear 2 sigma(sup +, sub u) and cyclic 2B(sub 2) structures of C3(+) has been investigated using large (5s3p2d1f) basis sets and multireference electron correlation treatments, including complete active space self consistent fields (CASSCF), multireference configuration interaction (MRCI), and averaged coupled-pair functional (ACPF) methods, as well as the single-reference quadratic configuration interaction (QCISD(T)) method. Our best estimate, including a correction for basis set incompleteness, is that the linear form lies above the cyclic from by 5.2(+1.5 to -1.0) kcal/mol. The 2 sigma(sup +, sub u) state is probably not a transition state, but a local minimum. Reliable computation of the cyclic/linear energy difference in C3(+) is extremely demanding of the electron correlation treatment used: of the single-reference methods previously considered, CCSD(T) and QCISD(T) perform best. The MRCI + Q(0.01)/(4s2p1d) energy separation of 1.68 kcal/mol should provide a comparison standard for other electron correlation methods applied to this system

Robust Forecasting of Non-Stationary Time Series

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.

Theoretical Considerations on the Effect of Ion Formation Conditions on the Transmission Through a Laser Microprobe Mass Analyzer

A theoretical study on the ion transmission through the laser microprobe mass analyzer LAMMA 500 was made using ray-tracing computer programs. The calculations reveal that the ion transmission is strongly affected by the initial conditions of ion formation. Chromatic and spherical aberrations give rise to considerable discrimination in the univoltage lens. A correlation is attempted between measured and theoretical transmission curves. For the latter a physically plausible plasma model was initially assumed to generate the input parameters, i.e., locus of ion formation and angular and energy distributions of the ions (atomic and cluster ions). The model needs correction for aberration and space-charge effects : comparison of experimental and calculated ion transmission curves suggests, indeed, a more important contribution of particles with high energy and emitted under large angles, than initially assumed

Laser Microprobe Mass Spectrometry in Biology and Biomedicine

An overview is given of laser microprobe mass spectrometry (LMMS) in biology and biomedicine (1989-1993). The present instrumentation and its analytical features are surveyed. Applications are presented with special attention on human and animal tissue samples, as well as plant material. The capabilities of LMMS to study the element distribution in histological sections, to identify the chemical composition of inorganic inclusions and to generate structural information from organic compounds are evidenced

The Primary Energy Dependence of Backscattered Electron Images Up to 100 keV

The backscattered electron coefficient is known to be primarily dependent on the atomic number of the sample. If the atomic number increases, the backscattered electron coefficient increases, which results in a higher intensity in the backscattered electron image. The dependence of the primary electron energy is somewhat more complicated. Using photographic material (with composition AgBr-AgI), it is seen that the contrast in the backscattered electron image increases with the primary electron energy. Using three independent methods, based on image analysis techniques, it is shown that the difference between the backscattered electron coefficient of AgBr and AgI increases with the primary electron energy in the range from 40 to 100 keV

Exploring wind direction and SO2 concentration by circular-linear density estimation

The study of environmental problems usually requires the description of variables with different nature and the assessment of relations between them. In this work, an algorithm for flexible estimation of the joint density for a circular-linear variable is proposed. The method is applied for exploring the relation between wind direction and SO2 concentration in a monitoring station close to a power plant located in Galicia (NW-Spain), in order to compare the effectiveness of precautionary measures for pollutants reduction in two different years.Comment: 17 pages, 7 figures, 2 table