In this paper statistical properties of estimators of drift parameters for diffusion processes are studied by modern numerical methods for stochastic differential equations. This is a particularly useful method for discrete time samples, where estimators can be constructed by making discrete time approximations to the stochastic integrals appearing in the maximum likelihood estimators for continuously observed diffusions. A review is given of the necessary theory for parameter estimation for diffusion processes and for simulation of diffusion processes. Three examples are studied.
The article proposes a technique for estimating the maximum possible errors in measuring the parameters of the signals observed against a background of interference that do not exhibit statistical properties on the measurement interval and of which only the region of their possible change is known. This region is a layer within which the interference-free signal can vary due to the variation in the values of the parameters of the model function describing it. The technique is also proposed that makes it possible to estimate the measurement error of the monitored parameter for a specific implementation of the signal described by the proposed model with a confidence probability close to unity. An example of the use of the offered techniques for estimating the coordinate of a light stain created by a point source is shown. The offered approach is actual, when each measurement represents special worth and there is no possibility to improve the measurement result by averaging over the set of signal realizations.
In this paper, a cubic transformation exponential Weibull distribution is proposed by using the family of cubic transformation distributions introduced by Rahman et al.The reasoning process of the proposed cubic transformation exponential Weibull distribution is discussed in detail, and its statistical properties and parameter estimation are also discussed.Using real data, the maximum likelihood estimation is used to simulate. Through the comparison of fitting results, it is concluded that the cubic transformation exponential Weibull distribution proposed in this paper has stronger applicability.
In this paper statistical properties of estimators of drift parameters for diffusion processes are studied by modern numerical methods for stochastic differential equations. This is a particularly useful method for discrete time samples, where estimators can be constructed by making discrete time approximations to the stochastic integrals appearing in the maximum likelihood estimators for continuously observed diffusions. A review is given of the necessary theory for parameter estimation for diffusion processes and for simulation of diffusion processes. Three examples are studied.
A monolithic, polycapillary, x-ray optic was adapted to a laboratory-based x-ray microprobe to evaluate the potential of the optic for x-ray micro fluorescence analysis. The polycapillary was capable of collecting x-rays over a 6 degree angle from a point source and focusing them to a spot approximately 40 µm diameter. The high intensities expected from this capillary should be useful for determining and mapping minor to trace elements in materials. Fig. 1 shows a sketch of the capillary with important dimensions.The microprobe had previously been used with straight and with tapered monocapillaries. Alignment of the monocapillaries with the focal spot was accomplished by electromagnetically scanning the focal spot over the beveled anode. With the polycapillary it was also necessary to manually adjust the distance between the focal spot and the polycapillary.The focal distance and focal spot diameter of the polycapillary were determined from a series of edge scans.
Application of the E – Layer Model for Solving the Problems of Parametric Estimation in Measuring Devices