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Prause(1999), Kiersti Asa & Ingrid Hobck Haft(2005), McNeil (2005) and WENBO HU(2005) had confirmed that generalized hyperbolic distribution has better performance to fit finiacial data[4][5][6][7].
When ,we obtain Normal Inverse Gaussian distributio(NIG) (2) When ,we obtain hyperbolic distribution(HYP) with the following density (3) Experiment Data and Parameter Estimation The data studied consists of the daily log-returns, which were calculated by,where is the close price of Shanghai composite index(Shindex).The start date of the sample is 2000-1-4 and the end is 2011-8-31.
Otherwise, the current point remains unchanged, and the mesh size will be doubled the size reduction to reduce the search range.
The distribtion of financial data is usually typical sharp-Kurtosis and fat-tail, so and are not suitale for financial data.
Generalized hyperbolic distributions and Brazilian data.

The in-situ partitioning data presented here were collected at approximately 1 minute intervals from the back-scattered bank of detectors located at 168.33°.
Corrections to lattice parameter data for thermal expansion effects were made using published data [10].
Lattice strain data are presented with no correction for crystallite size contributions to peak broadening.
Figure 3 illustrates the specimen temperature calculations, and the thermal expansion corrections made to the raw data to reveal the evolution of lattice parameters with respect to partitioning time and temperature.
The peak austenite carbon concentration as estimated from lattice parameter data is much lower than would be expected if 100% of the alloy carbon content partitioned to austenite.

The analysis of tool bit cutting performance becomes a necessity in order to provide basic data for machine selection, design and performance prediction.
When this force exceeds the strength of coal, then a coal fragment or chip is produced with immediate reduction of pick force.
Investigation and analysis The primary data related to coal and shaper machine are given in table no. 1.
The following table were drawn using experimental data.
Table 1: Available data Weight of coal 4.862 kg Stroke length 5 inch = 12.7 cm No. of stroke per minute 24 Cutting surface of coal 23x13cm2 Dimension of specimen 23x13x12 cm3 Time of forward stroke 1.4 sec Time of backward stroke 1 sec Table 2: Experimental data of weight removal at different depth of cut when tilt angle is 0° S.

Measure Definition, Input Data and Uncertainty Sources.
/A, in which the input data are the force Fcor and area A.
Considering that the measure (fcor) is related to several input data, the shear strength (fcor) was denominated the main measure, and the other input data were denominated secondary measures.
Fig. 2 Cause and effect diagram, main measure and input data.
Fig. 3 Main measured, input data and uncertainty sources.

In order to obtain interdiffusion coefficients of the pseudo-binary systems, the experimental data was analyzed by the Sauer and Freise method, and also impurity diffusion coefficients of Ti, V and Nb in Ni3Al were estimated by applying the Darken-Manning equation.
The activation enthalpies obtained from the experimental data confirmed the retardation of Ti, V and Nb diffusion in Ni3Al by the anti-site diffusion mechanism.
The extrapolated tracer diffusion coefficients in Ni3Al are estimated and the activation energies are correlated with existing data of the elements from a point of view of the site preferences.
Thus, the solutes residing in the β-site diffuse with higher activation enthalpy than those in the α-site, which corresponds to the additional contribution needed for antisite formation [2, 18]; i.e., a reduction of mobility of the Ti, V, and Nb diffusing atoms in Ni3Al occurs due to the antisite diffusion mechanism.
The activation enthalpies (Q) for solute element diffusion in Ni3Al were estimated from experimental data.

The work presents a new analytical model to predict coherent length which is shown to correlate well with measured data from experiment.
Equation (2) correlates the data with a root-mean-square error of 9.4 per cent from their experimental work.
It is not entirely clear from the plots at which length the jet break-up occurs so another measure using the experimental data was sought.
Data was transferred from MATLAB ® into Excel for direct comparison of the two phenomena of interest.
A method has been developed which enables the nozzle loss factor, Nf, to be obtained from the experimental data over the whole range of operational conditions.

However, measurements can also be useful each time the plant is shut down during a plant outage; which would be used to complement data from existing proven rugged monitoring methods.
Using the micro-strain data from previous measurements, accumulated strain and strain rate can be determined.
The localization of the yielding around the defect has lead to a reduction of strain away from the defect.
There are occasions when in addition to the ARCMAC data, it would be very helpful to be able to search for the presence of sub-surface cracks and other defects in power station components as reported on here for the laboratory specimens.
Also, this is to research ways of achieving the additional methods for monitoring of steam pipes to obtain their remaining life data.

A series of fatigue delamination tests on bonded aluminium were performed to provide input data.
A linear fit was applied to each data set in order to determine the parameters to fit a Paris Law equation.
The predictions are compared to a baseline (no reinforcement) and with experimental strapped test data.
An iterative FE model incorporating the model and data was developed to predict fatigue crack growth life of bonded structures.
Fatigue life prediction using the calculated stress intensity factor coefficient and AFGROW code showed good agreement with experimental data.

The high temperature compression tests were conducted up to 50 % reduction in a vacuum of 0.1 torr at temperatures of 900, 950, 1000, 1050 and 1100 °C with various strain rates between 0.1 s-1 and 10 s-1.
The true stress-true strain curves were obtained from the load-displacement data.
Based on the data, the power dissipation maps were constructed and illustrated in Fig. 2.

Li [5] analyzed the seismic behavior of this bridge using the spectral analysis method, the elastic dynamic analysis method and elasto-plastic dynamic analysis method, but some data about the bridge are different from ours which based on an inspection visit to the earthquake site.
The results and maximum value of earthquake responses are listed in Table 3, the data except the italic ones did consistent with the real damages.
Huang, in: Proceedings of conference on earthquake engineering and earthquake disaster reduction – the 1st anniversary of Wenchuan earthquake, Earthquake Press, Beijing (2009).