Some specific density functions of aggregated discounted claims with dependent risks

Kurtosis is a commonly used descriptive statistics. Kurtosis “Coefficient of excess” is critically reviewed in different aspects and is called as, measuring the fatness of the tails of the density functions, concentration towards the central value, scattering away from the target point or degree of peakedness of probability distribution. Kurtosis is referred to the shape of the distribution but many distributions having same kurtosis value may have different shapes while Kurtosis may exist when peak of a distribution is not in existence. Through extensive study of kurtosis on several distributions, Wu (2002) introduced a new measure called “W-Peakedness” that offers a fine capture of distribution shape to provide an intuitive measure of peakedness of the distribution which is inversely proportional to the standard deviation of the distribution. In this paper the work is extended for different others continuous probability distributions. Empirical results through simulation illustrate the proposed method to evaluate kurtosis by W-peakedness

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Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

Remote Sensing ◽

10.3390/rs13122307 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2307

Author(s):

J. Javier Gorgoso-Varela ◽

Rafael Alonso Ponce ◽

Francisco Rodríguez-Puerta

Keyword(s):

Probability Density ◽

Linear Models ◽

Pinus Halepensis ◽

Beta Function ◽

Probability Density Functions ◽

Lidar Data ◽

Density Functions ◽

Minimum Diameter ◽

Location Parameters ◽

Diameter Distributions

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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A synergy of the velocity gradients technique and the probability density functions for identifying gravitational collapse in self-absorbing media

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab087 ◽

2021 ◽

Vol 502 (2) ◽

pp. 1768-1784

Author(s):

Yue Hu ◽

A Lazarian

Keyword(s):

Magnetic Fields ◽

Probability Density ◽

Column Density ◽

Mass Density ◽

Probability Density Functions ◽

Emission Lines ◽

Density Functions ◽

Star Forming ◽

Velocity Gradients ◽

Self Gravity

ABSTRACT The velocity gradients technique (VGT) and the probability density functions (PDFs) of mass density are tools to study turbulence, magnetic fields, and self-gravity in molecular clouds. However, self-absorption can significantly make the observed intensity different from the column density structures. In this work, we study the effects of self-absorption on the VGT and the intensity PDFs utilizing three synthetic emission lines of CO isotopologues 12CO (1–0), 13CO (1–0), and C18O (1–0). We confirm that the performance of VGT is insensitive to the radiative transfer effect. We numerically show the possibility of constructing 3D magnetic fields tomography through VGT. We find that the intensity PDFs change their shape from the pure lognormal to a distribution that exhibits a power-law tail depending on the optical depth for supersonic turbulence. We conclude the change of CO isotopologues’ intensity PDFs can be independent of self-gravity, which makes the intensity PDFs less reliable in identifying gravitational collapsing regions. We compute the intensity PDFs for a star-forming region NGC 1333 and find the change of intensity PDFs in observation agrees with our numerical results. The synergy of VGT and the column density PDFs confirms that the self-gravitating gas occupies a large volume in NGC 1333.

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Modelling with star-shaped distributions

Dependence Modeling ◽

10.1515/demo-2020-0003 ◽

2020 ◽

Vol 8 (1) ◽

pp. 45-69

Author(s):

Eckhard Liebscher ◽

Wolf-Dieter Richter

Keyword(s):

Probabilistic Models ◽

Arbitrary Dimension ◽

Probability Density Functions ◽

Density Functions ◽

Wide Range ◽

Gaussian Density ◽

Spherical Distributions ◽

Data Points ◽

Non Gaussian ◽

General Method

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.

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The Influence of Sewage Sludge Content and Sintering Temperature on Selected Properties of Lightweight Expanded Clay Aggregate

Materials ◽

10.3390/ma14123363 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3363

Author(s):

Jolanta Latosińska ◽

Maria Żygadło ◽

Przemysław Czapik

Keyword(s):

Sewage Sludge ◽

Water Absorption ◽

Apparent Density ◽

Environmental Sustainability ◽

Sintering Temperature ◽

Total Porosity ◽

Raw Material ◽

Closed Porosity ◽

Specific Density ◽

Sustainability Principles

Wastewater treatment processes produce sewage sludge (SS), which, in line with environmental sustainability principles, can be a valuable source of matter in the production of lightweight expanded clay aggregate (LECA). The literature on the influence of SS content and sintering temperature on the properties of LECA is scarce. This paper aims to statistically evaluate the effects of SS content and sintering temperature on LECA physical properties. Total porosity, pore volume, and apparent density were determined with the use of a density analyzer. A helium pycnometer was utilized to determine the specific density. Closed porosity was calculated. The test results demonstrated a statistically significant influence of the SS content on the specific density and water absorption of LECA. The sintering temperature had a significant effect on the specific density, apparent density, total porosity, closed porosity, total volume of pores, and water absorption. It was proved that a broad range of the SS content is admissible in the raw material mass for the production of LECA.

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